![]() ![]() Massively parallel whole transcriptome sequencing, commonly referred to as RNA-Seq, and its ability to generate full transcriptome data at the single transcript level, provides a powerful tool with multiple interrelated applications, including transcriptome assembly, gene and transcript expression level estimation, also known as transcriptome quantification, studying trans- and cis-regulatory effects, studying parent-of-origin effects, and calling expressed variants. Experimental results suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM solution compared to existing state-of-the-art approaches. The MaLTA-IsoEM tool is publicly available at: ConclusionsĮxperimental results on both synthetic and real datasets show that Ion Torrent RNA-Seq data can be successfully used for transcriptome analyses. ![]() A new version of the IsoEM algorithm suitable for Ion Torrent RNA-Seq reads is used to accurately estimate transcript expression levels. Our approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. ![]() We present MaLTA, a method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. Another challenge in transcriptomic analysis comes from the ambiguities in mapping reads to transcripts. The sequences of novel transcripts can be reconstructed from deep RNA-Seq data, but this is computationally challenging due to sequencing errors, uneven coverage of expressed transcripts, and the need to distinguish between highly similar transcripts produced by alternative splicing. ![]() High throughput RNA sequencing (RNA-Seq) can generate whole transcriptome information at the single transcript level providing a powerful tool with multiple interrelated applications including transcriptome reconstruction and quantification. ![]()
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December 2022
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