GIIRA--RNA-Seq driven gene finding incorporating ambiguous reads

• Published in: Bioinformatics 2014-03, Vol.30 (5), p.606-613
• Main Authors: Zickmann, Franziska, Lindner, Martin S, Renard, Bernhard Y
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Escherichia coli - genetics; Gene Expression Profiling - methods; Genes; Genomics; Humans; Saccharomyces cerevisiae - genetics; Sequence Alignment; Sequence Analysis, RNA - methods
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btt577

The reliable identification of genes is a major challenge in genome research, as further analysis depends on the correctness of this initial step. With high-throughput RNA-Seq data reflecting currently expressed genes, a particularly meaningful source of information has become commonly available for gene finding. However, practical application in automated gene identification is still not the standard case. A particular challenge in including RNA-Seq data is the difficult handling of ambiguously mapped reads. We present GIIRA (Gene Identification Incorporating RNA-Seq data and Ambiguous reads), a novel prokaryotic and eukaryotic gene finder that is exclusively based on a RNA-Seq mapping and inherently includes ambiguously mapped reads. GIIRA extracts candidate regions supported by a sufficient number of mappings and reassigns ambiguous reads to their most likely origin using a maximum-flow approach. This avoids the exclusion of genes that are predominantly supported by ambiguous mappings. Evaluation on simulated and real data and comparison with existing methods incorporating RNA-Seq information highlight the accuracy of GIIRA in identifying the expressed genes. GIIRA is implemented in Java and is available from https://sourceforge.net/projects/giira/.