Highlights from the 6th International Society for Computational Biology Student Council Symposium at the 18th Annual International Conference on Intelligent Systems for Molecular Biology

This meeting report gives an overview of the keynote lectures and a selection of the student oral and poster presentations at the 6th International Society for Computational Biology Student Council Symposium that was held as a precursor event to the annual international conference on Intelligent Systems for Molecular Biology (ISMB). The symposium was held in Boston, MA, USA on July 9th, 2010

Comparative analysis of mycobacterium and related actinomycetes yields insight into the evolution of mycobacterium tuberculosis pathogenesis

<p>Abstract</p> <p>Background</p> <p>The sequence of the pathogen <it>Mycobacterium tuberculosis </it>(<it>Mtb</it>) strain <it>H37Rv </it>has been available for over a decade, but the biology of the pathogen remains poorly understood. Genome sequences from other <it>Mtb </it>strains and closely related bacteria present an opportunity to apply the power of comparative genomics to understand the evolution of <it>Mtb </it>pathogenesis. We conducted a comparative analysis using 31 genomes from the Tuberculosis Database (TBDB.org), including 8 strains of <it>Mtb </it>and <it>M. bovis</it>, 11 additional Mycobacteria, 4 Corynebacteria, 2 Streptomyces, <it>Rhodococcus jostii RHA1, Nocardia farcinia, Acidothermus cellulolyticus, Rhodobacter sphaeroides, Propionibacterium acnes</it>, and <it>Bifidobacterium longum</it>.</p> <p>Results</p> <p>Our results highlight the functional importance of lipid metabolism and its regulation, and reveal variation between the evolutionary profiles of genes implicated in saturated and unsaturated fatty acid metabolism. It also suggests that DNA repair and molybdopterin cofactors are important in pathogenic Mycobacteria. By analyzing sequence conservation and gene expression data, we identify nearly 400 conserved noncoding regions. These include 37 predicted promoter regulatory motifs, of which 14 correspond to previously validated motifs, as well as 50 potential noncoding RNAs, of which we experimentally confirm the expression of four.</p> <p>Conclusions</p> <p>Our analysis of protein evolution highlights gene families that are associated with the adaptation of environmental Mycobacteria to obligate pathogenesis. These families include fatty acid metabolism, DNA repair, and molybdopterin biosynthesis. Our analysis reinforces recent findings suggesting that small noncoding RNAs are more common in Mycobacteria than previously expected. Our data provide a foundation for understanding the genome and biology of <it>Mtb </it>in a comparative context, and are available online and through TBDB.org.</p

Semantically linking molecular entities in literature through entity relationships

Background Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts. Results We describe, compare and evaluate two frameworks developed for the prediction of non-causal or 'entity' relations (REL) between gene symbols and domain terms. For the corresponding REL challenge of the BioNLP Shared Task of 2011, these systems ranked first (57.7% F-score) and second (41.6% F-score). In this paper, we investigate the performance discrepancy of 16 percentage points by benchmarking on a related and more extensive dataset, analysing the contribution of both the term detection and relation extraction modules. We further construct a hybrid system combining the two frameworks and experiment with intersection and union combinations, achieving respectively high-precision and high-recall results. Finally, we highlight extremely high-performance results (F-score > 90%) obtained for the specific subclass of embedded entity relations that are essential for integrating text mining predictions with database facts. Conclusions The results from this study will enable us in the near future to annotate semantic relations between molecular entities in the entire scientific literature available through PubMed. The recent release of the EVEX dataset, containing biomolecular event predictions for millions of PubMed articles, is an interesting and exciting opportunity to overlay these entity relations with event predictions on a literature-wide scale

Comparative and Functional Genomics of Rhodococcus opacus PD630 for Biofuels Development

The Actinomycetales bacteria Rhodococcus opacus PD630 and Rhodococcus jostii RHA1 bioconvert a diverse range of organic substrates through lipid biosynthesis into large quantities of energy-rich triacylglycerols (TAGs). To describe the genetic basis of the Rhodococcus oleaginous metabolism, we sequenced and performed comparative analysis of the 9.27 Mb R. opacus PD630 genome. Metabolic-reconstruction assigned 2017 enzymatic reactions to the 8632 R. opacus PD630 genes we identified. Of these, 261 genes were implicated in the R. opacus PD630 TAGs cycle by metabolic reconstruction and gene family analysis. Rhodococcus synthesizes uncommon straight-chain odd-carbon fatty acids in high abundance and stores them as TAGs. We have identified these to be pentadecanoic, heptadecanoic, and cis-heptadecenoic acids. To identify bioconversion pathways, we screened R. opacus PD630, R. jostii RHA1, Ralstonia eutropha H16, and C. glutamicum 13032 for growth on 190 compounds. The results of the catabolic screen, phylogenetic analysis of the TAGs cycle enzymes, and metabolic product characterizations were integrated into a working model of prokaryotic oleaginy.Cambridge-MIT InstituteMassachusetts Institute of Technology. (Seed Grant program)Shell Oil CompanyNational Institute of Allergy and Infectious Diseases (U.S.)United States. National Institutes of HealthNational Institutes of Health. Department of Health and Human Services (Contract No. HHSN272200900006C

Computational pan-genomics: Status, promises and challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations

Genomic and functional analyses of mycobacterium tuberculosis strains implicate ald in D-cycloserine resistance.

CAPRISA, 2016.Abstract available in PDF file

Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal.

CAPRISA, 2015.Abstract available in pdf