Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data
© Schultheiss et al; licensee BioMed Central Ltd. 2011
Published: 21 November 2011
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  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 . 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 . 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 . 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.
- Goecks J, Nekrutenko A, Taylor J: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology 2010, 11(8):R86. 10.1186/gb-2010-11-8-r86PubMed CentralView ArticlePubMedGoogle Scholar
- Schultheiss SJ, Münch MC, Andreeva GD, Rätsch G: Persistence and Availability of Web Services in Computational Biology. PLoS computational biology 2011, 6(9):e24914.Google Scholar
- Jean G, Kahles A, Sreedharan VT, De Bona F, Ratsch G: RNA-Seq read alignments with PALMapper. In Current protocols in bioinformatics Edited by: Andreas D Baxevanis [et al]. 2010. Chapter 11:Unit 11 16 Chapter 11:Unit 11 16Google Scholar
- Bohnert R, Ratsch G: rQuant.web: a tool for RNA-Seq-based transcript quantitation. Nucleic acids research 2010, 38(Web Server):W348–351. 10.1093/nar/gkq448PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25(9):1105–1111. 10.1093/bioinformatics/btp120PubMed CentralView ArticlePubMedGoogle Scholar
- Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L: Improving RNA-Seq expression estimates by correcting for fragment bias. Genome biology 2011, 12(3):R22. 10.1186/gb-2011-12-3-r22PubMed CentralView ArticlePubMedGoogle Scholar
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