Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed collaboration that involves processing of data from several partners at different sites is rare. However, such distributed analyses are often very interesting, in particular, for scalability, security and privacy reasons. In this article, we motivate the need for real distributed biomedical analyses in the context of several ongoing projects, including the ICAN project that involves 34 French hospitals and affiliated research groups. We present a set of distributed architectures for such analyses that we have derived from discussions with different medical research groups and a study of related work. These architectures allow for scalability, security/privacy and reproducibility issues to be taken into account. Finally, we illustrate that these architectures can serve as the basis of a development method for biomedical distributed analyses.

Motivation: Reformatting and filtering bioinformatics files are common tasks for bioinformaticians. Standard Linux tools and specific programs are usually used to perform such tasks but there is still a gap between using these tools and the programming interface of some existing libraries. Results: In this study, we developed a set of tools namely bioalcidae, samjs and vcffilterjs that reformat or filter files using a JavaScript engine or a pure java expression and taking advantage of the java API for high-throughput sequencing data (htsjdk). Availability and implementation: https://github.com/lindenb/jvarkit. Contact: [email protected].

SUMMARY: We describe mod_bio, a set of modules for the Apache HTTP server that allows the users to access and query fastq, tabix, fasta and bam files through a Web browser. Those data are made available in plain text, HTML, XML, JSON and JSON-P. A javascript-based genome browser using the JSON-P communication technique is provided as an example of cross-domain Web service. AVAILABILITY AND IMPLEMENTATION: https://github.com/lindenb/mod_bio.

MOTIVATION: The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH-usually Cy5 and Cy3-can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries. RESULTS: In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner. CONCLUSIONS: We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).

SUMMARY: Analysing large amounts of data generated by next-generation sequencing (NGS) technologies is difficult for researchers or clinicians without computational skills. They are often compelled to delegate this task to computer biologists working with command line utilities. The availability of easy-to-use tools will become essential with the generalization of NGS in research and diagnosis. It will enable investigators to handle much more of the analysis. Here, we describe Knime4Bio, a set of custom nodes for the KNIME (The Konstanz Information Miner) interactive graphical workbench, for the interpretation of large biological datasets. We demonstrate that this tool can be utilized to quickly retrieve previously published scientific findings.