种间mRNA-Seq实验的同源聚类差异表达分析

IF 0.9 4区 数学 Q3 Mathematics
J. Gelfond, J. Ibrahim, Ming-Hui Chen, Wei Sun, Kaitlyn N. Lewis, Sean Kinahan, Matthew A. Hibbs, R. Buffenstein
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引用次数: 2

摘要

对具有特殊性状的多种物种转录组的研究需求日益增加,如裸鼹鼠(NMR)。核磁共振的非凡之处在于它的寿命和抗癌性。了解赋予这些性状的分子机制具有科学意义,比较物种的rna测序实验可以将转录组动力学与这些表型联系起来。比较转录组差异需要一个物种中每个转录本与另一个物种中转录本的同源性映射。这样的映射是必要的,特别是如果一个物种没有很好的注释基因组可用。目前这种类型的分析方法通常为每个转录本确定最佳匹配,但是最佳匹配分析忽略了当存在多个具有相似同源性分数的候选转录本时不匹配的固有风险。我们提出了一种方法,将来自新物种的同系物集作为参考物种中单个基因对应的集群,并将基于集群的方法与传统的最佳匹配分析方法进行了比较,包括模拟数据和核磁共振和小鼠组织的案例研究。我们证明了基于聚类的方法在检测差异表达方面具有优越的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
Abstract There is an increasing demand for exploration of the transcriptomes of multiple species with extraordinary traits such as the naked-mole rat (NMR). The NMR is remarkable because of its longevity and resistance to developing cancer. It is of scientific interest to understand the molecular mechanisms that impart these traits, and RNA-sequencing experiments with comparator species can correlate transcriptome dynamics with these phenotypes. Comparing transcriptome differences requires a homology mapping of each transcript in one species to transcript(s) within the other. Such mappings are necessary, especially if one species does not have well-annotated genome available. Current approaches for this type of analysis typically identify the best match for each transcript, but the best match analysis ignores the inherent risks of mismatch when there are multiple candidate transcripts with similar homology scores. We present a method that treats the set of homologs from a novel species as a cluster corresponding to a single gene in the reference species, and we compare the cluster-based approach to a conventional best-match analysis in both simulated data and a case study with NMR and mouse tissues. We demonstrate that the cluster-based approach has superior power to detect differential expression.
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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