Publications - Published papers

Please find below publications of our group. Currently, we list 563 papers. Some of the publications are in collaboration with the group of Sonja Prohaska and are also listed in the publication list for her individual group. Access to published papers (access) is restricted to our local network and chosen collaborators. If you have problems accessing electronic information, please let us know:

©NOTICE: All papers are copyrighted by the authors; If you would like to use all or a portion of any paper, please contact the author.

Ryuto: network-flow based transcriptome reconstruction

Gatter, Thomas and Stadler, Peter F.


PREPRINT 19-013:
[ Publishers's page ]  paperID

Status: Published

BMC Bioinformatics 20: 190


Background The rapid increase in High-throughput sequencing of RNA (RNA-seq) has led to tremendous improvements in the detection and reconstruction of both expressed coding and non-coding RNA transcripts. Yet, the complete and accurate annotation of the complex transcriptional output of not only the human genome has remained elusive. One of the critical bottlenecks in this endeavor is the computational reconstruction of transcript structures, due to high noise levels, technological limits, and other biases in the raw data. Results We introduce several new and improved algorithms in a novel workflow for transcript assembly and quantification. We propose an extension of the common splice graph framework that combines aspects of overlap and bin graphs and makes it possible to efficiently use both multi-splice and paired-end information to the fullest extent. Phasing information of reads is used to further resolve loci. The decomposition of read coverage patterns is modeled as a minimum-cost flow problem to account for the unavoidable non-uniformities of RNA-seq data. Conclusion Its performance compares favorably with state of the art methods on both simulated and real-life datasets. Ryūtō calls 1−4% more true transcripts, while calling 5−35% less false predictions compared to the next best competitor.