Bioinformatics Tools

pET Vectors

noreply@blogger.com (Sanjiv Kumar) — Tue, 11 Feb 2020 14:52:00 +0000

pET21a Download PDF
https://drive.google.com/file/d/1xXf4t3cpyvmSsk95ZJXkOJ67lyPsrfGQ/view?usp=sharing

pET28a Download PDF
https://drive.google.com/file/d/164M0uOwB-M9IJDE9BJ8j5OcN6XCDT7yt/view?usp=sharing

Sequence Variation of Rare Outer Membrane Protein β-Barrel Domains in Clinical Strains Provides Insights into the Evolution of Treponema pallidum subsp. pallidum, the Syphilis Spirochete

noreply@blogger.com (Sanjiv Kumar) — Fri, 20 Jul 2018 08:59:00 +0000

http://mbio.asm.org/content/9/3/e01006-18.short

ABSTRACT

In recent years, considerable progress has been made in topologically and functionally characterizing integral outer membrane proteins (OMPs) of Treponema pallidum subspecies pallidum, the syphilis spirochete, and identifying its surface-exposed β-barrel domains. Extracellular loops in OMPs of Gram-negative bacteria are known to be highly variable. We examined the sequence diversity of β-barrel-encoding regions of tprC, tprD, and bamA in 31 specimens from Cali, Colombia; San Francisco, California; and the Czech Republic and compared them to allelic variants in the 41 reference genomes in the NCBI database. To establish a phylogenetic framework, we used T. pallidum 0548 (tp0548) genotyping and tp0558 sequences to assign strains to the Nichols or SS14 clades. We found that (i) β-barrels in clinical strains could be grouped according to allelic variants in T. pallidum subsp. pallidum reference genomes; (ii) for all three OMP loci, clinical strains within the Nichols or SS14 clades often harbored β-barrel variants that differed from the Nichols and SS14 reference strains; and (iii) OMP variable regions often reside in predicted extracellular loops containing B-cell epitopes. On the basis of structural models, nonconservative amino acid substitutions in predicted transmembrane β-strands of T. pallidum repeat C (TprC) and TprD2 could give rise to functional differences in their porin channels. OMP profiles of some clinical strains were mosaics of different reference strains and did not correlate with results from enhanced molecular typing. Our observations suggest that human host selection pressures drive T. pallidum subsp. pallidum OMP diversity and that genetic exchange contributes to the evolutionary biology of T. pallidum subsp. pallidum. They also set the stage for topology-based analysis of antibody responses to OMPs and help frame strategies for syphilis vaccine development.

IMPORTANCE

Despite recent progress characterizing outer membrane proteins (OMPs) of Treponema pallidum, little is known about how their surface-exposed, β-barrel-forming domains vary among strains circulating within high-risk populations. In this study, sequences for the β-barrel-encoding regions of three OMP loci, tprC, tprD, and bamA, in T. pallidum subsp. pallidum isolates from a large number of patient specimens from geographically disparate sites were examined. Structural models predict that sequence variation within β-barrel domains occurs predominantly within predicted extracellular loops. Amino acid substitutions in predicted transmembrane strands that could potentially affect porin channel function were also noted. Our findings suggest that selection pressures exerted within human populations drive T. pallidum subsp. pallidum OMP diversity and that recombination at OMP loci contributes to the evolutionary biology of syphilis spirochetes. These results also set the stage for topology-based analysis of antibody responses that promote clearance of T. pallidum subsp. pallidum and frame strategies for vaccine development based upon conserved OMP extracellular loops.

The major outer sheath protein forms distinct conformers and multimeric complexes in the outer membrane and periplasm of Treponema denticola

noreply@blogger.com (Sanjiv Kumar) — Mon, 23 Oct 2017 08:40:00 +0000

Recent Article: https://www.nature.com/articles/s41598-017-13550-6

The major outer sheath protein (MOSP) is a prominent constituent of the cell envelope of Treponema denticola (TDE) and one of its principal virulence determinants. Bioinformatics predicts that MOSP consists of N- and C-terminal domains, MOSPN and MOSPC. Biophysical analysis of constructs refolded in vitro demonstrated that MOSPC, previously shown to possess porin activity, forms amphiphilic trimers, while MOSPN forms an extended hydrophilic monomer. In TDE and E. coliexpressing MOSP with a PelB signal sequence (PelB-MOSP), MOSPC is OM-embedded and surface-exposed, while MOSPN resides in the periplasm.

Immunofluorescence assay, surface proteolysis, and novel cell fractionation schemes revealed that MOSP in TDE exists as outer membrane (OM) and periplasmic trimeric conformers; PelB-MOSP, in contrast, formed only OM-MOSP trimers. Although both conformers form hetero-oligomeric complexes in TDE, only OM-MOSP associates with dentilisin. Mass spectrometry (MS) indicated that OM-MOSP interacts with proteins in addition to dentilisin, most notably, oligopeptide-binding proteins (OBPs) and the β-barrel of BamA. MS also identified candidate partners for periplasmic MOSP, including TDE1658, a spirochete-specific SurA/PrsA ortholog.

Collectively, our data suggest that MOSP destined for the TDE OM follows the canonical BAM pathway, while formation of a stable periplasmic conformer involves an export-related, folding pathway not present in E. coli.

The Treponema pallidum Outer Membrane

noreply@blogger.com (Sanjiv Kumar) — Mon, 23 Oct 2017 08:37:00 +0000

Recent Article: https://link.springer.com/chapter/10.1007/82_2017_44

The outer membrane (OM) of Treponema pallidum, the uncultivatable agent of venereal syphilis, has long been the subject of misconceptions and controversy. Decades ago, researchers postulated that T. pallidum’s poor surface antigenicity is the basis for its ability to cause persistent infection, but they mistakenly attributed this enigmatic property to the presence of a protective outer coat of serum proteins and mucopolysaccharides. Subsequent studies revealed that the OM is the barrier to antibody binding, that it contains a paucity of integral membrane proteins, and that the preponderance of the spirochete’s immunogenic lipoproteins is periplasmic.

Since the advent of recombinant DNA technology, the fragility of the OM, its low protein content, and the lack of sequence relatedness between T. pallidum and Gram-negative outer membrane proteins (OMPs) have complicated efforts to characterize molecules residing at the host–pathogen interface. We have overcome these hurdles using the genomic sequence in concert with computational tools to identify proteins predicted to form β-barrels, the hallmark conformation of OMPs in double-membrane organisms and evolutionarily related eukaryotic organelles. We also have employed diverse methodologies to confirm that some candidate OMPs do, in fact, form amphiphilic β-barrels and are surface-exposed in T. pallidum.

These studies have led to a structural homology model for BamA and established the bipartite topology of the T. pallidum repeat (Tpr) family of proteins. Recent bioinformatics has identified several structural orthologs for well-characterized Gram-negative OMPs, suggesting that the T. pallidum OMP repertoire is more Gram-negative-like than previously supposed. Lipoprotein adhesins and proteases on the spirochete surface also may contribute to disease pathogenesis and protective immunity.

Consensus Phylogenetic trees of Fifteen Prokaryotic Aminoacyl-tRNA Synthetase Polypeptides based on Euclidean Geometry of All-Pairs Distances and Concatenation

noreply@blogger.com (Sanjiv Kumar) — Fri, 27 May 2016 19:07:00 +0000

An interesting one from my friend.

Read full here: http://biorxiv.org/content/early/2016/05/06/051623

For comments and quarries please write to rhishi.sr[at]gmail.com

Most molecular phylogenetic trees depict the relative closeness or the extent of similarity among a set of taxa based on comparison of sequences of homologous genes or proteins. Since the tree topology for individual monogenic traits varies among the same set of organisms and does not overlap taxonomic hierarchy, hence there is a need to generate multidimensional phylogenetic trees. Phylogenetic trees were constructed for 119 prokaryotes representing 2 phyla under Archaea and 11 phyla under Bacteria after comparing multiple sequence alignments for 15 different aminoacyl-tRNA synthetase polypeptides.

The topology of Neighbor Joining (NJ) trees for individual tRNA synthetase polypeptides varied substantially. We use Euclidean geometry to estimate all-pairs distances in order to construct phylogenetic trees. Further, we used a novel 'Taxonomic fidelity' algorithm to estimate clade by clade similarity between the phylogenetic tree and the taxonomic tree. We find that, as compared to trees for individual tRNA synthetase polypeptides and rDNA sequences, the topology of our Euclidean tree and that for aligned and concatenated sequences of 15 proteins are closer to the taxonomic trees and offer the best consensus. We have also aligned sequences after concatenation, and find that by changing the order of sequence joining prior to alignment, the tree topologies vary. In contrast, changing the types of polypeptides in the grouping for Euclidean trees does not affect the tree topologies.

We show that a consensus phylogenetic tree of 15 polypeptides from 14 aminoacyl-tRNA synthetases for 119 prokaryotes using Euclidean geometry exhibits better taxonomic fidelity than trees for individual tRNA synthetase polypeptides as well as 16S rDNA. We have also examined Euclidean N-dimensional trees for 15 tRNA synthetase polypeptides which give the same topology as that constructed after amalgamating 3-dimensional Euclidean trees for groups of 3 polypeptides. Euclidean N-dimensional trees offer a reliable future to multi-genic molecular phylogenetics.

Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins

noreply@blogger.com (Sanjiv Kumar) — Fri, 27 May 2016 18:44:00 +0000

Background

The efficacy of antibiotics against bacterial infections is decreasing due to the development of resistance in bacteria, and thus, there is a need to search for potential alternatives to antibiotics. In this scenario, peptidoglycan hydrolases can be used as alternate antibacterial agents due to their unique property of cleaving peptidoglycan cell wall present in both gram-positive and gram-negative bacteria. Along with a role in maintaining overall peptidoglycan turnover in a cell and in daughter cell separation, peptidoglycan hydrolases also play crucial role in bacterial pathophysiology requiring development of a computational tool for the identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data.

Results

In this study, the known peptidoglycan hydrolases were divided into multiple classes based on their site of action and were used for the development of a computational tool ‘HyPe’ for identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data. Various classification models were developed using amino acid and dipeptide composition features by training and optimization of Random Forest and Support Vector Machines. Random Forest multiclass model was selected for the development of HyPe tool as it showed up to 71.12 % sensitivity, 99.98 % specificity, 99.55 % accuracy and 0.80 MCC in four different classes of peptidoglycan hydrolases. The tool was validated on 24 independent genomic datasets and showed up to 100 % sensitivity and 0.94 MCC. The ability of HyPe to identify novel peptidoglycan hydrolases was also demonstrated on 24 metagenomic datasets.

Conclusions

The present tool helps in the identification and classification of novel peptidoglycan hydrolases from complete genomic or metagenomic ORFs. To our knowledge, this is the only tool available for the prediction of peptidoglycan hydrolases from genomic and metagenomic data.

Keywords

Peptidoglycan hydrolase N-acetylglucosaminidase N-acetylmuramidases Lytic transglycosylases Endopeptidase N-acetylmuramoyl-L-alanine Carboxypeptidase Cell wall hydrolases Support Vector Machine Random Forest

Reconstruction of bacterial and viral genomes from multiple metagenomes

noreply@blogger.com (Sanjiv Kumar) — Fri, 27 May 2016 18:40:00 +0000

Predicting Beta Barrel Outer Membrane Proteins (OMPs)

noreply@blogger.com (Sanjiv Kumar) — Sun, 30 Aug 2015 01:15:00 +0000

Here is compilation of some of the commonly available tools for prediction of Beta Barrel Outer Membrane Proteins (OMPs)

1. Pred-TMBB (2004): http://bioinformatics.biol.uoa.gr/PRED-TMBB/input.jsp

PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins. The beta-barrel outer membrane proteins constitute one of the two known structural classes of membrane proteins. Whereas there are several different web-based predictors for alpha-helical membrane proteins, currently there is no freely available prediction method for beta-barrel membrane proteins, at least with an acceptable level of accuracy. We present here a web server (PRED-TMBB, http://bioinformatics.biol.uoa.gr/PRED-TMBB) which is capable of predicting the transmembrane strands and the topology of beta-barrel outer membrane proteins of Gram-negative bacteria. The method is based on a Hidden Markov Model, trained according to the Conditional Maximum Likelihood criterion. The model was retrained and the training set now includes 16 non-homologous outer membrane proteins with structures known at atomic resolution. The user may submit one sequence at a time and has the option of choosing between three different decoding methods. The server reports the predicted topology of a given protein, a score indicating the probability of the protein being an outer membrane beta-barrel protein, posterior probabilities for the transmembrane strand prediction and a graphical representation of the assumed position of the transmembrane strands with respect to the lipid bilayer. http://nar.oxfordjournals.org/content/32/suppl_2/W400.long

2. BOCTOPUS (2012): http://boctopus.cbr.su.se/

BOCTOPUS: improved topology prediction of transmembrane β barrel proteins

Transmembrane β barrel proteins (TMBs) are found in the outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They play a major role in the translocation machinery, pore formation, membrane anchoring and ion exchange. TMBs are also promising targets for antimicrobial drugs and vaccines. Given the difficulty in membrane protein structure determination, computational methods to identify TMBs and predict the topology of TMBs are important. Results: Here, we present BOCTOPUS; an improved method for the topology prediction of TMBs by employing a combination of support vector machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for local and global residue preferences, respectively. Based on a 10-fold cross-validation test, BOCTOPUS performs better than all existing methods, reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable identification of TMBs by using it as an additional filter to methods specialized in this task. http://bioinformatics.oxfordjournals.org/content/28/4/516.long

3. TBBpred (2004): http://www.imtech.res.in/raghava/tbbpred/

Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods. This article describes a method developed for predicting transmembrane β-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using “leave one out cross-validation” (LOOCV). http://onlinelibrary.wiley.com/doi/10.1002/prot.20092/abstract;jsessionid=F041C3CA2F5E53B83924D0D73D2832C7.f03t02

4. BETAWARE (2013): http://www.biocomp.unibo.it/~savojard/betawarecl/

BETAWARE: a machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes. The annotation of membrane proteins in proteomes is an important problem of Computational Biology, especially after the development of high-throughput techniques that allow fast and efficient genome sequencing. Among membrane proteins, transmembrane β-barrels (TMBBs) are poorly represented in the database of protein structures (PDB) and difficult to identify with experimental approaches. They are, however, extremely important, playing key roles in several cell functions and bacterial pathogenicity. TMBBs are included in the lipid bilayer with a β-barrel structure and are presently found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. Recently, we developed two top-performing methods based on machine-learning approaches to tackle both the detection of TMBBs in sets of proteins and the prediction of their topology. Here, we present our BETAWARE program that includes both approaches and can run as a standalone program on a linux-based computer to easily address in-home massive protein annotation or filtering. http://bioinformatics.oxfordjournals.org/content/29/4/504.abstract

5. ConBBPRED (2005): http://bioinformatics.biol.uoa.gr/ConBBPRED/index.jsp

Prediction of the transmembrane strands and topology of β-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of β-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 β-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane β-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.

http://www.biomedcentral.com/1471-2105/6/7

6. TMBETA-RBF (2008): http://rbf.bioinfo.tw/~sachen/OMPpredict/TMBETADISC-RBF.php

TMBETA-NET: discrimination and prediction of membrane spanning β-strands in outer membrane proteins. We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning β-strand segments. The amino acid compositions of globular and outer membrane proteins have been systematically analyzed and a statistical method has been proposed for discriminating outer membrane proteins. The prediction of membrane spanning segments is mainly based on feed forward neural network and refined with β-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning β-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane β-strand has been provided with a coloring scheme. We observed that outer membrane proteins were discriminated with an accuracy of 89% and their membrane spanning β-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/

7. TMB-HUNT (2005): http://www.bioinformatics.leeds.ac.uk/betaBarrel/

TMB-Hunt: a web server to screen sequence sets for transmembrane β-barrel proteins. TMB-Hunt is a program that uses a modified k-nearest neighbour (k-NN) algorithm to classify protein sequences as transmembrane β-barrel (TMB) or non-TMB on the basis of whole sequence amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, a discrimination accuracy of 92.5% was achieved, as tested using a rigorous cross-validation procedure. The TMB-Hunt web server, available at www.bioinformatics.leeds.ac.uk/betaBarrel, allows screening of up to 10 000 sequences in a single query and provides results and key statistics in a simple colour coded format. http://nar.oxfordjournals.org/content/33/suppl_2/W188.long

8. TMBPro (2008): suite of specialized predictors for predicting secondary structure, beta-contacts, and tertiary structure of Transmembrane Beta-Barrel (TMB) proteins. http://tmbpro.ics.uci.edu/ TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins. Transmembrane β-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully.Results: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), β-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane β-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q2) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts β-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q2 of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 Å for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 Å, with a GDT_TS score greater than 60.0. http://bioinformatics.oxfordjournals.org/content/24/4/513.long

9. MCMBB Markov Chain Model Beta Barrels (2004): http://athina.biol.uoa.gr/bioinformatics/mcmbb/

The task of finding β-barrel outer membrane proteins of the gram-negative bacteria is of greatimportance in current Bioinformatics research. We developed a computational method, which discriminates β- barrel outer membrane proteins from globular ones and, also, from α-helical membrane proteins. The methodis based on a 1st order Markov Chain model, which captures the alternating pattern of hydrophilic-hydrophobicresidues occurring in the membrane-spanning beta-strands of beta-barrel outer membrane proteins. The modelachieves high accuracy in discriminating outer membrane proteins, and could be used alone, or in conjunctionwith other more sophisticated methods, already available http://www.academia.edu/316959/Finding_Beta-Barrel_Outer_Membrane_Proteins_With_a_Markov_Chain_Model

10. TMB-KNN (2008): http://cs.ndsu.nodak.edu/~chayan/Server/TMB_KNN.html

TMB-Hunt: a web server to screen sequence sets for transmembrane β-barrel proteins

TMB-Hunt is a program that uses a modified k-nearest neighbour (k-NN) algorithm to classify protein sequences as transmembrane β-barrel (TMB) or non-TMB on the basis of whole sequence amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, a discrimination accuracy of 92.5% was achieved, as tested using a rigorous cross-validation procedure. The TMB-Hunt web server, available at www.bioinformatics.leeds.ac.uk/betaBarrel, allows screening of up to 10 000 sequences in a single query and provides results and key statistics in a simple colour coded format. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160145/

11. transFold (2006): super-secondary structure prediction of transmembrane β-barrel proteins http://bioinformatics.bc.edu/clotelab/transFold/

transFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels. Transmembrane β-barrel (TMB) proteins are embedded in the outer membrane of Gram-negative bacteria, mitochondria and chloroplasts. The cellular location and functional diversity of β-barrel outer membrane proteins makes them an important protein class. At the present time, very few non-homologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane (TM) proteins. The transFold web server uses pairwise inter-strand residue statistical potentials derived from globular (non-outer-membrane) proteins to predict the supersecondary structure of TMB. Unlike all previous approaches, transFold does not use machine learning methods such as hidden Markov models or neural networks; instead, transFold employs multi-tape S-attribute grammars to describe all potential conformations, and then applies dynamic programming to determine the global minimum energy supersecondary structure. The transFold web server not only predicts secondary structure and TMB topology, but is the only method which additionally predicts the side-chain orientation of transmembrane β-strand residues, inter-strand residue contacts and TM β-strand inclination with respect to the membrane. The program transFold currently outperforms all other methods for accuracy of β-barrel structure prediction. Available at http://bioinformatics.bc.edu/clotelab/transFold. http://nar.oxfordjournals.org/content/34/suppl_2/W189.full

12. BOMP (2004): http://services.cbu.uib.no/tools/bomp

BOMP: a program to predict integral β-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria. This work describes the development of a program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral β-barrel outer membrane protein. The program, called the β-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral β-barrel proteins. The first component is a C-terminal pattern typical of many integral β-barrel proteins. The second component calculates an integral β-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane β-strands. The precision of the predictions was found to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated subcellular localization in Escherichia coli K 12 (788 sequences) and Salmonella typhimurium (366 sequences). When tested on the predicted proteome of E.coli, BOMP found 103 of a total of 4346 polypeptide sequences to be possible integral β-barrel proteins. Of these, 36 were found by BLAST to lack similarity (E-value score < 1e−10) to proteins with annotated subcellular localization in SwissProt. BOMP predicted the content of integral β-barrels per predicted proteome of 10 different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp http://nar.oxfordjournals.org/content/32/suppl_2/W394.full

13. TMBETA-net (2004): http://psfs.cbrc.jp/tmbeta-net/

TMBETA-NET: discrimination and prediction of membrane spanning beta-strands in outer membrane proteins. We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning beta-strand segments. The amino acid compositions of globular and outer membrane proteins have been systematically analyzed and a statistical method has been proposed for discriminating outer membrane proteins. The prediction of membrane spanning segments is mainly based on feed forward neural network and refined with beta-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning beta-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane beta-strand has been provided with a coloring scheme. We observed that outer membrane proteins were discriminated with an accuracy of 89% and their membrane spanning beta-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/. http://nar.oxfordjournals.org/content/33/suppl_2/W164.long

14. TMBB-DB (2012): http://beta-barrel.tulane.edu/index.html

TMBB-DB: a transmembrane β-barrel proteome database. We previously reported the development of a highly accurate statistical algorithm for identifying β-barrel outer membrane proteins or transmembrane β-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26, 1965–1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes. For each protein in the database, there is information on (i) β-barrel membrane protein probability for identification of β-barrels, (ii) β-strand and β-hairpin propensity for structure and topology prediction, (iii) signal sequence score because most TMBBs are secreted through the inner membrane translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix predictions, for reducing false positive predictions. This information is sufficient for the accurate identification of most β-barrel membrane proteins in these genomes. In the database there are nearly 50 000 predicted TMBBs (out of 1.9 million total putative proteins). Of those, more than 15 000 are ‘hypothetical’ or ‘putative’ proteins, not previously identified as TMBBs. This wealth of genomic information is not available anywhere else. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463127/

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

noreply@blogger.com (Sanjiv Kumar) — Thu, 16 Jul 2015 20:13:00 +0000

Another recent publication from our lab.

Citation:

Sharma, A. K., Gupta, A., Kumar, S., Dhakan, D. B., & Sharma, V. K. (2015). Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences. Genomics.

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow.

Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

The Woods web server is freely accessible at http://metagenomics.iiserb.ac.in/woods/index.php and http://metabiosys.iiserb.ac.in/woods/index.php. The standalone version of Woods can be downloaded from the above web servers and usage instructions are provided in Text S1 and also in the Tutorial section of the web server.

For complete article click here: Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

MP3: A Software Tool for the Prediction of Pathogenic Proteins in Genomic and Metagenomic Data

noreply@blogger.com (Sanjiv Kumar) — Thu, 05 Feb 2015 09:34:00 +0000

MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets.

Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51–100 amino acids and Blind B: 30–50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets.

To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100–150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences.

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

noreply@blogger.com (Sanjiv Kumar) — Thu, 05 Feb 2015 09:14:00 +0000

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0116106

Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier.

The sequencing of 16S rRNA gene is commonly performed to estimate the microbial diversity in a metagenomic study. The rapid developments in genome sequencing technologies have shifted the focus on sequencing the selected hypervariable regions (HVRs) of 16S rRNA gene instead of sequencing the complete gene. The recent metagenomic projects involve the sequencing of only a single HVR or a combination of two or more HVRs. At present there is no specialized method available for the correct identification and classification of species using short variable 16S rRNA sequences. Therefore, we have developed 16S Classifier using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed the precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the first test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level.

16S classifier displayed up to 42.9%, 40.7%, 41.0%, 57.9% and 73.8% higher accuracy at phylum, class, order, family and genus levels, respectively, as compared to the commonly used RDP classifier program. In addition, it is 7.5 times faster than RDP Classifier and 800 times faster than BLAST. 16S classifier can be easily used with the QIIME pipeline which is commonly used for the 16S rRNA analysis.

To the best of our knowledge, 16S Classifier is the only available tool which can carry out the efficient, sensitive and accurate taxonomic assignment of any of the 16S rRNA hypervariable regions which are commonly used in metagenomic projects. In the case of complete 16S rRNA also, it displayed exceptional (precision of 0.97) performance on the test dataset. Thus, the wide usage of this tool is anticipated in different metagenomic projects. 16S

Instructions for running the stand-alone version of 16S Classifier on the Linux PC.

1. User can download zip file of a particular hypervariable region or complete 16S, which is freely available at http://metagenomics.iiserb.ac.in/16Sclassifier/download.html

2. Extract the zipped file which contains a model file (*.Rdata), a script file (*.sh) and an exe file (16sclassifier.exe).

Other dependencies:

1. User has to install R from the following link http://cran.r-project.org/

2. intall Randomforest by typing the following commands in terminal R and install.packages ('randomForest')

# Command line usage #

./16sclassifier.exe 'queryfile' 'modelname'

The query file should be in Fasta format and the model name could be v2, v3, v4, v5, v6, v7, v8, v23, v34, v35, v45, v56, v67, v78 and Complete16S.

Metagenomic Assemblers:

noreply@blogger.com (Sanjiv Kumar) — Sat, 05 Jul 2014 10:04:00 +0000

Here is the list of most commonly used assembler for metagenomics reads used. The list is extensive but by no means is complete. I will try to update as soon as I come across a new one. Help me keeping the list updated if you come across any new and interesting assembler I have missed.

MetaVelvet : http://metavelvet.dna.bio.keio.ac.jp/ : An extension of Velvet assembler to de novo metagenome assembly from short sequence reads: An important step in ‘metagenomics’ analysis is the assembly of multiple genomes from mixed sequence reads of multiple species in a microbial community. Most conventional pipelines use a single-genome assembler with carefully optimized parameters. A limitation of a single-genome assembler for de novo metagenome assembly is that sequences of highly abundant species are likely misidentified as repeats in a single genome, resulting in a number of small fragmented scaffolds. We extended a single-genome assembler for short reads, known as ‘Velvet’, to metagenome assembly, which we called ‘MetaVelvet’, for mixed short reads of multiple species. Our fundamental concept was to first decompose a de Bruijn graph constructed from mixed short reads into individual sub-graphs, and second, to build scaffolds based on each decomposed de Bruijn sub-graph as an isolate species genome. We made use of two features, the coverage (abundance) difference and graph connectivity, for the decomposition of the de Bruijn graph. For simulated datasets, MetaVelvet succeeded in generating significantly higher N50 scores than any single-genome assemblers. MetaVelvet also reconstructed relatively low-coverage genome sequences as scaffolds. On real datasets of human gut microbial read data, MetaVelvet produced longer scaffolds and increased the number of predicted genes. http://nar.oxfordjournals.org/content/40/20/e155.short

MetAMOS: https://github.com/marbl/metAMOS a metagenomic assembly and analysis pipeline for AMOS: We describe MetAMOS, an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations. MetAMOS can aid in reducing assembly errors, commonly encountered when assembling metagenomic samples, and improves taxonomic assignment accuracy while also reducing computational cost. MetAMOS can be downloaded from: https://github.com/treangen/MetAMOS. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053804/

IDBA-UD: http://www.cs.hku.hk/~alse/idba_ud a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Motivation: Next-generation sequencing allows us to sequence reads from a microbial environment using single-cell sequencing or metagenomic sequencing technologies. However, both technologies suffer from the problem that sequencing depth of different regions of a genome or genomes from different species are highly uneven. Most existing genome assemblers usually have an assumption that sequencing depths are even. These assemblers fail to construct correct long contigs. Results: We introduce the IDBA-UD algorithm that is based on the de Bruijn graph approach for assembling reads from single-cell sequencing or metagenomic sequencing technologies with uneven sequencing depths. Several non-trivial techniques have been employed to tackle the problems. Instead of using a simple threshold, we use multiple depthrelative thresholds to remove erroneous k-mers in both low-depth and high-depth regions. The technique of local assembly with paired-end information is used to solve the branch problem of low-depth short repeat regions. To speed up the process, an error correction step is conducted to correct reads of high-depth regions that can be aligned to highconfident contigs. Comparison of the performances of IDBA-UD and existing assemblers (Velvet, Velvet-SC, SOAPdenovo and Meta-IDBA) for different datasets, shows that IDBA-UD can reconstruct longer contigs with higher accuracy. Availability: The IDBA-UD toolkit is available at our website http://www.cs.hku.hk/~alse/idba_ud. http://bioinformatics.oxfordjournals.org/content/28/11/1420.short

Meta-IDBA: http://www.cs.hku.hk/~alse/metaidba a de Novo assembler for metagenomic data. Motivation: Next-generation sequencing techniques allow us to generate reads from a microbial environment in order to analyze the microbial community. However, assembling of a set of mixed reads from different species to form contigs is a bottleneck of metagenomic research. Although there are many assemblers for assembling reads from a single genome, there are no assemblers for assembling reads in metagenomic data without reference genome sequences. Moreover, the performances of these assemblers on metagenomic data are far from satisfactory, because of the existence of common regions in the genomes of subspecies and species, which make the assembly problem much more complicated. Results: We introduce the Meta-IDBA algorithm for assembling reads in metagenomic data, which contain multiple genomes from different species. There are two core steps in Meta-IDBA. It first tries to partition the de Bruijn graph into isolated components of different species based on an important observation. Then, for each component, it captures the slight variants of the genomes of subspecies from the same species by multiple alignments and represents the genome of one species, using a consensus sequence. Comparison of the performances of Meta-IDBA and existing assemblers, such as Velvet and Abyss for different metagenomic datasets shows that Meta-IDBA can reconstruct longer contigs with similar accuracy. Availability: Meta-IDBA toolkit is available at our website http://www.cs.hku.hk/~alse/metaidba. http://bioinformatics.oxfordjournals.org/content/27/13/i94.short

Ray Meta: http://denovoassembler.sf.net: Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a massively distributed metagenome assembler that is coupled with Ray Communities, which profiles microbiomes based on uniquely-colored k-mers. It can accurately assemble and profile a three billion read metagenomic experiment representing 1,000 bacterial genomes of uneven proportions in 15 hours with 1,024 processor cores, using only 1.5 GB per core. The software will facilitate the processing of large and complex datasets, and will help in generating biological insights for specific environments. Ray Meta is open source and available at http://denovoassembler.sf.net. : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056372/

MAP: http://bioinfo.ctb.pku.edu.cn/MAP/ Motivation: A high-quality assembly of reads generated from shotgun sequencing is a substantial step in metagenome projects. Although traditional assemblers have been employed in initial analysis of metagenomes, they cannot surmount the challenges created by the features of metagenomic data. Result: We present a de novo assembly approach and its implementation named MAP (metagenomic assembly program). Based on an improved overlap/layout/consensus (OLC) strategy incorporated with several special algorithms, MAP uses the mate pair information, resulting in being more applicable to shotgun DNA reads (recommended as >200 bp) currently widely used in metagenome projects. Results of extensive tests on simulated data show that MAP can be superior to both Celera and Phrap for typical longer reads by Sanger sequencing, as well as has an evident advantage over Celera, Newbler and the newest Genovo, for typical shorter reads by 454 sequencing. Availability and implementation: The source code of MAP is distributed as open source under the GNU GPL license, the MAP program and all simulated datasets can be freely available at http://bioinfo.ctb.pku.edu.cn/MAP/. http://bioinformatics.oxfordjournals.org/content/28/11/1455.short

Genovo: http://cs.stanford.edu/group/genovo/ : Next-generation sequencing technologies produce a large number of noisy reads from the DNA in a sample. Metagenomics and population sequencing aim to recover the genomic sequences of the species in the sample, which could be of high diversity. Methods geared towards single sequence reconstruction are not sensitive enough when applied in this setting. We introduce a generative probabilistic model of read generation from environmental samples and present Genovo, a novel de novo sequence assembler that discovers likely sequence reconstructions under the model. A nonparametric prior accounts for the unknown number of genomes in the sample. Inference is performed by applying a series of hill-climbing steps iteratively until convergence. We compare the performance of Genovo to three other short read assembly programs in a series of synthetic experiments and across nine metagenomic datasets created using the 454 platform, the largest of which has 311k reads. Genovo's reconstructions cover more bases and recover more genes than the other methods, even for low-abundance sequences, and yield a higher assembly score. http://online.liebertpub.com/doi/abs/10.1089/cmb.2010.0244

Extended Genovo: http://xgenovo.dna.bio.keio.ac.jp Metagenomes present assembly challenges, when assembling multiple genomes from mixed reads of multiple species. An assembler for single genomes can’t adapt well when applied in this case. A metagenomic assembler, Genovo, is a de novo assembler for metagenomes under a generative probabilistic model. Genovo assembles all reads without discarding any reads in a preprocessing step, and is therefore able to extract more information from metagenomic data and, in principle, generate better assembly results. Paired end sequencing is currently widely-used yet Genovo was designed for 454 single end reads. In this research, we attempted to extend Genovo by incorporating paired-end information, named Xgenovo, so that it generates higher quality assemblies with paired end reads. First, we extended Genovo by adding a bonus parameter in the Chinese Restaurant Process used to get prior accounts for the unknown number of genomes in the sample. This bonus parameter intends for a pair of reads to be in the same contig and as an effort to solve chimera contig case. Second, we modified the sampling process of the location of a read in a contig. We used relative distance for the number of trials in the symmetric geometric distribution instead of using distance between the offset and the center of contig used in Genovo. Using this relative distance, a read sampled in the appropriate location has higher probability. Therefore a read will be mapped in the correct location. Results of extensive experiments on simulated metagenomic datasets from simple to complex with species coverage setting following uniform and lognormal distribution showed that Xgenovo can be superior to the original Genovo and the recently proposed metagenome assembler for 454 reads, MAP. Xgenovo successfully generated longer N50 than Genovo and MAP while maintaining the assembly quality even for very complex metagenomic datasets consisting of 115 species. Xgenovo also demonstrated the potential to decrease the computational cost. This means that our strategy worked well. The software and all simulated datasets are publicly available online at http://xgenovo.dna.bio.keio.ac.jp. https://peerj.com/articles/196/

SmashCommunity: a metagenomic annotation and analysis tool. SmashCommunity is a stand-alone metagenomic annotation and analysis pipeline suitable for data from Sanger and 454 sequencing technologies. It supports state-of-the-art software for essential metagenomic tasks such as assembly and gene prediction. It provides tools to estimate the quantitative phylogenetic and functional compositions of metagenomes, to compare compositions of multiple metagenomes and to produce intuitive visual representations of such analyses. Availability: SmashCommunity source code and documentation are available at http://www.bork.embl.de/software/smash: http://bioinformatics.oxfordjournals.org/content/26/23/2977.short

Bambus 2: http://amos.sf.net. Motivation: Sequencing projects increasingly target samples from non-clonal sources. In particular, metagenomics has enabled scientists to begin to characterize the structure of microbial communities. The software tools developed for assembling and analyzing sequencing data for clonal organisms are, however, unable to adequately process data derived from non-clonal sources. Results: We present a new scaffolder, Bambus 2, to address some of the challenges encountered when analyzing metagenomes. Our approach relies on a combination of a novel method for detecting genomic repeats and algorithms that analyze assembly graphs to identify biologically meaningful genomic variants. We compare our software to current assemblers using simulated and real data. We demonstrate that the repeat detection algorithms have higher sensitivity than current approaches without sacrificing specificity. In metagenomic datasets, the scaffolder avoids false joins between distantly related organisms while obtaining long-range contiguity. Bambus 2 represents a first step toward automated metagenomic assembly. Availability: Bambus 2 is open source and available from http://amos.sf.net. http://bioinformatics.oxfordjournals.org/content/27/21/2964.short

MetaCAA: https://metagenomics.atc.tcs.com/MetaCAA A clustering-aided methodology for efficient assembly of metagenomic datasets. A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA — a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA. http://www.sciencedirect.com/science/article/pii/S0888754314000135

Genomic Assemblers:

noreply@blogger.com (Sanjiv Kumar) — Sat, 05 Jul 2014 08:48:00 +0000

Here is the list of most commonly used assembler for genomic reads used. The list is extensive but by no means is complete. I will try to update as soon as I come across a new one. Help me keeping the list updated if you come across any new and interesting assembler I have missed.

ABySS: http://www.bcgsc.ca/downloads/abyss/ : Widespread adoption of massively parallel deoxyribonucleic acid (DNA) sequencing instruments has prompted the recent development of de novo short read assembly algorithms. A common shortcoming of the available tools is their inability to efficiently assemble vast amounts of data generated from large-scale sequencing projects, such as the sequencing of individual human genomes to catalog natural genetic variation. To address this limitation, we developed ABySS (Assembly By Short Sequences), a parallelized sequence assembler. As a demonstration of the capability of our software, we assembled 3.5 billion paired-end reads from the genome of an African male publicly released by Illumina, Inc. Approximately 2.76 million contigs ≥100 base pairs (bp) in length were created with an N50 size of 1499 bp, representing 68% of the reference human genome. Analysis of these contigs identified polymorphic and novel sequences not present in the human reference assembly, which were validated by alignment to alternate human assemblies and to other primate genomes. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694472/

SOAPdenovo: http://soap.genomics.org.cn/ : Next-generation massively parallel DNA sequencing technologies provide ultrahigh throughput at a substantially lower unit data cost; however, the data are very short read length sequences, making de novo assembly extremely challenging. Here, we describe a novel method for de novo assembly of large genomes from short read sequences. We successfully assembled both the Asian and African human genome sequences, achieving an N50 contig size of 7.4 and 5.9 kilobases (kb) and scaffold of 446.3 and 61.9 kb, respectively. The development of this de novo short read assembly method creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost-effective way. http://genome.cshlp.org/content/early/2009/12/16/gr.097261.109

Velvet: http://www.ebi.ac.uk/~zerbino/velvet : We have developed a new set of algorithms, collectively called “Velvet,” to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25–50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of ∼8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies. http://genome.cshlp.org/content/18/5/821.short

ALLPATHS-LG: ftp://ftp.broadinstitute.org/pub/crd/ALLPATHS/Release-LG/: Massively parallel DNA sequencing technologies are revolutionizing genomics by making it possible to generate billions of relatively short (~100-base) sequence reads at very low cost. Whereas such data can be readily used for a wide range of biomedical applications, it has proven difficult to use them to generate high-quality de novo genome assemblies of large, repeat-rich vertebrate genomes. To date, the genome assemblies generated from such data have fallen far short of those obtained with the older (but much more expensive) capillary-based sequencing approach. Here, we report the development of an algorithm for genome assembly, ALLPATHS-LG, and its application to massively parallel DNA sequence data from the human and mouse genomes, generated on the Illumina platform. The resulting draft genome assemblies have good accuracy, short-range contiguity, long-range connectivity, and coverage of the genome. In particular, the base accuracy is high (≥99.95%) and the scaffold sizes (N50 size = 11.5 Mb for human and 7.2 Mb for mouse) approach those obtained with capillary-based sequencing. The combination of improved sequencing technology and improved computational methods should now make it possible to increase dramatically the de novo sequencing of large genomes.

The ALLPATHS-LG program is available at http://www.broadinstitute.org/science/programs/genome-biology/crd. http://www.pnas.org/content/108/4/1513.short

Bambus2: http://amos.sf.net: Motivation: Sequencing projects increasingly target samples from non-clonal sources. In particular, metagenomics has enabled scientists to begin to characterize the structure of microbial communities. The software tools developed for assembling and analyzing sequencing data for clonal organisms are, however, unable to adequately process data derived from non-clonal sources. Results: We present a new scaffolder, Bambus 2, to address some of the challenges encountered when analyzing metagenomes. Our approach relies on a combination of a novel method for detecting genomic repeats and algorithms that analyze assembly graphs to identify biologically meaningful genomic variants. We compare our software to current assemblers using simulated and real data. We demonstrate that the repeat detection algorithms have higher sensitivity than current approaches without sacrificing specificity. In metagenomic datasets, the scaffolder avoids false joins between distantly related organisms while obtaining long-range contiguity. Bambus 2 represents a first step toward automated metagenomic assembly. Availability: Bambus 2 is open source and available from http://amos.sf.net. http://bioinformatics.oxfordjournals.org/content/27/21/2964.short

Newbler: http://454.com/contact-us/software-request.asp : In the last year, high-throughput sequencing technologies have progressed from proof-of-concept to production quality. While these methods produce high-quality reads, they have yet to produce reads comparable in length to Sanger-based sequencing. Current fragment assembly algorithms have been implemented and optimized for mate-paired Sanger-based reads, and thus do not perform well on short reads produced by short read technologies. We present a new Eulerian assembler that generates nearly optimal short read assemblies of bacterial genomes and describe an approach to assemble reads in the case of the popular hybrid protocol when short and long Sanger-based reads are combined. http://genome.cshlp.org/content/18/2/324.full

MIRA: http://www.chevreux.org/mira_downloads.html We present an EST sequence assembler that specializes in reconstruction of pristine mRNA transcripts, while at the same time detecting and classifying single nucleotide polymorphisms (SNPs) occuring in different variations thereof. The assembler uses iterative multipass strategies centered on high-confidence regions within sequences and has a fallback strategy for using low-confidence regions when needed. It features special functions to assemble high numbers of highly similar sequences without prior masking, an automatic editor that edits and analyzes alignments by inspecting the underlying traces, and detection and classification of sequence properties like SNPs with a high specificity and a sensitivity down to one mutation per sequence. In addition, it includes possibilities to use incorrectly preprocessed sequences, routines to make use of additional sequencing information such as base-error probabilities, template insert sizes, strain information, etc., and functions to detect and resolve possible misassemblies. The assembler is routinely used for such various tasks as mutation detection in different cell types, similarity analysis of transcripts between organisms, and pristine assembly of sequences from various sources for oligo design in clinical microarray experiments. http://genome.cshlp.org/content/14/6/1147

Euler-USR: http://euler-assembler.ucsd.edu/portal/ : Increasing read length is currently viewed as the crucial condition for fragment assembly with next-generation sequencing technologies. However, introducing mate-paired reads (separated by a gap of length, GapLength) opens a possibility to transform short mate-pairs into long mate-reads of length ≈ GapLength, and thus raises the question as to whether the read length (as opposed to GapLength) even matters. We describe a new tool, EULER-USR, for assembling mate-paired short reads and use it to analyze the question of whether the read length matters. We further complement the ongoing experimental efforts to maximize read length by a new computational approach for increasing the effective read length. While the common practice is to trim the error-prone tails of the reads, we present an approach that substitutes trimming with error correction using repeat graphs. An important and counterintuitive implication of this result is that one may extend sequencing reactions that degrade with length “past their prime” to where the error rate grows above what is normally acceptable for fragment assembly. http://genome.cshlp.org/content/19/2/336.full

Celera Assembler: http://sourceforge.net/projects/wgs-assembler/files/wgs-assembler/wgs-7.0/ :

Minia: http://minia.genouest.org/files/minia-1.6088.tar.gz : Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet). http://minia.genouest.org/files/minia.pdf

Ray: http://sourceforge.net/projects/denovoassembler/files/ : An accurate genome sequence of a desired species is now a pre-requisite for genome research. An important step in obtaining a high-quality genome sequence is to correctly assemble short reads into longer sequences accurately representing contiguous genomic regions. Current sequencing technologies continue to offer increases in throughput, and corresponding reductions in cost and time. Unfortunately, the benefit of obtaining a large number of reads is complicated by sequencing errors, with different biases being observed with each platform. Although software are available to assemble reads for each individual system, no procedure has been proposed for high-quality simultaneous assembly based on reads from a mix of different technologies. In this paper, we describe a parallel short-read assembler, called Ray, which has been developed to assemble reads obtained from a combination of sequencing platforms. We compared its performance to other assemblers on simulated and real datasets. We used a combination of Roche/454 and Illumina reads to assemble three different genomes. We showed that mixing sequencing technologies systematically reduces the number of contigs and the number of errors. Because of its open nature, this new tool will hopefully serve as a basis to develop an assembler that can be of universal utilization (availability: http://deNovoAssembler.sf.Net/). For online Supplementary Material, seewww.liebertonline.com. http://online.liebertpub.com/doi/abs/10.1089/cmb.2009.0238

Edena: http://www.genomic.ch/edena : Novel high-throughput DNA sequencing technologies allow researchers to characterize a bacterial genome during a single experiment and at a moderate cost. However, the increase in sequencing throughput that is allowed by using such platforms is obtained at the expense of individual sequence read length, which must be assembled into longer contigs to be exploitable. This study focuses on the Illumina sequencing platform that produces millions of very short sequences that are 35 bases in length. We propose a de novo assembler software that is dedicated to process such data. Based on a classical overlap graph representation and on the detection of potentially spurious reads, our software generates a set of accurate contigs of several kilobases that cover most of the bacterial genome. The assembly results were validated by comparing data sets that were obtained experimentally for Staphylococcus aureus strain MW2 and Helicobacter acinonychis strain Sheeba with that of their published genomes acquired by conventional sequencing of 1.5- to 3.0-kb fragments. We also provide indications that the broad coverage achieved by high-throughput sequencing might allow for the detection of clonal polymorphisms in the set of DNA molecules being sequenced. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2336802/

MSR-CA: http://www.genome.umd.edu/SR_CA_MANUAL.htm : The MSR-CA assembler combines the benefits of deBruijn graph and Overlap-Layout-Consensus assembly approaches. The strength of the deBruijn graph approach is in its ability to quickly create a graph representation of the genome assembly from the deep coverage short read data. However in most cases the graph is extremely complex and it is hard to find a way to recover the original genome sequence from simply traversing it. On the other hand, overlap-layout-consensus is better suited for longer reads with high coverage, and since it usually relies on overlaps of 40 bases or longer, it is better for resolving short repetitive structures.

SGA: https://github.com/jts/sga De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharacterized genomes and to identify the genome sequence of individuals in a reference-unbiased way. We present memory efficient data structures and algorithms for assembly using the FM-index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error correct, assemble and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs less than 200bp in length). Because of the low memory requirements and parallelization without requiring inter-process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster. http://genome.cshlp.org/content/early/2011/12/07/gr.126953.111

SSAKE: http://www.bcgsc.ca/bioinfo/software/ssake : Novel DNA sequencing technologies with the potential for up to three orders magnitude more sequence throughput than conventional Sanger sequencing are emerging. The instrument now available from Solexa Ltd, produces millions of short DNA sequences of 25 nt each. Due to ubiquitous repeats in large genomes and the inability of short sequences to uniquely and unambiguously characterize them, the short read length limits applicability for de novo sequencing. However, given the sequencing depth and the throughput of this instrument, stringent assembly of highly identical sequences can be achieved. We describe SSAKE, a tool for aggressively assembling millions of short nucleotide sequences by progressively searching through a prefix tree for the longest possible overlap between any two sequences. SSAKE is designed to help leverage the information from short sequence reads by stringently assembling them into contiguous sequences that can be used to characterize novel sequencing targets. Availability: http://www.bcgsc.ca/bioinfo/software/ssake . http://bioinformatics.oxfordjournals.org/content/23/4/500.full

VCAKE: http://sourceforge.net/projects/vcake/ : Inexpensive de novo genome sequencing, particularly in organisms with small genomes, is now possible using several new sequencing technologies. Some of these technologies such as that from Illumina's Solexa Sequencing, produce high genomic coverage by generating a very large number of small reads (∼30 bp). While prior work shows that partial assembly can be performed by k-mer extension in error-free reads, this algorithm is unsuccessful with the sequencing error rates found in practice. We present VCAKE (Verified Consensus Assembly by K-mer Extension), a modification of simple k-mer extension that overcomes error by using high depth coverage. Though it is a simple modification of a previous approach, we show significant improvements in assembly results on simulated and experimental datasets that include error. Availability: http://152.2.15.114/~labweb/VCAKE. http://bioinformatics.oxfordjournals.org/content/23/21/2942.long

QSRA: http://qsra.cgrb.oregonstate.edu : New rapid high-throughput sequencing technologies have sparked the creation of a new class of assembler. Since all high-throughput sequencing platforms incorporate errors in their output, short-read assemblers must be designed to account for this error while utilizing all available data. Results: We have designed and implemented an assembler, Quality-value guided Short Read Assembler, created to take advantage of quality-value scores as a further method of dealing with error. Compared to previous published algorithms, our assembler shows significant improvements not only in speed but also in output quality. Conclusion: QSRA generally produced the highest genomic coverage, while being faster than VCAKE. QSRA is extremely competitive in its longest contig and N50/N80 contig lengths, producing results of similar quality to those of EDENA and VELVET. QSRA provides a step closer to the goal of de novo assembly of complex genomes, improving upon the original VCAKE algorithm by not only drastically reducing runtimes but also increasing the viability of the assembly algorithm through further error handling capabilities. http://www.biomedcentral.com/1471-2105/10/69

SHARCGS: http://sharcgs.molgen.mpg.de : The latest revolution in the DNA sequencing field has been brought about by the development of automated sequencers that are capable of generating giga base pair data sets quickly and at low cost. Applications of such technologies seem to be limited to resequencing and transcript discovery, due to the shortness of the generated reads. In order to extend the fields of application to de novo sequencing, we developed the SHARCGS algorithm to assemble short-read (25–40-mer) data with high accuracy and speed. The efficiency of SHARCGS was tested on BAC inserts from three eukaryotic species, on two yeast chromosomes, and on two bacterial genomes (Haemophilus influenzae, Escherichia coli). We show that 30-mer-based BAC assemblies have N50 sizes >20 kbp for Drosophila and Arabidopsis and >4 kbp for human in simulations taking missing reads and wrong base calls into account. We assembled 949,974 contigs with length >50 bp, and only one single contig could not be aligned error-free against the reference sequences. We generated 36-mer reads for the genome of Helicobacter acinonychis on the Illumina 1G sequencing instrument and assembled 937 contigs covering 98% of the genome with an N50 size of 3.7 kbp. With the exception of five contigs that differ in 1–4 positions relative to the reference sequence, all contigs matched the genome error-free. Thus, SHARCGS is a suitable tool for fully exploiting novel sequencing technologies by assembling sequence contigs de novo with high confidence and by outperforming existing assembly algorithms in terms of speed and accuracy. http://genome.cshlp.org/content/early/2007/10/01/gr.6435207

CABOG: http://wgs-assembler.sourceforge.net/wiki/index.php?title=Main_Page : The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874646/

Shorty: http://www.cs.sunysb.edu/~skiena/shorty : New short-read sequencing technologies produce enormous volumes of 25–30 base paired-end reads. The resulting reads have vastly different characteristics than produced by Sanger sequencing, and require different approaches than the previous generation of sequence assemblers. In this paper, we present a short-read de novo assembler particularly targeted at the new ABI SOLiD sequencing technology. Results This paper presents what we believe to be the first de novo sequence assembly results on real data from the emerging SOLiD platform, introduced by Applied Biosystems. Our assembler SHORTY augments short-paired reads using a trivially small number (5 – 10) of seeds of length 300 – 500 bp. These seeds enable us to produce significant assemblies using short-read coverage no more than 100×, which can be obtained in a single run of these high-capacity sequencers. SHORTY exploits two ideas which we believe to be of interest to the short-read assembly community: (1) using single seed reads to crystallize assemblies, and (2) estimating intercontig distances accurately from multiple spanning paired-end reads. Conclusion We demonstrate effective assemblies (N50 contig sizes ~40 kb) of three different bacterial species using simulated SOLiD data. Sequencing artifacts limit our performance on real data, however our results on this data are substantially better than those achieved by competing assemblers. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2648751/

Taipan: http://taipan.sourceforge.net : Summary: The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this article, we present Taipan, an assembly algorithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make better decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources. Availability and Implementation: Source code in C running on Linux is freely available at http://taipan.sourceforge.net http://bioinformatics.oxfordjournals.org/content/25/17/2279.long

PCAP long-read assembler: http://seq.cs.iastate.edu/pcap.html : This unit describes how to use the Parallel Contig Assembly Program (PCAP) to assemble the data produced by a whole-genome shotgun sequencing project. We present a basic protocol for using PCAP on a multiprocessor computer in a 300-Mb genome assembly project. A support protocol to prepare input files for PCAP is also described. Another basic protocol for using PCAP on a distributed cluster of computers in a 3-Gb genome assembly project is presented, in addition to suggestions for understanding results from PCAP. http://onlinelibrary.wiley.com/doi/10.1002/0471250953.bi1103s11/abstract;jsessionid=D1E990D19BC5B53F5145818C47152BE5.f04t03

Seqcons: http://www.seqan.de/uploads/media/MicroRazerS.zip : Motivation: Novel high-throughput sequencing technologies pose new algorithmic challenges in handling massive amounts of short-read, high-coverage data. A robust and versatile consensus tool is of particular interest for such data since a sound multi-read alignment is a prerequisite for variation analyses, accurate genome assemblies and insert sequencing. Results: A multi-read alignment algorithm for de novo or reference-guided genome assembly is presented. The program identifies segments shared by multiple reads and then aligns these segments using a consistency-enhanced alignment graph. On real de novo sequencing data obtained from the newly established NCBI Short Read Archive, the program performs similarly in quality to other comparable programs. On more challenging simulated datasets for insert sequencing and variation analyses, our program outperforms the other tools. Availability: The consensus program can be downloaded fromhttp://www.seqan.de/projects/consensus.html. It can be used stand-alone or in conjunction with the Celera Assembler. Both application scenarios as well as the usage of the tool are described in the documentation. http://bioinformatics.oxfordjournals.org/content/25/9/1118.abstract

How to make a protein soluble?

noreply@blogger.com (Sanjiv Kumar) — Wed, 30 Apr 2014 14:21:00 +0000

Cloning, expression and purification of difficult to clone, express and purify proteins in E. coli

I have got some mails in relation to the expression of difficult to purify proteins, so I thought of making a short do's and don't's. For pure bioinformatic people, please bear with me for a couple of posts. First of all it is important to know about the protein, gather as much information about the protein as you can. All those small pieces of information help a lot if kept in mind while designing the strategy for cloning, expression and purification of the proteins. Also be informed about the source of protein, eukaryotic or prokaryotic or any others source. Some of the basic parameters like the size of the protein, PI, amino acid composition etc. pays a vital role in designing the strategy. Here are some tools to look for such information I have compiled on this blog before http://bioinformatictools.blogspot.in/2014/04/functional-annotation-of-hypothetical.html and http://bioinformatictools.blogspot.in/2011/11/in-silico-characterization-of-proteins.html. Look for other sources too. Main theme is to find as much information about the protein as much one could. I am not a big fan of purifying the protein under denaturing condition. There are lots of question that are difficult to answer if the protein needs to be refolded from denaturing conditions, like if the protein has folded properly, if this is the way the protein is natively folded and not just any random refolding of the protein, which are difficult to demonstrate experimentally until you already have some assay in mind. Since I have tried that too I will end by suggesting what all I have learned on that part.

Downstream experimental procedures: Before designing strategy for Cloning, expression and purification of protein, it is wise to determine the downstream experimental procedure you are going to perform and strategy for Cloning, expression and purification mainly depends on this. At times it is possible to purify the protein in soluble form in very small amount using a very large culture (which is ok, if you need very small amount of protein for downstream experiments) for which one need not go through all the standardization experiments with trials in different vectors and host cells. However, in case if large amount of protein is required (such as in crystallization experiments) it is advised to optimize the purification process overall.

Read as much as you can: There are various resources available for suggestions for cloning, expression and purification of the protein in soluble fraction (i.e. QIAexpress handbook). But please keep in mind that it’s easy to suggest in wet lab work but it takes a lot of time and energy to perform the experiments the way one wishes to, so try what you think is logical and more importantly easily available to you (do-able).

Membrane or membrane associated protein: check if the selected protein is Membrane or membrane associated protein. This can be done by using surface localization tools, some of them are listed here http://bioinformatictools.blogspot.in/2007/09/predicting-subcellular-localization-of.html. Also, check if the protein Transmembrane domain (TMHMM http://www.cbs.dtu.dk/services/TMHMM/) or signal peptide (Signal Phttp://www.cbs.dtu.dk/services/SignalP/) in it. These are hydrophobic regions and are normally intrinsically disordered. Membrane proteins are bit tough to get in soluble form till one removes the transmembrane or signal peptide part. It is logical to remove the initial (normally N-terminal) transmembrane or signal peptide part to get the functional domain or multiple domains in soluble form. (I had similar problem with a protein I was working on, when removed the signal peptide and transmembrane domain, it solved everything, got the protein into soluble fraction and got purified as charm, got it crystallized also).

Check for the functional domain in protein if any: This will help in determining the probable function the protein might be having. This will also indicate the other proteins with similar domain and their nature with respect to the cloning, expression and purification of the protein in E. coli. If you can find the protein with the similar domain use the cloning, expression and purification protocol for target protein. Also, for some of the protein the sequence based analysis results/characters change with addition of the tag, keep this in mind too, it might lead to change in PI or so on.

Optimize the temperature: Try different temperature for growth and induction. Induction temperature is more crucial.

Try growing cells at 37 C and induction at 37 C.
Try growing cells at 37 C and induction at 25 C for long time.
Try growing cells at 37 C and induction at 16 C for long time.
Try growing cells at 25 C and induction at 16 C for long time.
Try growing cells at 37 C followed by chilling at 16 C at least one hour before induction.

Low temperature decreases the rate of protein synthesis and usually more soluble protein is obtained. Also, if the temperature is reduced before induction of the cells, it is more likely to yield protein in soluble fraction, it kind of diverts from the pathway of going into inclusion bodies (Sorry, I do not know how).

Optimize the IPTG concentration: it is a good idea to check a gradient in a small scale for the amount of IPTG (using a range from 0.1, 0.2, 0.3 ….mM) required for optimal expression level of the protein. Normally, IPTG is required at very low levels for optimal expression and using higher concentration not only is costly, but also doesn’t show much improvement in the expression level of the protein.

Use a large tag, but make sure to make and arrangement to remove it once you have the protein: Larger tags like intein tag, His-SUMO, GST tag, MBP (maltose binding protein) etc. are known to increase the solubility of proteins, use them if you have the corresponding vectors easily available for them.

Change the vector: using a weaker promoter (e.g. trc instead of T7) and using a lower copy number plasmid normally increases the chance of protein to be purified in soluble fraction. Also, using N- and/or C- terminal tags (in various vectors) affects the solubility of the protein, especially in those protein where folding is dependent on any of these terminals.

Change the host cells: Some of the E. coli strains are better capable of handling toxic or membrane proteins in comparison to others. I had very good experience working with C41 and C43 strains which I came to know through this paper http://www.ncbi.nlm.nih.gov/pubmed/15294299. There are also pLysS versions of these strains, I did not try but you can read and try. Other strains like rosetta etc. might also be good to try (depends upon the strains you can get your hands on) (So, beg, borrow or steal ;)). For a new protein I usually perform as many changes one by one as I can do at small scale and then move them onto large scale. Also, check if your protein is using codons that are rarely used in E. coli. You can check ‘rare codon usage’ using different software available.

Change the culture media: After changing and optimizing as many parameters I could, I was getting low level of protein in soluble fraction in LB media, I read somewhere that someone had good yield with the Terrific Broth, I tried and it gave a way more protein in soluble fraction. I was happy to use it thereafter for any protein I had to purify.

Use Auto-induction media: it will be worthwhile trying auto-induction. The idea is that instead of using an inducing agent like IPTG one uses the native function of the T7 promoter. So if you use media containing glucose and lactose and grow the cells, as the glucose is depleted, the cells will slowly start activating their T7 promoters which will start using lactose in place of glucose. This will also induce the promoters on your expression vector and lead to a much more gradual expression than from using IPTG.

Functional Annotation of Hypothetical proteins

noreply@blogger.com (Sanjiv Kumar) — Wed, 30 Apr 2014 12:24:00 +0000

Experimental work is though time taking but is direct approach for functional annotation of hypothetical proteins; however, at times it is difficult to decide upon the experimental design for a relatively new class of a protein. With increasing size and quality of various protein databases, it is becoming relatively easier to look for the experimental design for the probable function of a protein. Following are the steps that can be used in choosing the type of experimental analysis that needs to be performed and the substrate to be used during laboratory tests.

1. If the protein is predicted to be an enzyme, BLAST results normally indicates its closely related proteins that can be looked upon for the experimental procedures to be performed as indicated by the matching hits (look for the papers on those proteins that might indicate the type of related function the protein might perform).

2. With the increasing domain databases, it is possible to analyze the protein domain wise indicating the ability to perform certain kind of biochemical reactions if any. The NCBI’s Conserved Doamin Database (CDD), Pfam and InterProScan databases have a large number of conserved domains that defines a functional class. Presence of certain domain is also indicative of the possible activity of the protein and therefore the type of substrate to be used for defining its chemical activity in laboratory could be helpful.

3. Composition based analysis of protein: there are various bioinformatics tools available online to studying the amino acid composition based analysis of protein informing various properties which help in indicating the properties of protein which later help with the functional annotation of the proteins i.e. Protparam, SPAAN, MP3 and a lot more etc.

4. Homology based modeling: this is an important step in determining the functional annotation of protein based on the structure of the protein, though it may be difficult for the proteins with low identity (<30%) with the already known crystal structures of the protein. However, a good homology model can be an important step towards determining functional annotation for a protein. So also the secondary and tertiary structure prediction of the protein will tell the similar functional categories thereby help in designing relative experimental assays. Some of the commonly used homology based modeling tools are listed here http://bioinformatictools.blogspot.in/2012/01/homology-modeling-of-proteins.html.

5. Phylogenetic analysis: Phylogenetic analysis not only shows evolutionary divergence of the protein but also act as an important step towards functional conservation of the protein. This helps in determining the degree of functional similarity with other related homologous proteins. Thus, determining the appropriate experimental assays towards functional annotation of the protein. With the help of molecular dynamic simulation, this also helps in-silico assessment of the ability of substrate to bind to the protein. In fact it can cut down from large number of substrate molecules to the top most hits, helping to prioritize the experimental analysis, saving time and resources.

6. It is sometimes a bit difficult while working with novel proteins for which relevant data is almost negligible worldwide, so you can wait till you get more information.

Let me know if you have more suggestions to add on.

Grep Tutorial

noreply@blogger.com (Sanjiv Kumar) — Sat, 08 Mar 2014 11:26:00 +0000

This is a reference booklet for grep and regular expression. For explanation of various usages in detail please refer more elaborate guide. grep: Global Regular Expression Print. GNU grep is combination of basic regular expressions, extended regular expressions, fixed strings and Perl-style regular expressions. Default behavior of grep is to return the filename and the line of the test that contains the searched string. Literals are the normal text characters, whereas metacharacters have special meanings. Backtic (``) enclosed portion is interpreted. Double quotes (“”) allow usage of environment variable as a part of search pattern.

There are two ways to search with grep i.e. searching for fixed string and searching for patterns. Concatenation is processed before alternation. Strings are concatenated by simply placing/being next to each other inside regular expression.

grep -E has advantage of accomplishing the task in fewer characters. If significant use of backreferences is required, grep -E is ideal.

grep -F, any search pattern for grep -F cannot contain any metacharacters, escapes, wildcards, or alternations.

Syntax usage of grep is as follows: grep [options] [regularexpression] [filename]

Example: grep -n 'error' logfile.txt

Metacharacter	Name	Matches
Single Character Match
.	Dot	Any one character
[…]	Character class	Any one member of the character listed in brackets
[^…]	Negates character class	Any character not listed in bracket (any one)
\char	Escape character	Use the character after escape (\) literally (not interpreted).
Position Match
^	Caret	Start of a line
$	Dollar	End of a line
\<	Backslash (less-than)	Start of a word
\>	Backslash (greater-than)	End of a word
Quantifiers
?	Question mark	Optional match (any single character)
*	Astrisk	Any number of occurrence including zero, wild card
+	Plus	One or more of preceding expression (repetitive match)
{N}	Exactly match	Match exactly N times
{N,}	Match at least	Match at least N times
{min,max}	Specified range	Match minimum and maximum times i.e. {3,4}

\|	Alternation	Match either of the expression given
-	Dash	Range
(…)	Parenthesis	Used to limit scope of alternation (sub pattern)
\1, \2, \3, …	Backreferences	Matches text previously matched within parenthesis
\b	Word boundary	Matches characters or words marked by the end of the word, i.e. space, period
\B	Backslash	Used for matching \ backslash same as \\
\w	Word character	Used for matching any word character, i.e. letter, number or underscore
\W	Non-word character	Used for matching anything considered not-word i.e. other than letter, number and underscore
\`	Start of buffer	Start of buffer sent to grep
\’	End of buffer	Matches the end of buffer sent to grep

POSIX definition
[:alpha:]		Any alphabetical character
[:digit:]		Any numerical character
[:alnum:]		Any alphabetical or numerical character
[:blank:]		Space or tab character
[:xdigit:]		Hexadecimal character
[:punct:]		Any punctuation symbol
[:print:]		Any printable character (not control characters)
[:space:]		Any white space character
[:graph:]		Excludes whitespace character
[:upper:]		Any uppercase letter
[:lower:]		Any lowercase letter
[:cntrl:]		Control character

Basic regular expression	grep or grep -G
-e	-e pattern	Recognizes pattern as regular expression argument i.e. grep -e -style (matches -style)
-f	-f file	Takes patterns from file. The pattern file must list one pattern per line.
-i	-i (ignore case)	Case insensitive search
-v	-v (invert match)	Returns lines that do not match pattern
-w	-w (word boundary match)	Matches exact word with boundary.
-x	-x (line match)	Matches entire line ‘Hello, World!’
-c	-c (counts)	Counts the number of matching lines
-l	grep -l “error” *.txt	Prints files containing the pattern, stops at first match
-L	grep -L “error” *.txt	Prints files that do not contain the pattern, stops at first match
-m num	grep -m 10 “error” *.txt	Stops reading file after num lines are matched i.e. only 10 lines that contain regular expression
-o	grep -o pattern filename	Prints only the text that matches
-q	quite	Suppresses output
-s	silent, no messages	Silently discards any error messages resulting from permission errors or non-existent files
-b	byte offset	Displays byte offset of each matching text instead of line number
-H	with filename	Includes the name of the file before each line printed (default when more than one file is input)
-h	no filename	when more than one filename is given it suppresses printing the filename before each output
--label=LABEL	adds label	It will prefix the line with LABLE
-n	line number	Includes the line number of each line displayed.
-T	initial tab	Inserts a tab before each matching line
-u	Unix byte offsets	Computes the byte offset as if it were running under Unix system
-z	null	Prints ASCII NUL (a zero byte) after each filename
-A num	after context = num	Prints num (number of lines) after match
-B num	before context = num	Prints num (number of lines) before match
-C num, -num		Prints num (number of lines before and after match
-R or -r	recursive	Searches files underneath directory submitted as an input file i.e. grep -R pattern path
Extended Regular Expressions	egrep or grep -E
?		Any character preceding ? may or may not appear in the target string.
+		Unlimited number of repetitions while looking for matching string, i.e. grep -E ‘regex1+’ filename (will look for regex1, regex11, regex111 etc.
{n,m}		Determines how many times a pattern needs to be repeated before matching. i.e. grep -E ‘regex{4,6}’ filename
\|		\| is or, allows to combine several patterns into one expression i.e. grep -E ‘regex1\|regex2’ filename
( )		Used to group particular strings of text for various roles i.e. backreferences, alternation, or simply readability
[{]		[ ] Used for matching the character without invoking the special meaning
Fixed strings / Fast grep	fgrep or grep -F
-c	Count	Counts the number of lines contain one or more instances of patter in a file i.e. fgrep -c ‘regex’ filename
-e		Used for searching more than one pattern or when the pattern begins with hyphen
-f	Outputs results to file	Outputs the results of the search into a file instead of printing it to the terminal
-h		When pattern is searched on more than one file, -h prevents fgrep from displaying filenames before the matched output.
-i	ignores case (capitalization)	-i option ignores capitalization in the pattern when matching it.
-l		Displays the files containing the pattern but not the matching lines.
-n	number of the line	Prints out the line number before the line that matches the pattern.
-v	reverse match	Matches any lines that do not contain the given pattern
Perl Style grep	grep -P	Perl-Compatible Regular Expression (PCRE)
PCRE-specific escapes
\a		Matches the alarm character
\cX		Matches ctrl+X, where X is any letter
\e		Matches escape character
\f		Matches from feed character
\n		Matches newline character
\r		Matches carriage return
\t		Matches tab character
\d		Matches any decimal digit
\D		Matches any non-decimal character
\s		Matches any whitespace character
\S		Matches any non-whitespace character
\w		Matches any word character
\W		Matches any non-word character
\b		Matches when at word boundary
\B		Matches when at not a word boundary
\A		Matches when at start of subject
\Z		Matches when at end of subject or before newline
\z		Matches when at end of subject
\G		Matches at first matching position

SPACE FOR NOTES:

Short Simple Tips and Techniques for use in Bioinformatics

noreply@blogger.com (Sanjiv Kumar) — Thu, 27 Feb 2014 09:59:00 +0000

Here I am compiling some of the short simple tips and tricks for use to ease out some of Bioinformatics work, I would also be thankful if you can contribute to it, if you can come across some. Write them as comment and I will update them.

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Running single or multiple commands on all files or selected files in a directory.

For running a command on number of files in a directory, use this:

CODE:

for i in *; do command; done;

Wherein * will select all the files to act upon, you can use *.fasta to work only with the fasta files, etc.
command is the linux command you wish to act on all the selected files, where in each selected file is represented by or recalled by "$i" and can also be used to generate output with the filename added to it.

Example:

for i in *.fasta; do grep ">gi" "$i" > "$i".output; done;

Similarly to copy a file from anywhere (previous directory in this example) in multiple directories

for i in *; do cp ../program.pl "$i" ; done;

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For working on multiples files, like removing, copying etc. on every directory inside a directory or multiple directories do the following:

To remove all the fasta files from the folder or sub folders

find /pathtofolder/ -type f -iname "*.fasta" -exec rm {} \;

This will find all the files
find /pathtofolder/ -type f -iname "*.fasta"

and this will remove these files
-exec rm {} \;

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To remove files containing a particular text in them from various folders inside the directory:

For example you have downloaded all genomes *.fna from NCBI Genomes for Bacterial Genomes and you just wish to use *.fna from the bacterial genomes but not from the plasmids which you wish to remove.

grep -lrIZ plasmid . | xargs -0 rm -f --

This will remove all files containing plasmid in it.

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To grep the count (say fasta) in various files inside a directory:

grep -c ">" */*/*.fasta > reads_count

This will count the number of reads from each file in those directories along with the file name and that can be redirected to a file.

NOTE: Change */*/*.fasta depending upon the number of directories inside i.e. */*.fasta will work if the files are in one directory below the present directory.

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Calculating Mean Length of FASTA Sequences

Here's a shot simple code for calculating the mean length of a FASTA sequence.

Code:

awk '{/>/&&++a||b+=length()}END{print b/a}' sequence.fasta > average

Alternate faster methods are available, read here BIOSTARS

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Convert FASTQ to FASTA:

Code:

sed -n ' 1~4s/^@/>/p; 2~4p ' file.fastq > file.fasta

sed 's/\ /\_/g' seqfile > seqfile_ed (to make unique ids in flash assembled sequences)

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Schedule one job after another

Run one script after another in such a way that second script starts after finishing first one. Without using Pipe | or ampercent && i.e. the first process is already running and you want second one to start after the first one finishes. And this can be done in different folder in case the output of second script will affect the output of first script. So run this on any folder you wish to.

Code:

while ps -p $PID; do sleep 1; done; script2

Explanation and Example:

while ps -p 4437; do sleep 1; done; echo "It Works"

Where $PID is the process id of the already running job (add PID number)

script2 is your script you wish to run after first script ends

sleep 1 is sleep for one second (SUFFIX may be ‘s’ for seconds (the default), ‘m’ for minutes, ‘h’ for hours or ‘d’ for days, read man sleep)

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Sample command for formatdb and blastall

FORMAT_DB:
nohup time /home/sanjiv/Softwares/blast-2.2.26/bin/formatdb -p T -i input.database &

BLAST_ALL:

Protein BLAST (blastp)

nohup /home/sanjiv/Softwares/blast-2.2.22/bin/blastall -p blastp -d location_of_database.fa -i location_of_input_file.fasta -o location_of_output_file.blastall.out -a 30 -e 0.000001 &

Nucleotide BLAST (blastx)

nohup /home/sanjiv/Softwares/blast-2.2.22/bin/blastall -p blastx -d location_of_database.fa -i location_of_input_file.fasta -o location_of_output_file.blastall.out -a 30 -e 0.000001 &

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Fetch FASTA sequences using identifier from a file.

CODE:

grep -A1 -f list mainfile > fastaofidsfromlist

Explanation:

To fetch selected fasta sequence from huge file. Keep your identifiers or fasta headers in 'list' (i.e. like Rv ids). 'mainfile' is the original file from where you have to fetch the fasta. 'fastaofidsfromlist' is the your file with fasta sequences of the ids you gave as list. Issue the following command.

CAUTION: For this to work properly, your mainfile should have sequences in one line only, without line breaks. Check output file before use.

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Convert multi-line fasta to single line fasta

awk 'BEGIN{RS=">"}NR>1{sub("\n","\t"); gsub("\n",""); print RS$0}' file

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Using BLAST+

Make blast database

makeblastdb -in proteases.faa -dbtype 'prot' -out CJProteases

BLAST

blastp -query proteins.fasta -db DATABASE -out OUTPUT_File -evalue 1e-30 -outfmt 7

LINUX BASICS FOR BEGINNERS AND ADVANCE USERS

noreply@blogger.com (Sanjiv Kumar) — Wed, 29 Jan 2014 14:30:00 +0000

Since long I was trying to learn Linux. I realized it is not as difficult as it seems. I have compiled some of the basic commands that are mandatory to learn to step into this beautiful world. They are not all, I have skipped grep, awk and sed knowingly, as I presume they deserve bit more attention. I will keep updating the list as soon as I can. Do look around too, some good video tutorials are also available that aid in learning. Also, I have made this available as a simple printable PDF.

LINUX BASICS FOR ADVANCE USERS - Download Here

Basic commands: On the terminal type these commands at-least two-three times to practice. Also, it is advisable to read man (help) pages of all these commands at least once.

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Command		Function
man <command>	:	Displays manual or help page for a commend, always read it full at least once
cd	:	Change directory, to navigate from one directory to another
cd -	:	Toggle between previous directory and current directory
ls	:	List files or folders, with several arguments give detail information about the files and folders, try: ls -ltrh
date	:	Displays current date
ssh	:	login to remote host
passwd	:	Change password
bc	:	Opens calculator
cp	:	Copies files or directories, usage: cp file.txt /directory
mv	:	Moves file, rename file, usage: mv file1.txt file2.txt (renames file1.txt to file2.txt)
pwd	:	Present working directory
mkdir	:	Make a new directory, usage: mkdir dir1 ; creates directory dir1
rmdir	:	Removes empty directory
cal	:	Opens calendar
echo	:	Displays message, usage: echo "hi" ; displays hi
printf	:	Alternative to echo, displays message
script	:	Records your session
uname	:	Know about your machine, usage: uname -a
tty	:	Know your terminal
stty	:	Display and setting terminal characteristics
gzip	:	Compressing files
gunzip	:	Decompress files
tar	:	Archival program
head <filename>	:	Displays beginning of a file
tail <filename>	:	Displays end of a file
exit	:	Exits terminal
who	:	Who is logged into the system
whoami	:	Username
hostname	:	Machine name
rm -r	:	Removes directory including its content recursively (be careful)
ctrl+w	:	Cuts last work with keyboard while typing on terminal
ctrl+y	:	Paste with keyboard
ctrl+a	:	Cursor at the beginning of the line while typing on terminal
ctrl+e	:	End of the line while typing on terminal
ctrl+k	:	Cuts to the end of the line while typing on terminal
ctra+c	:	Kills the process currently running
history	:	History of all the commands on terminal
~	:	Home directory, you ca do [ cd ~ ] to go to the home directory
wc	:	Word count, can be combined (piped through) various other commands (it gives results as lines, words and characters)
wc -c	:	Counts the number of bytes
wc -w	:	Counts the number of words
wc -l	:	Counts the number of lines
free -m	:	Free memory (ram) in mb
free -g	:	Free memory (ram) in gb
df	:	Disk space
du - sch <dir>	:	Disk space of the current directory
du -sch *	:	Disk space of individual files or directories
*du -sch \| sort -nr**	:	Disk space of individual files or directories sorted by file size
w	:	Who is logged onto the system and what are they doing
ps	:	Processes running by the users
ps -e	:	All the processes running in the system, also used with argument -a, -x, read man ps
ps -o %t -p <pid>	:	How long the process was running
ctrl+z	:	Suspend or sleep the current running process in foreground
bg	:	Run process in background
fg	:	Bring processes foreground, those running in background
kill <pid>	:	Kills a process with the given process id
kill -9 <pid>	:	Violently kills a given process id giving it no time to cleanup
kill -l <pid>	:	List all signals that can be sent to a process
kill -s sigstop <pid>	:	Suspends or sleep a process
kill -s sigcont <pid>	:	Resumes or wakes up a process
renice -n <value> <pid>	:	Gives a priority value to the process id, ranges from 1-19, higher the value lower the priority, default is 10, it is said to be how nice the process is i.e. How much ram it leaves for other process to run
cmp <file1> <file2>	:	Compares two given files
diff <file1> <file2>	:	Find the differences between two files, compares two files, argument -w ignores whitespace
mount	:	Mounts drive, usage: mount /media/usb
umount	:	Unmounts drive, usage: /media/usb
eject	:	Ejects cd rom, usage: /media/cdrom
join	:	Combines lines from two files on a common field, usage: join file1.txt file2.txt
tr a-z A-Z < file.txt	:	Transfers case to another i.e. Lowercase to uppercase
tr A-Z a-z < file.txt	:	Transfers case to another i.e. Uppercase to lowercase
xargs	:	Takes output of one command and passes it as an argument to another command usage: cat urllist.txt \| xargs wget -c
sort	:	Sorts the lines of a text files in ascending order, usage: sort file.txt ; argument -r sorts in descending order, usage: sort -r file.txt ; argument -t is delimiter (colon, space, comma, tab etc.), usage: sort -t: file.txt ; argument -k sorts on a particular field, I recommend to read more on this, important and very helpful, used in combination with other commands [ man sort ]
uniq	:	Removes duplicate entries from sorted files, therefore mostly used in combination with sort, in order for uniq to work, all the duplicate entries should be in adjacent lines, usage: sort names.txt \| uniq ; sort -u names.txt ; to count duplicate lines: sort file.txt \| uniq -c ; to display duplicate entries: sort file.txt \| uniq -cd
cut	:	Used to display only specific columns from a text file or other command outputs i.e. To display first field from a colon delimited file : cut -d: -f 1 file.txt ; to display first and third field from colon delimited file : cut -d: -f 1, 3 file.txt
find	:	Find files, usage: find [pathname] [condition], arguments it takes are -name (for names of the files), -type (for type of file) -size (for size of file i.e. +100m, -mtime (modified time in days i.e. +60 or -2), -exec
stat	:	Used either to check the status or properties of single file or file system, usage: stat /etc/file.txt ; argument -f i.e. Stat -f / (displays the status of the file system i.e. Size/total/free/available
ac -d	:	Displays the statics about the user connect time, argument -d breaks the output for individual days, usage: ac -d username
&	:	At the end of the command executes job in the background but if you logout the job will be killed
nohup	:	At the beginning of the command executes job in background with (ampercent &), usage: nohup ./script.sh & ; the job will run in background even if you logout
screen	:	Once you logout the same session will not be connected, to do that, end with screen and attach it later by screen to get back as it was when you logged out
at	:	Schedule job i.e. At -f backup.sh 10am tomorrow
watch	:	To execute command continuously at certain intervals, usage: watch df -h
split	:	Splits the given file (big file) as per the requirement with respective arguments, i.e. File size, number of lines, etc.

De-Novo Transcriptome Assembly and Annotation

noreply@blogger.com (Sanjiv Kumar) — Wed, 27 Nov 2013 07:51:00 +0000

Introduction

ranscriptome sequencing (RNA-seq) helps to find gene expression, reconstruct the transcripts, SNP detection and alternate splicing. There are two assembly approaches i.e. Genome dependent (alignment to reference genome) and genome independent (de novo assembly). De-novo assembly is the process of constructing a reference genome sequence for a newly sequenced organism. And it is necessary because for reference based sequencing reference genome is required and this approach is not useful for organism having partial or missing reference genome. Secondly genome sequences are incomplete, fragmented and altered. The disadvantage of genome assembly over transcriptome assembly is its inability to account for structural alterations of mRNA transcripts, i.e. alternative splicing.

In RNA-seq first mRNA is extracted and purified from cell and then reverse transcribed to create cDNA library with the help of high-throughput sequencing techniques. This cDNA is fragmented into various lengths.

Algorithm behind assembly is so simple, cDNA sequence reads are assembled into transcripts by using any short read assembler, here we are using ‘SOAPdenovo-Trans’. Short read assemblers generally one of two basic algorithms: overlap graphs – compute pairwise overlap between the reads and capture this information in a graph. De-Bruijin graph: breaks the reads into smaller sequences of DNA, called K-mers, and captures overlaps of length k-1 between these k-mers not between reads. As the number of reads are growing day by day and it is getting difficult to determine which read should be joined to contiguous sequence contigs, so, de-Bruijin is the solution of this problem in which a node is defined by fixed length of k-mer and nodes are connected by edges, if they overlap by k-1 nucleotide.

De novo assembly work flow

Input files

Paired-end RNA-seq reads of P.chabaudi in fastq format

File 1: ERR306016_1.fastq

File 2: ERR306016_2.fastq

Exercise 1. Quality control check using fastQC

Shell command for running fastQC on the read file:

./fastqc ERR306016_1.fastq

A. Removal of adaptor sequences using FASTX-Toolkit

Shell command:

./fastx_clipper -a -l -C -i ERR306016_1.fastq -o new_ERR306016_1.fastq

B. Run Quality check again on trimmed files i.e. new_ERR306016_1.fastq and new_ERR306016_2.fastq

Shell command:

./fastqc

Repeat Exercise 1 for ERR306016_2.fastq

Exercise 2. De novo assembly of reads as well as RPKM calculation using

SOAPdenovo-Trans

Shell command:

./SOAPdenovo-trans-127mer all -k 23 -R -s sample.conf -o plas_R

Clustering of resulted contigs or transcriptome using TGICL

Shell command:

./tgicl plas_R.contig

Result will be clustered contig indexes and singletons indexes (contigs which are not in cluster) and asm_1 directory containing CAP3 assembly result. If no singleton file form means all contigs are clustered. Contig sequence and Singlets sequence files inside the asm_1 directory are merged together using linux 'cat' command

Shell command:

cat contig singlets > contig_singlets.txt

Retreive the indexes from the cluster file by using 'grep' and ‘sed’

grep -v 'CL' plas_R.contig_clusters > contig_index.txt

Substitute the tab space with next line using 'sed' command.

sed 's/\t/\n/g' contig_index.txt > contig_index_new.txt

Retreive the clustered contig sequences from SOAPdenovo-trans assembled contig file 'plas_R.contig'

grep -Fxv -f contig_index_new.txt plas_R.contig > final_singletons.txt

Merge the 'contig_singlets.txt' into "final_singletons.txt" using ‘cat’ command

cat contig_singlets.txt final_singletons.txt > final_assembled_transcriptome.txt

Exercise 3. Read mapping using SeqMap

Shell command:

./seqmap 2 ERR306016_1.fastq plas_R_contig.fasta > seqmap_output.txt /eland:3 /available_memory:8000

Exercise 4. Annotation using standalone ncbi-BLAST and online tool KOBAS

Shell command:

./makeblastdb -in /home/user/Desktop/NGS_Workshop/jyoti/kobas_data/p.chabaudi.pep.fasta -input type 'fasta' -title 'plasmo_db' -dbtype 'prot'

./blastx -db /home/user/Desktop/NGS_Workshop/jyoti/kobas_data/p.chabaudi.pep.fasta -query /home/user/Desktop/NGS_Workshop/jyoti/plas_R.contig/ -out /kobas_input.fasta/ -outfmt 6

Run KOBAS using 'kobas_input.fasta'

KOBAS is available on the following url:

http://kobas.cbi.pku.edu.cn/home.do

The structure of Rv3717 reveals a novel amidase from Mycobacterium tuberculosis

noreply@blogger.com (Sanjiv Kumar) — Thu, 21 Nov 2013 12:06:00 +0000

The article is OPEN ACCESS:

http://journals.iucr.org/d/issues/2013/12/00/lv5048/index.html

The structure of Rv3717 reveals a novel amidase from Mycobacterium tuberculosis

A. Kumar, S. Kumar, D. Kumar, A. Mishra, R. P. Dewangan, P. Shrivastava, S. Ramachandran and B. Taneja

Abstract: Bacterial N-acetylmuramoyl-L-alanine amidases are cell-wall hydrolases that hydrolyze the bond between N-acetylmuramic acid and L-alanine in cell-wall glycopeptides. Rv3717 ofMycobacterium tuberculosis has been identified as a unique autolysin that lacks a cell-wall-binding domain (CBD) and its structure has been determined to 1.7 Å resolution by the Pt-SAD phasing method. Rv3717 possesses an /-fold and is a zinc-dependent hydrolase. The structure reveals a short flexible hairpin turn that partially occludes the active site and may be involved in autoregulation. This type of autoregulation of activity of PG hydrolases has been observed in Bartonella henselae amidase (AmiB) and may be a general mechanism used by some of the redundant amidases to regulate cell-wall hydrolase activity in bacteria. Rv3717 utilizes its net positive charge for substrate binding and exhibits activity towards a broad spectrum of substrate cell walls. The enzymatic activity of Rv3717 was confirmed by isolation and identification of its enzymatic products by LC/MS. These studies indicate that Rv3717, an N-acetylmuramoyl-L-alanine amidase from M. tuberculosis, represents a new family of lytic amidases that do not have a separate CBD and are regulated conformationally.

PDB reference: 4lq6

The article is OPEN ACCESS

http://journals.iucr.org/d/issues/2013/12/00/lv5048/index.html

Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

noreply@blogger.com (Sanjiv Kumar) — Mon, 04 Nov 2013 10:52:00 +0000

http://pubs.rsc.org/en/content/articlelanding/2013/mb/c3mb70278b#!divAbstract

We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules ‘Turquoise’, ‘Blue’ and ‘Red’ also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.

Identification of Novel Adhesins of M. tuberculosis H37Rv Using Integrated Approach of Multiple Computational Algorithms and Experimental Analysis.

noreply@blogger.com (Sanjiv Kumar) — Fri, 02 Aug 2013 18:15:00 +0000

My recent publication:

Identification of Novel Adhesins of M. tuberculosis H37Rv Using Integrated Approach of Multiple Computational Algorithms and Experimental Analysis

Authors:

Sanjiv Kumar, Bhanwar Lal Puniya, Shahila Parween, Pradip Nahar, Srinivasan Ramachandran

Abstract

Pathogenic bacteria interacting with eukaryotic host express adhesins on their surface. These adhesins aid in bacterial attachment to the host cell receptors during colonization. A few adhesins such as Heparin binding hemagglutinin adhesin (HBHA), Apa, Malate Synthase of M. tuberculosis have been identified using specific experimental interaction models based on the biological knowledge of the pathogen. In the present work, we carried out computational screening for adhesins of M. tuberculosis. We used an integrated computational approach using SPAAN for predicting adhesins, PSORTb, SubLoc and LocTree for extracellular localization, and BLAST for verifying non-similarity to human proteins. These steps are among the first of reverse vaccinology. Multiple claims and attacks from different algorithms were processed through argumentative approach. Additional filtration criteria included selection for proteins with low molecular weights and absence of literature reports. We examined binding potential of the selected proteins using an image based ELISA. The protein Rv2599 (membrane protein) binds to human fibronectin, laminin and collagen. Rv3717 (N-acetylmuramoyl-L-alanine amidase) and Rv0309 (L,D-transpeptidase) bind to fibronectin and laminin. We report Rv2599 (membrane protein), Rv0309 and Rv3717 as novel adhesins of M. tuberculosis H37Rv. Our results expand the number of known adhesins of M. tuberculosis and suggest their regulated expression in different stages.

Read Full article here (Free)

Identification of Novel Adhesins of M. tuberculosis H37Rv Using Integrated Approach of Multiple Computational Algorithms and Experimental Analysis

The Need of New Drugs for TB- It's Time for Action

noreply@blogger.com (Sanjiv Kumar) — Fri, 30 Nov 2012 10:51:00 +0000

Research resources for tuberculosis

noreply@blogger.com (Sanjiv Kumar) — Sun, 18 Mar 2012 18:01:00 +0000

Tuberculist: http://tuberculist.epfl.ch/ The TubercuList knowledge base integrates genome details, protein information, drug and transcriptome data, mutant and operon annotation, bibliography, structural views and comparative genomics, in a structured manner required for the rational development of new diagnostic, therapeutic and prophylactic measures against tuberculosis. With the means of expert curation and continuous updates, we deliver a broad view of the Mycobacterium tuberculosis genome.

TB Database: http://www.tbdb.org/ Tuberculosis (TB) is a public health challenge of paramount importance. Control of TB will require a multifaceted approach integrating efficient public health interventions with the discovery and use of new vaccines and drugs. TBDatabase (TBDB) makes available the tools and resources available at the Stanford Microarray Database and the Broad Institute. Anyone is welcome to access the published data available on the TBDB site without signing in. Some data in TBDB are unpublished and can therefore only be accessed by the authors and their collaborators after they sign in. The "Access Polices" page provides more information about TBDB accounts. A grant from the Bill & Melinda Gates Foundation has enabled us to create an integrated software platform for tuberculosis drug discovery and research. Learn more >

webTB.org: http://www.webtb.org/ a resource for Mycobacterium tuberculosis researchers is brought to you by the TB Structural Genomics Consortium with lots of tools: Gene expression correlation display: This server presents the pair wise gene expression correlation for two or more genes. For a set of genes you can get all the pair wise correlations and express it as a matrix, graphically display it.Operon Search: Search for operons and directions in the TB genome. Known operons are listed by name. These data will be expanded as more sources are found. BLAST the TB genome: BLAST a sequence against the TB genome and other NCBI databases. Target Explorer: Target Explorer is intended to be an interactive web site that allows researchers in the Tuberculosis research community to experiment with different target selection criteria and explore alternative ways of prioritizing gene targets for experimentation (crystallization structure solution, high-throughput screening for inhibitor discovery, etc.) Mycobacterial Genome DataBase: The site is a database of genomes of mycobacterial strains sequenced in the lab of James C. Sacchettini at Texas A&M University. It provides access to sequence data (including coverage statistics) and comparison of polymorphisms among various strains of tuberculosis, with a focus on drug-resistance. The sequencing is done on an Illumina GenomeAnalyzer II (short reads). The data was analyzed using customized sequence-assembly methods written by Tom Ioerger and his group at Texas A&M. The Genome browser: Graphically scan the entire TB genome for information on each ORF with indications of any predicted operons and includes links back to the quick search page for more gene information ORF Progress search tool: Search the status and progress of MTb ORFs that are targeted and pursued by consortium members. Structure Gallery: See the protein structures determined by members of the consortium.Structure Summary pagesThis page is a front page portal to the structure information on known TB proteins. All other WebTB servers can be accessed from this page, including the Gallery and JMOL viewer. MTBreg Database: A database of proteins up- and down-regulated in Mycobacterium tuberculosis grown under conditions mimicking infection as well as information on proteins that are regulated by selected transcription factors or other regulatory proteins. TBDB Legacy Tools: Legacy tools from the former TBSGC site to search and browse the TB Genome

TBDreamDB : http://www.tbdreamdb.com/index.html Exciting news at TBDreamDB! The Database is currently undergoing complete redesign and data evaluation. The database will be converted to a fully searchable relational Database with a new look front end website as well. We hope to have the beta design up and running towards the end of 2011 early 2012. Any feedback would be great, simply contact us at the curator email below. Also please keep sending in any errors you find in the dataset. The data is being evaluated and corrected as it is being inserted into the new database format. -Your Emails are much appreciated!

Sanger Institute Welcome Trust: The Wellcome Trust and DEFRA has funded the Sanger Instiute to sequence reference genomes for Mycobacterium africanum http://www.sanger.ac.uk/resources/downloads/bacteria/mycobacterium.html Mycobacterium is a genus within the order Actinomycetales that comprises a large number of well characterised species, several of which are associated with human and animal disease such as tuberculosis and leprosy.

MGDD : http://mirna.jnu.ac.in/mgdd/index.html (Mycobacterial Genome Divergence Database) is a repository of genetic differences among different strains and species of organisms belonging to Mycobacterium tuberculosis complex. The differences are based on comparison of user chosen organisms. The query sequences are used to compare against subject sequences. The users can also choose the type of genetic divergence, that is, SNPs (Single Nucleotide Polymorphism), insertions, repeat expansion and divergent sequences that they are interested in. The results from a specific region (based on boundary defined by nucleotide sequence) or a specific gene can be displayed based on user's choice. Presently, the database has precomputed analysis from three different fully sequenced genomes of this complex. These are Mycobacterium tuberculosis H37Rv, Mycobacterium tuberculosis CDC1551 and Mycobacterium bovis AF2122/97. In future it will be updated with more strains species as fully sequenced genomes become available.

MTBreg: http://www.doe-mbi.ucla.edu/Services/MTBreg/ Proteins up- and down- regulated in Mycobacterium tuberculosis grown under conditions mimicking infection are included in this database. It also includes information on proteins that are regulated by selected transcription factors or other regulatory proteins. The literature data provided here is complimentary to the databases provided by Michael Strong that include recent TB computational functional linkages and the Prolinks Database by Peter Bowers.

MycoperonDB: http://cdfd.org.in/mycoperondb/home.html is a database of computationaly predicted operons and transcriptional units of Mycobacteria. MycoperonDB is setup to provide operon and trancriptional unit information of different mycobacterial species at one place. At present, this database covers five species from mycobacteria and consist of an insilico model of operon organization of 18,053 genes . The operon information provides a basis and a refenece for a comprehensive understanding of how the transcriptional control are encoded in genome. The database has a user friendly web interface which takes simple sequence, gene name or ORF ID as an input and reports the transcription unit and operon associated with the input query.

GenoMycDB Browser: http://157.86.176.108/~catanho/genomycdb/

Several databases and computational tools have been created with the aim of organizing, integrating and analyzing the wealth of information generated by large-scale sequencing projects of mycobacterial genomes and those of other organisms. However, with very few exceptions, these databases and tools do not allow for massive and/or dynamic comparison of these data. GenoMycDB (http://www.dbbm.fiocruz.br/GenoMycDB) is a relational database built for large-scale comparative analyses of completely sequenced mycobacterial genomes, based on their predicted protein content. Its central structure is composed of the results obtained after pair-wise sequence alignments among all the predicted proteins coded by the genomes of six mycobacteria: Mycobacterium tuberculosis (strains H37Rv and CDC1551), M. bovis AF2122/97, M. avium subsp. paratuberculosis K10, M. leprae TN, and M. smegmatis MC2 155. The database stores the computed similarity parameters of every aligned pair, providing for each protein sequence the predicted subcellular localization, the assigned cluster of orthologous groups, the features of the corresponding gene, and links to several important databases. Tables containing pairs or groups of potential homologs between selected species/strains can be produced dynamically by user-defined criteria, based on one or multiple sequence similarity parameters. In addition, searches can be restricted according to the predicted subcellular localization of the protein, the DNA strand of the corresponding gene and/or the description of the protein. Massive data search and/or retrieval are available, and different ways of exporting the result are offered. GenoMycDB provides an on-line resource for the functional classification of mycobacterial proteins as well as for the analysis of genome structure, organization, and evolution.

TB Drug Target Database: http://www.bioinformatics.org/tbdtdb/ TB Drug Target Database contains information on the antituberculer drugs and the target proteins for the treatment of TB. Informations are avilable on the drugs and other possible inhibitors including their Structural details, also the analysis made to the target proteins are made available.

MIRU-VNTRplus web application: http://www.miru-vntrplus.org/MIRU/index.faces Molecular typing of bacteria from the Mycobacterium tuberculosis complex (MTBC) is essential for epidemiological purposes such as investigating the spreading of specific genotypes. Recently, mycobacterial interspersed repetitive units (MIRU) typing has become an important method, as it allows high-throughput, discriminatory and reproducible analysis of clinical isolates. MIRU is a MTBC specific name of a multiple locus VNTR [variable number of tandem repeats] analysis (MLVA) bacterial typing scheme. Because of its portable data format, MIRU typing has the potential to be a versatile tool for individual strain identification based on large reference databases. However, specialized bioinformatic web tools to analyze MIRU data and public reference databases are not available. To meet this need, a collection of 186 strains representing the major MTBC lineages was used for implementing a web server, MIRU-VNTRplus (http://www.miru-vntrplus.org/). For each strain species, lineage, and epidemiologic information was stored together with copy numbers of 24 MIRU loci, spoligotyping patterns, regions of difference (RD) profiles, single nucleotide polymorphisms (SNPs), susceptibility data, and IS6110 RFLP fingerprint images. Via the freely accessible MIRU-VNTRplus service users can compare their strain(s) with the reference strains for the assignment of MTBC species, lineages, and genotypes. For easier scientific communication a universal expanding nomenclature (MLVA MtbC15-9) to name different MIRU genotypes is maintained at the server. Comparisons can be based on MIRU-, spoligo-, RD-, SNP-, susceptibility-typing data, or by a combination of different data types. Several distance coefficients are available, including Jaccard's and categorical. Based upon the respective distance matrix, a dendrogram can be calculated using UPGMA or neighbor-joining clustering algorithms. The resulting trees may be exported in various data formats. MIRU-VNTRplus provides also functions for the user to analyze own strains without interrogating the reference database. Extensive documentation (manual and tutorials) of the service is available to make best use of all features.

Web resources for Mycobacterium tuberculosis

noreply@blogger.com (Sanjiv Kumar) — Sun, 18 Mar 2012 17:58:00 +0000

Mycobacterium tuberculosis the causative agent of tuberculosis (TB) still the major killer among human population, that has co-evolved with the human civilization so beautifully and so robustly, is really surprising. The war against tuberculosis is on and we are trying here to integrate major resources that are available for the disease and the organism all over the web to a single place. This is one of the open articles that would be upgraded time to time for acknowledging the recent development in the field, so that you as a used need not spend much time in data collection and integration. Hope this helps.

General information about tuberculosis:

Wiki page on tuberculosis on Wikipedia: http://en.wikipedia.org/wiki/Tuberculosis Tuberculosis, MTB, or TB (short for tubercle bacillus) is a common, and in many cases lethal, infectious disease caused by various strains of mycobacteria, usually Mycobacterium tuberculosis. Tuberculosis usually attacks the lungs but can also affect other parts of the body. It is spread through the air when people who have an active MTB infection cough, sneeze, or otherwise transmit their saliva through the air. Most infections in humans result in an asymptomatic, latent infection, and about one in ten latent infections eventually progress to active disease, which, if left untreated, kills more than 50% of those infected.

Wiki page on M. tuberculosis: Mycobacterium tuberculosis (MTB) is a pathogenic bacterial species in the genus Mycobacterium and the causative agent of most cases of tuberculosis (TB). First discovered in 1882 by Robert Koch, M. tuberculosis has an unusual, waxy coating on its cell surface (primarily mycolic acid), which makes the cells impervious to Gram staining, so acid-fast detection techniques are used, instead. The physiology of M. tuberculosis is highly aerobic and requires high levels of oxygen. Primarily a pathogen of the mammalian respiratory system, MTB infects the lungs. The most frequently used diagnostic methods for TB are the tuberculin skin test, acid-fast stain, and chest radiographs. The M. tuberculosis genome was sequenced in 1998.

Centers for Disease Control and Prevention (CDC) page on tuberculosis: Tuberculosis (TB) is a disease caused by a bacterium called Mycobacterium tuberculosis. The bacteria usually attack the lungs, but TB bacteria can attack any part of the body such as the kidney, spine, and brain. If not treated properly, TB disease can be fatal. TB disease was once the leading cause of death in the United States. Focusing on Data and Statistics, Diseases and Conditions, Emergencies and Disasters, Environmental Health, Healthy Living, Injury, Violence and Safety, Life Stages and Populations, Travelers' Health, Workplace Safety and Health, Learn More »

Global TB database: http://www.who.int/tb/country/global_tb_database/en/index2.html Access the database to make data queries, interactive maps and country profiles. Country profiles: Country profiles provide key indicators, notification and treatment outcome data, and budget and financing graphs (for a subset of countries, including all high-burden countries). For high-burden countries, there is also a brief overview of TB control and epidemiology in the country, and a summary of achievements, challenges and planned activities related to implementing the first 5 components of the Stop TB Strategy.

U.S. National Library of Medicine Resource on tuberculosis: Has a resourced on various fields of the disease, from the basics to the advanced including the disease data.

WHO page on tuberculosis: Too many people have undetected TB for too long; late detection of TB increases their risk of transmitting the disease to others, having poor health outcomes, or that they and their family will suffer distress and economic hardship. WHO has produced an overview of approaches, guidelines and tools to improve early detection of TB. It presents a framework to assess barriers for early detection and helps identify appropriate actions.

The Stop TB Partnership: The Stop TB Partnership is leading the way to a world without tuberculosis (TB), a disease that is curable but still kills three people every minute. Founded in 2001, the Partnership's mission is to serve every person who is vulnerable to TB and ensure that high-quality treatment is available to all who need it. Together our nearly 1000 partners are a collective force that is transforming the fight against TB in more than 100 countries. They include international and technical organizations, government programmes, research and funding agencies, foundations, NGOs, civil society and community groups and the private sector. We operate through a secretariat hosted by the World Health Organization (WHO) in Geneva, Switzerland and seven working groups whose role is to accelerate progress on access to TB diagnosis and treatment; research and development for new TB diagnostics, drugs and vaccines; and tackling drug resistant- and HIV-associated TB. The secretariat is governed by a coordinating board that sets strategic direction for the global fight against TB.

Bioinformatics Tools

pET Vectors

Sequence Variation of Rare Outer Membrane Protein β-Barrel Domains in Clinical Strains Provides Insights into the Evolution of Treponema pallidum subsp. pallidum, the Syphilis Spirochete

The major outer sheath protein forms distinct conformers and multimeric complexes in the outer membrane and periplasm of Treponema denticola

The Treponema pallidum Outer Membrane

Consensus Phylogenetic trees of Fifteen Prokaryotic Aminoacyl-tRNA Synthetase Polypeptides based on Euclidean Geometry of All-Pairs Distances and Concatenation

Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins

Recent article from our lab:

Read more at: http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2753-8

Background

Results

Conclusions

Keywords

Reconstruction of bacterial and viral genomes from multiple metagenomes

Predicting Beta Barrel Outer Membrane Proteins (OMPs)

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

MP3: A Software Tool for the Prediction of Pathogenic Proteins in Genomic and Metagenomic Data

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

Metagenomic Assemblers:

Genomic Assemblers:

How to make a protein soluble?

Functional Annotation of Hypothetical proteins

Grep Tutorial

Short Simple Tips and Techniques for use in Bioinformatics

LINUX BASICS FOR BEGINNERS AND ADVANCE USERS

De-Novo Transcriptome Assembly and Annotation

The structure of Rv3717 reveals a novel amidase from Mycobacterium tuberculosis

The article is OPEN ACCESS:

http://journals.iucr.org/d/issues/2013/12/00/lv5048/index.html

The structure of Rv3717 reveals a novel amidase from Mycobacterium tuberculosis

A. Kumar, S. Kumar, D. Kumar, A. Mishra, R. P. Dewangan, P. Shrivastava, S. Ramachandran and B. Taneja

http://journals.iucr.org/d/issues/2013/12/00/lv5048/index.html

Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

Identification of Novel Adhesins of M. tuberculosis H37Rv Using Integrated Approach of Multiple Computational Algorithms and Experimental Analysis.

The Need of New Drugs for TB- It's Time for Action

Research resources for tuberculosis

Web resources for Mycobacterium tuberculosis