k-mer-based Upstream Preprocessing of long reads for Isoform Discovery.

IF 6.3 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molly Borowiak, Yun William Yu
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引用次数: 0

Abstract

Eukaryotic genes can encode multiple protein isoforms based on alternative splicing of their transcribed regions. Most modern novel isoform discovery methods function by identifying and assembling exon splice junctions from an RNA-seq sample. However, splice junctions can only be accurately annotated with time-intensive dynamic programming alignment. This manuscript introduces KuPID, a method for preprocessing long RNA-seq reads with the goal of better identifying novel isoform transcripts. KuPID utilizes k-mer sketching as a prefilter to quickly pseudo-align reads to known reference isoforms. Full alignment need only then be applied to reads that are most relevant to isoform discovery. Not only does KuPID speed up the discovery pipeline, it also increases downstream accuracy by filtering out extraneous reads. KuPID preprocessing simultaneously increases the f1 accuracy of isoform discovery pipelines by up to 11.6 points while decreasing the runtime by a factor of 2-3×;. An optional mode permits a KuPID sample to be paired with both isoform discovery and transcript quantification.

基于k-mer的长读段上游预处理的异构体发现。
真核生物基因可以编码基于其转录区域的选择性剪接的多种蛋白质亚型。大多数现代新型异构体发现方法都是通过从RNA-seq样本中识别和组装外显子剪接来起作用的。然而,剪接结点只能通过时间密集的动态规划对齐来精确标注。本文介绍了KuPID,一种预处理长RNA-seq reads的方法,目的是更好地识别新的同工异构体转录本。KuPID利用k-mer草图作为预滤波器快速伪对齐读取到已知的参考异构体。全比对只需要应用于与同型异构体发现最相关的读取。KuPID不仅加快了发现流程,还通过过滤掉无关的读取来提高下游的准确性。KuPID预处理同时将异型发现管道的f1精度提高了11.6点,同时将运行时间缩短了2-3倍;可选模式允许KuPID样本与异构体发现和转录本定量配对。
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来源期刊
Genome research
Genome research 生物-生化与分子生物学
CiteScore
12.40
自引率
1.40%
发文量
140
审稿时长
6 months
期刊介绍: Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine. Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.
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