NDLI: Fleximer: Accurate Quantification of RNA-Seq via Variable-Length k-mers

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Fleximer: Accurate Quantification of RNA-Seq via Variable-Length k-mers

Content Provider	ACM Digital Library
Author	Ju, Chelsea J.-T. Li, Ruirui Yang, Zhao Wang, Wei Jiang, Jyun-Yu Wu, Zhengliang
Abstract	The advent of RNA-Seq has made it possible to quantify transcript expression on a large scale simultaneously. This technology generates small fragments of each transcript sequence, known as sequencing reads. As the first step of data analysis towards expression quantification, most of the existing methods align these reads to a reference genome or transcriptome to establish their origins. However, read alignment is computationally costly. Recently, a series of methods have been proposed to perform a lightweight quantification analysis in an alignment-free manner. These methods utilize the notion of k-mers, which are short consecutive sequences representing the signatures of each transcript, to estimate the relative abundance from RNA-Seq reads. Current k-mer based approaches make use of a set of fixed size k-mers; however, the true signatures of each transcript may not exist in a fixed size. In this paper, we demonstrate the importance of k-mers selection in transcript abundance estimation. We propose a novel method, Fleximer, to efficiently discover and select an optimal set of k-mers with flexible lengths. Using both simulated and real datasets, we show that, with fewer k-mers, Fleximer is able to cover the similar amount of reads as Sailfish and Kallisto. The selected k-mers own more distinguishing features, and thus substantially reduce the errors in transcript abundance estimation.
Starting Page	263
Ending Page	272
Page Count	10
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107444
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Suffix tree Aho-corasick algorithm Rna-seq Em algorithm Transcript quantification
Content Type	Text
Resource Type	Article

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