TransMeta simultaneously assembles multisample RNA-seq reads
- 1Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China;
- 2School of Mathematics, Shandong University, Jinan, Shandong 250100, China;
- 3School of Mathematical Science, Liaocheng University, Liaocheng 252000, China
-
↵4 These authors contributed equally to this work.
Abstract
Assembling RNA-seq reads into full-length transcripts is crucial in transcriptomic studies and poses computational challenges. Here we present TransMeta, a simple and robust algorithm that simultaneously assembles RNA-seq reads from multiple samples. TransMeta is designed based on the newly introduced vector-weighted splicing graph model, which enables accurate reconstruction of the consensus transcriptome via incorporating a cosine similarity–based combing strategy and a newly designed label-setting path-searching strategy. Tests on both simulated and real data sets show that TransMeta consistently outperforms PsiCLASS, StringTie2 plus its merge mode, and Scallop plus TACO, the most popular tools, in terms of precision and recall under a wide range of coverage thresholds at the meta-assembly level. Additionally, TransMeta consistently shows superior performance at the individual sample level.
Footnotes
-
[Supplemental material is available for this article.]
-
Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.276434.121.
-
Freely available online through the Genome Research Open Access option.
- Received November 24, 2021.
- Accepted June 3, 2022.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
