Zusammenfassung
Abstract Motivation Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors. Results We present an extension to Ry\=ut\=o enabling the reconstruction of consensus transcriptomes from multiple RNA-seq datasets, incorporating consensus calling at low level features. We report stable improvements already at three replicates. Ry\=ut\=o outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ry\=ut\=o's unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis. Ry\=ut\=o consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ry\=ut\=o is especially effective at high precision assembly, while Ry\=ut\=o's conventional mode can reach higher recall. Availability and implementation Ry\=ut\=o is available at https://github.com/studla/RYUTO. Supplementary information Supplementary data are available at Bioinformatics online.
Nutzer
