Abstract

In this work we discuss the benefits of using computational intelligence methods, like Support Vector Machines (SVM) for the optimization of the prediction of compounds solubility. SVMs are trained with a database of known soluble and insoluble compounds, and this information is being exploited afterwards to improve Virtual Screening (VS) prediction. The landscape in the high performance computing arena opens up great opportunities in the simulation of relevant biological systems and for applications in bioinformatics, computational biology and computational chemistry. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS methods and the model. We discussed the benefits of using massively parallel architectures, in particular graphics processing units. We empirically demonstrate that GPUs are well-suited architecture for the acceleration of SVM, obtaining up to 15 times sustained speedup compared to its sequential counterpart version.

Abstract

Background: The analysis of next generation sequencing (NGS) has become a standard task for many laboratories in the life sciences. Though there exists several tools to support users in the manipulation of such datasets on various levels, few are built on the basis of vertical integration. Here, we present the NExt generation Analysis Toolbox (NEAT) that allows non-expert users including wet-lab scientists to comprehensively build, run and analyze NGS data through double-clickable executables without the need of any programming experience. Results: In comparison to many publicly available tools including Galaxy, NEAT provides three main advantages: (1) Through the development of double-clickable executables, NEAT is efficient (completes within "24 hours), easy to implement and intuitive; (2) Storage space, maximum number of job submissions, wall time and cluster-specific parameters can be customized as NEAT is run on the institution’s cluster; (3) NEAT allows users to visualize and summarize NGS data rapidly and efficiently using various built-in exploratory data analysis tools including metagenomic and differentially expressed gene analysis. To simplify the control of the workflow, NEAT projects are built around a unique and centralized file containing sample names, replicates, conditions, antibodies, alignment-, filtering- and peak calling parameters as well as cluster-specific paths and settings. Moreover, the small-sized files produced by NEAT allow users to easily manipulate, consolidate and share datasets from different users and institutions. Conclusions: NEAT provides biologists and bioinformaticians with a robust, efficient and comprehensive tool for the analysis of massive NGS datasets. Frameworks such as NEAT not only allow novice users to overcome the increasing number of technical hurdles due to the complexity of manipulating large datasets, but provide more advance users with tools that ensure high reproducibility standards in the NGS era. NEAT is publically available at https://github.com/pschorderet/NEAT. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0902-3) contains supplementary material, which is available to authorized users. Document type: Article

Abstract

ns4:p"Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at "ns4:ext-link xmlns:ns3="http://www.w3.org/1999/xlink" ext-link-type="uri" ns3:href="http://clusterflow.io""http://clusterflow.io"/ns4:ext-link"."/ns4 Document type: Article

Abstract

International audience; Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metage-nomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestima-tion, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described. Document type: Article