TY  - JOUR
A1  - Yu, Ting
A1  - Zhao, Xiaoyu
A1  - Li, Guojun
T1  - TransMeta simultaneously assembles multisample RNA-seq reads
Y1  - 2022/07/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1398 
EP  - 1407 
DO  - 10.1101/gr.276434.121 
VL  - 32 
IS  - 7 
UR  - http://genome.cshlp.org/content/32/7/1398.abstract 
N2  - 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. 
ER  -