We present Oqtans, an open-source workbench for quantitative transcriptome analysis, that is integrated in Galaxy. Its distinguishing features include customizable computational workflows and a modular pipeline architecture that facilitates comparative assessment of tool and data quality. Oqtans integrates an assortment of machine learning-powered tools into Galaxy, which show superior or equal performance to state-of-the-art tools. Implemented tools comprise a complete transcriptome analysis workflow: short-read alignment, transcript identification/quantification and differential expression analysis. Oqtans and Galaxy facilitate persistent storage, data exchange and documentation of intermediate results and analysis workflows. We illustrate how Oqtans aids the interpretation of data from different experiments in easy to understand use cases. Users can easily create their own workflows and extend Oqtans by integrating specific tools. Oqtans is available as (i) a cloud machine image with a demo instance at cloud.oqtans.org, (ii) a public Galaxy instance at galaxy.cbio.mskcc.org, (iii) a git repository containing all installed software (oqtans.org/git); most of which is also available from (iv) the Galaxy Toolshed and (v) a share string to use along with Galaxy CloudMan.

The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes via deep RNA Sequencing (RNA-Seq). In considering the full complement of RNA transcripts that comprise the transcriptome, two important analytical questions emerge: what is the abundance of RNA transcripts and which genes or transcripts are differentially expressed. In parallel with developing sequencing technologies, data analysis software is also constantly updated to improve accuracy and sensitivity while minimizing run times. The abundance of software programs, however, can be prohibitive and confusing for researchers evaluating RNA-Seq analysis pipelines. We present an open-source workbench, Oqtans , that can be integrated into the Galaxy framework that enables researchers to set up a computational pipeline for quantitative transcriptome analysis. Its distinguishing features include a modular pipeline architecture, which facilitates comparative assessment of tool and data quality. Within Oqtans , the Galaxy ’s workflow architecture enables direct comparison of several tools. Furthermore, it is straightforward to compare the performance of different programs and parameter settings on the same data and choose the best suited for the task. Oqtans analysis pipelines are easy to set up, modify, and (re-)use without significant computational skill. Oqtans integrates more than twenty sophisticated tools that perform very well compared to the state-of-the-art for transcript identification, quantification and differential expression analysis. The toolsuite contains several tools developed in the Rätsch Laboratory, but the majority of the tools were developed by other groups. In particular, we provide tools for read alignment (bwa, STAR, TopHat, PALMapper, …), transcript prediction (cufflinks, Trinity, Scripture, …) and quantitative analyses (DESeq2, edgeR, rDiff, rQuant, …). In addition, we provide tools for alignment filtering (RNA-geeq toolbox), GFF file processing (GFF toolbox) and tools for predictive sequence analysis (EasySVM, ASP, ARTS, …). See http://oqtans.org/tools for more details on included tools. Oqtans is integrated into the publicly available Galaxy server http://galaxy.cbio.mskcc.org which is maintained by the Rätsch Laboratory. It is also available as source code in a public GitHub repository http://bioweb.me/oqtans/git and as a machine image (managed by Galaxy CloudMan) for the Amazon Web Service cloud environment (instructions available at http://oqtans.org ). Oqtans sets a new standard in terms of reproducibility and builds upon Galaxy’s features to facilitate persistent storage, exchange and documentation of intermediate results and analysis workflows. Support: [email protected] Contact: [email protected] Details: http://oqtans.org Public Computing Server: http://galaxy.cbio.mskcc.org oqtans Demo Server: http://cloud.oqtans.org oqtans Amazon Machine Image: ami-65376a0c License: GPL http://www.gnu.org/licenses/gpl.html

What is more inspiring than a discussion with the leading scientists in your field? As a student or a young researcher, you have likely been influenced by mentors guiding you in your career and leading you to your current position. Any discussion with or advice from an expert is certainly very helpful for young people. But how often do we have the opportunity to meet experts? Do we make the most out of these situations? Meetings organized for young scientists are a great opportunity not only for the attendees: they are an opportunity for experts to meet bright students and learn from them in return. In this article, we introduce several successful events organized by Regional Student Groups all around the world, bridging the gap between experts and young scientists. We highlight how rewarding it is for all participants: young researchers, experts, and organizers. We then discuss the various benefits and emphasize the importance of organizing and attending such meetings. As a young researcher, seeking mentorship and additional skills training is a crucial step in career development. Keep in mind that one day, you may be an inspiring mentor, too.

Exchanging ideas with like-minded, enthusiastic people interested in the same topic is crucial for the advancement of a scientist's career. Several Regional Student Groups (RSGs) of the International Society for Computational Biology (ISCB) Student Council have cooperated in the last six years to organize scientific workshops and conferences. With motivated students, it is possible to create a memorable event for fellow scientists; in doing so, the organizers gain valuable experiences. While collaborating across borders and time zones can be difficult, feedback from event organizers was always positive. When limited resources are juxtaposed with great ideas and a network of contacts, the outcome is always an amazing experience, despite organizers being separated geographically across different countries.

The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes via RNA-Sequencing. We have developed the first integrative online platform, oqtans, for quantitatively analyzing RNA-Seq experiments. Our approach of providing a self-contained machine image with the accessible, transparent Galaxy framework [ 1 ] minimizes the risk of using a third-party web service for data analysis. These services often disappear a few years after publication and render results irreproducible [ 2 ]. With oqtans, bioinformatics becomes reproducible by providing analysis building blocks for a customized workflow of read mapping, transcript reconstruction and quantitation as well as differential expression analysis. Oqtans includes a comprehensive machine-learning-powered toolsuite developed by the authors for NGS data analysis. PALMapper is a short-read mapper which efficiently computes both unspliced and spliced alignments at high accuracy by taking advantage of base quality information and computational splice site predictions [ 3 ]. mTIM is a transcript reconstruction method, which exploits features derived from RNA-seq read alignments and from computational splice site predictions to infer the exon-intron structure of the corresponding transcripts. rQuant is based on quadratic programming. It simultaneously estimates biases inherent in library preparation, sequencing, and read mapping, and accurately determines the abundances of given transcripts [ 4 ]. rDiff is a set of statistical test techniques that determine significant differences between two RNA-seq experiments to find differentially expressed regions with or without knowledge of transcripts. We compare predictions to the published annotation at the intron and transcript levels. The performance of read aligners is shown in Fig. 1A from D. melanogaster data , and transcript segmentation tools in Fig. 1B, on C. elegans . Our tools, PALMapper and mTIM, outperform TopHat [ 5 ] and Cufflinks [ 6 ]. Oqtans is available free and open-source, from http://oqtans.org as a virtual machine for cloud computing environments, and ready to use on our public compute cluster at http://bioweb.me/mlb-galaxy . A) Accuracy (F-score) of intron predictions in 3-day-old adults of D. melanogaster with aligners PALMapper (green) and TopHat (blue). B) Accuracy of intron predictions with the same aligners and transcript predictions with mTIM (green) and Cufflinks (blue) on C. elegans RNA-seq transcriptome data.

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MOTIVATION: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. RESULTS: We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. AVAILABILITY: The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/.