iPAC: a genome-guided assembler of isoforms via phasing and combing paths

TransMeta simultaneously assembles multisample RNA-seq reads

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.

Tiglon enables accurate transcriptome assembly via integrating mappings of different aligners

Full-length transcript reconstruction has a pivotal role in RNA-seq data analysis. In this research, we present a new genome-guided transcriptome assembly algorithm, namely Tiglon, which integrates multiple alignments of different mapping tools and builds the labeled splice graphs, followed by a label-based dynamic path-searching strategy to reconstruct the transcripts. We evaluate Tiglon on a simulated dataset and 12 real datasets under the Hisat2 and Star mappings. The results indicate that the integrating techniques of Tiglon exhibit great superiority over the state-of-the-art assemblers, including StringTie2 and Scallop, depending on Hisat2 alignments, Star alignments, or the merged alignments of both. Especially, Tiglon is significantly powerful in recovering lowly expressed transcripts.

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Tiglon is designed for integrating multiple alignments to assemble transcripts

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Integrating alignments of different aligners is helpful for transcriptome assembly

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Tiglon proposes a new graph model called the labeled splice graph

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Our experiments demonstrate that Tiglon outperforms the leading assemblers

TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective.