The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes. Studying them under understanding of the underlying mechanisms of gene expression and processing. An important prerequisite is to be able to accurately determine the full complement of RNA transcripts and to infer their abundance various limitations and biases in next-generation sequencing (NGS) technologies.

We present a Machine Learning powered platform for quantitatively analyzing RNA-seq experiments, called oqtans. Oqtans is integrated in the easy-to-use Galaxy framework [1] and builds on NGS analysis methods developed in the Rätsch Laboratory and by others, which can be freely combined.

[A] General RNAseq workflow. [B, C] Example workflows using different tools.

Oqtans also allows for tools to be compared against each other.

Some of our own tools integrated into oqtans:

• PALMapper [2] 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 [3] is a machine learning-based transcript reconstruction method, which exploits features derived from RNA-seq read alignments and from computational splice sites predictions to infer the exon-intron structure of the corresponding transcripts.
• rQuant [4] is a method based on quadratic programming that simultaneously estimates biases inherent in library preparation, sequencing, and read mapping and accurately determines the abundances of given transcripts.
• rDiff [5] [6] 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.

An overview over all tools can be found on the Oqtans Tools Page. The machine-learning tools above show very accurate results and perform better or on par with the state-of-the-art for short read alignments, transcript identification and quantification as well as differential expression analysis. Their combination into a powerful workflow integrated in the Galaxy framework makes it possible to easily and effectively conduct a complete quantitative RNA-Seq analysis.

It can be accessed on our server, locally or in the cloud:

http://galaxy.cbio.mskcc.org (public compute server)

http://cloud.oqtans.org (online demo instance)

cm-ba5c56b95144e564f70e5762dc5fa177/shared/2013-11-07--22-16/ (share-an-instance-cluster for Galaxy CloudMan)

Our programs are free, open source and standalone, and can be downloaded from their respective Supplement page (links to the left).