Ryūtō: improved multi-sample transcript assembly for differential transcript expression analysis and more

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 Ryūtō enabling the reconstruction of consensus transcriptomes from multiple RNA-seq data sets, incorporating consensus calling at low level features. We report stable improvements already at 3 replicates. Ryūtō outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ryūtō’s unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis. Conclusion Ryūtō consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ryūtō is especially effective at high precision assembly, while Ryūtō’s conventional mode can reach higher recall. Availability Ryūtō is available at https://github.com/studla/RYUTO Supplementary information Supplementary data are available at Bioinformatics online.

Assembly of the 81.6 Mb centromere of pea chromosome 6 elucidates the structure and evolution of metapolycentric chromosomes

“…Next, the mapped data were processed by braker2 [ 53 ] to perform a de novo annotation of genes. To improve the quality of annotation, Ryūtō [ 54 ] was run twice on the mapping results, once for the stranded library and the second time for the unstranded library. The results of Ryūtō and psi-class were combined using Mikado [ 55 ] to obtain a high-quality (HQ) annotation dataset.…”

Assembly of the 81.6 Mb centromere of pea chromosome 6 elucidates the structure and evolution of metapolycentric chromosomes

“…Next, the mapped data were processed by braker2 (Brůna et al 2021) to perform a de novo annotation of genes. To improve the quality of annotation, Ryūtō (Gatter and Stadler 2021) was run twice on the mapping results, once for the stranded library and the second time for the unstranded library. The results of Ryūtō and psi-class were combined using Mikado (Venturini et al 2018) to obtain a high-quality (HQ) annotation dataset.…”

Common Features in lncRNA Annotation and Classification: A Survey

“…First, compared with raw genomic data, highquality RNA-seq data are not always readily available for less well studied organisms, are sometimes difficult to acquire (e.g., compared to DNA from museum samples), and in any case requiring additional wet lab work. Second, and maybe more importantly, transcript reconstruction even from high-coverage RNA-seq data sets is by far less than perfect (see, e.g., [85] for benchmark data), and tends to produce fragmented and incomplete transcripts in particular for low coverage-which is the norm for most lncRNAs. With annotation quality rising in well-known model organisms, these less well studied species are precisely the target for mostly computational annotation efforts.…”