BIND – An algorithm for loss-less compression of nucleotide sequence data

Recent advances in DNA sequencing technologies have enabled the current generation of life science researchers to probe deeper into the genomic blueprint. The amount of data generated by these technologies has been increasing exponentially since the last decade. Storage, archival and dissemination of such huge data sets require efficient solutions, both from the hardware as well as software perspective. The present paper describes BIND-an algorithm specialized for compressing nucleotide sequence data. By adopting a unique 'block-length' encoding for representing binary data (as a key step), BIND achieves significant compression gains as compared to the widely used general purpose compression algorithms (gzip, bzip2 and lzma). Moreover, in contrast to implementations of existing specialized genomic compression approaches, the implementation of BIND is enabled to handle non-ATGC and lowercase characters. This makes BIND a loss-less compression approach that is suitable for practical use. More importantly, validation results of BIND (with real-world data sets) indicate reasonable speeds of compression and decompression that can be achieved with minimal processor/ memory usage. BIND is available for download at http://metagenomics.atc.tcs.com/compression/BIND. No license is required for academic or non-profit use.     

Design,green synthesis and pharmacological evaluation of novel 5,6-diaryl-1,2,4-triazines bearing 3-morpholinoethylamine moiety as potential antithrombotic agents*

The aim of this research work was to investigate a series of novel 5,6-diaryl-1,2,4-triazines (3a–3q) containing 3-morpholinoethylamine side chain, and to address their antiplatelet activity by in vitro, ex vivo and in vivo methods. All compounds were synthesized by environment benign route and their structures were unambiguously confirmed by spectral data. Compounds (3l) and (3m) were confirmed by their single crystal X-ray structures. Out of all the synthesized compounds, 10 were found to be more potent in vitro than aspirin; six of them were found to be prominent in ex vivo assays and one compound (3d) was found to have the most promising antithrombotic profile in vivo. Moreover, compound (3d) demonstrated less ulcerogenicity in rats as compared to aspirin. The selectivity of the most promising compound (3d) for COX-1 and COX-2 enzymes was determined with the help of molecular docking studies and the results were correlated with the biological activity.     

Binpairs: Utilization of Illumina Paired-End Information for Improving Efficiency of Taxonomic Binning of Metagenomic Sequences

Motivation

Paired-end sequencing protocols, offered by next generation sequencing (NGS) platforms like Illumia, generate a pair of reads for every DNA fragment in a sample. Although this protocol has been utilized for several metagenomics studies, most taxonomic binning approaches classify each of the reads (forming a pair), independently. The present work explores some simple but effective strategies of utilizing pairing-information of Illumina short reads for improving the accuracy of taxonomic binning of metagenomic datasets. The strategies proposed can be used in conjunction with all genres of existing binning methods.

Results

Validation results suggest that employment of these “Binpairs” strategies can provide significant improvements in the binning outcome. The quality of the taxonomic assignments thus obtained are often comparable to those that can only be achieved with relatively longer reads obtained using other NGS platforms (such as Roche).

Availability

An implementation of the proposed strategies of utilizing pairing information is freely available for academic users at https://metagenomics.atc.tcs.com/binning/binpairs.     

The population structure of African cultivated rice oryza glaberrima (Steud.): evidence for elevated levels of linkage disequilibrium caused by admixture with O. sativa and ecological adaptation

Genome-wide linkage disequilibrium (LD) was investigated for 198 accessions of Oryza glaberrima using 93 nuclear microsatellite markers. Significantly elevated levels of LD were detected, even among distantly located markers. Free recombination among loci at the population genetic level was shown (1) by a lack of decay in LD among markers on the same chromosome and (2) by a strictly increasing composite likelihood function for the recombination parameter. This suggested that the elevation in LD was due not to physical linkage but to other factors, such as population structure. A Bayesian clustering analysis confirmed this hypothesis, indicating that the sample of O. glaberrima in this study was subdivided into at least five cryptic subpopulations. Two of these subpopulations clustered with control samples of O. sativa, subspecies indica and japonica, indicating that some O. glaberrima accessions represent admixtures. The remaining three O. glaberrima subpopulations were significantly associated with specific combinations of phenotypic traits-possibly reflecting ecological adaptation to different growing environments.     

ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples

Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of 'correct' assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from http://metagenomics.atc.tcs.com/binning/ProViDE/     

Structure–sequence features based prediction of phosphosites of serine/threonine protein kinases of Mycobacterium tuberculosis

Elucidation of signaling events in a pathogen is potentially important to tackle the infection caused by it. Such events mediated by protein phosphorylation play important roles in infection, and therefore, to predict the phosphosites and substrates of the serine/threonine protein kinases, we have developed a Machine learning-based approach for Mycobacterium tuberculosis serine/threonine protein kinases using kinase-peptide structure–sequence data. This approach utilizes features derived from kinase three-dimensional-structure environment and known phosphosite sequences to generate support vector machine (SVM)-based kinase-specific predictions of phosphosites of serine/threonine protein kinases (STPKs) with no or scarce data of their substrates. SVM outperformed the four machine learning algorithms we tried (random forest, logistic regression, SVM, and k-nearest neighbors) with an area under the curve receiver-operating characteristic value of 0.88 on the independent testing dataset and a 10-fold cross-validation accuracy of ~81.6% for the final model. Our predicted phosphosites of M. tuberculosis STPKs form a useful resource for experimental biologists enabling elucidation of STPK mediated posttranslational regulation of important cellular processes.     

Identification of conserved residue patterns in small beta-barrel proteins

Our abilities to predict three-dimensional conformation of a polypeptide, given its amino acid sequence, remain limited despite advances in structure analysis. Analysis of structures and sequences of protein families with similar secondary structural elements, but varying topologies, might help in addressing this problem. We have studied the small beta-barrel class of proteins characterized by four strands (n = 4) and a shear number of 8 (S = 8) to understand the principles of barrel formation. Multiple alignments of the various protein sequences were generated for the analysis. Positional entropy, as a measure of residue conservation, indicated conservation of non-polar residues at the core positions. The presence of a type II beta-turn among the various barrel proteins considered was another strikingly invariant feature. A conserved glycyl-aspartyl dipeptide at the beta-turn appeared to be important in guiding the protein sequence into the barrel fold. Molecular dynamics simulations of the type II beta-turn peptide suggested that aspartate is a key residue in the folding of the protein sequence into the barrel. Our study suggests that the conserved type II beta-turn and the non-polar residues in the barrel core are crucial for the folding of the protein's primary sequence into the beta-barrel conformation.     

Crystal structure of Mycobacterium tuberculosis YefM antitoxin reveals that it is not an intrinsically unstructured protein

Toxin-antitoxin modules are present on chromosomes of almost all free-living prokaryotes. Some are implicated to act as stress-responsive elements, among their many functional roles. The YefM-YoeB toxin-antitoxin system is present in many bacterial species, where YefM belongs to the Phd family antidote of phage P1, whereas YoeB is a homolog of the RelE toxin of the RelBE system, rather than the Doc system of phage P1. YoeB, a ribonuclease, is believed to be conformationally stable, whereas YefM has been proposed to be a member of intrinsically disordered proteins. The ribonucleolytic activity of YoeB is neutralized by YefM upon formation of the YefM-YoeB complex. We report here the crystal structure of Mycobacterium tuberculosis YefM from two crystal isoforms. Our crystallographic and biophysical studies reveal that YefM is not an intrinsically unfolded protein and instead forms a well-defined structure with significant secondary and tertiary structure conformations. The residues involved in core formation of the folded structure are evolutionarily conserved among many bacterial species, supporting our observation. The C-terminal end of its polypeptide is highly pliable, which adopts different conformations in different monomers. Since at the physiological level YefM controls the activity of YoeB through intricate protein-protein interactions, the conformational heterogeneity in YefM revealed by our structure suggests that these might act a master switch in controlling YoeB activity.     

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only ''pair-wise interactions''. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the ''Apriori algorithm'' for deriving ''microbial association rules'' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various ''(subsets of) features/items'' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between ''subsets/subgroups'' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving ''microbial association rules'' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.