Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

A quick guide for building a successful bioinformatics community

“Scientific community” refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop “The ‘How To Guide’ for Establishing a Successful Bioinformatics Network” at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB)

Novel Drosophila Viruses Encode Host-Specific Suppressors of RNAi

Contains fulltext : 136405.pdf (publisher's version ) (Open Access)The ongoing conflict between viruses and their hosts can drive the co-evolution between host immune genes and viral suppressors of immunity. It has been suggested that an evolutionary 'arms race' may occur between rapidly evolving components of the antiviral RNAi pathway of Drosophila and viral genes that antagonize it. We have recently shown that viral protein 1 (VP1) of Drosophila melanogaster Nora virus (DmelNV) suppresses Argonaute-2 (AGO2)-mediated target RNA cleavage (slicer activity) to antagonize antiviral RNAi. Here we show that viral AGO2 antagonists of divergent Nora-like viruses can have host specific activities. We have identified novel Nora-like viruses in wild-caught populations of D. immigrans (DimmNV) and D. subobscura (DsubNV) that are 36% and 26% divergent from DmelNV at the amino acid level. We show that DimmNV and DsubNV VP1 are unable to suppress RNAi in D. melanogaster S2 cells, whereas DmelNV VP1 potently suppresses RNAi in this host species. Moreover, we show that the RNAi suppressor activity of DimmNV VP1 is restricted to its natural host species, D. immigrans. Specifically, we find that DimmNV VP1 interacts with D. immigrans AGO2, but not with D. melanogaster AGO2, and that it suppresses slicer activity in embryo lysates from D. immigrans, but not in lysates from D. melanogaster. This species-specific interaction is reflected in the ability of DimmNV VP1 to enhance RNA production by a recombinant Sindbis virus in a host-specific manner. Our results emphasize the importance of analyzing viral RNAi suppressor activity in the relevant host species. We suggest that rapid co-evolution between RNA viruses and their hosts may result in host species-specific activities of RNAi suppressor proteins, and therefore that viral RNAi suppressors could be host-specificity factors

Designing an in silico strategy to select tissue-leakage biomarkers using the galaxy framework

International audienceKnowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discover

The Myb-p300-CREB axis modulates intestine homeostasis, radiosensitivity and tumorigenesis

The gastrointestinal (GI) epithelium is constantly renewing, depending upon the intestinal stem cells (ISC) regulated by a spectrum of transcription factors (TFs), including Myb. We noted previously in mice with a p300 mutation (plt6) within the Myb-interaction-domain phenocopied Myb hypomorphic mutant mice with regard to thrombopoiesis, and here, changes in GI homeostasis. p300 is a transcriptional coactivator for many TFs, most prominently cyclic-AMP response element-binding protein (CREB), and also Myb. Studies have highlighted the importance of CREB in proliferation and radiosensitivity, but not in the GI. This prompted us to directly investigate the p300–Myb–CREB axis in the GI. Here, the role of CREB has been defined by generating GI-specific inducible creb knockout (KO) mice. KO mice show efficient and specific deletion of CREB, with no evident compensation by CREM and ATF1. Despite complete KO, only modest effects on proliferation, radiosensitivity and differentiation in the GI under homeostatic or stress conditions were evident, even though CREB target gene pcna (proliferating cell nuclear antigen) was downregulated. creb and p300 mutant lines show increased goblet cells, whereas a reduction in enteroendocrine cells was apparent only in the p300 line, further resembling the Myb hypomorphs. When propagated in vitro, crebKO ISC were defective in organoid formation, suggesting that the GI stroma compensates for CREB loss in vivo, unlike in MybKO studies. Thus, it appears that p300 regulates GI differentiation primarily through Myb, rather than CREB. Finally, active pCREB is elevated in colorectal cancer (CRC) cells and adenomas, and is required for the expression of drug transporter, MRP2, associated with resistance to Oxaliplatin as well as several chromatin cohesion protein that are relevant to CRC therapy. These data raise the prospect that CREB may have a role in GI malignancy as it does in other cancer types, but unlike Myb, is not critical for GI homeostasis

Deciphering metatranscriptomic data

International audienceMetatranscriptomic data contributes another piece of the puzzle to understanding the phylogenetic structure and function of a community of organisms. High-quality total RNA is a bountiful mixture of ribosomal, transfer, messenger and other noncoding RNAs, where each family of RNA is vital to answering questions concerning the hidden microbial world. Software tools designed for deciphering metatranscriptomic data fall under two main categories: the first is to reassemble millions of short nucleotide fragments produced by high-throughput sequencing technologies into the original full-length transcriptomes for all organisms within a sample, and the second is to taxonomically classify the organisms and determine their individual functional roles within a community. Species identification is mainly established using the ribosomal RNA genes, whereas the behavior and functionality of a community is revealed by the messenger RNA of the expressed genes. Numerous chemical and computational methods exist to separate families of RNA prior to conducting further downstream analyses, primarily suitable for isolating mRNA or rRNA from a total RNA sample. In this chapter, we demonstrate a computational technique for filtering rRNA from total RNA using the software SortMeRNA. Additionally, we propose a post-processing pipeline using the latest software tools to conduct further studies on the filtered data, including the reconstruction of mRNA transcripts for functional analyses and phylogenetic classification of a community using the ribosomal RNA