Alignment-Free Sequence Analysis and Applications.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jie Ren, Xin Bai, Yang Young Lu, Kujin Tang, Ying Wang, Gesine Reinert, Fengzhu Sun
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引用次数: 0

Abstract

Genome and metagenome comparisons based on large amounts of next generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been applied to many problems including the comparison of gene regulatory regions, genome sequences, metagenomes, binning contigs in metagenomic data, identification of virus-host interactions, and detection of horizontal gene transfers. We provide an updated review of these applications and other related developments of word-count based approaches for alignment-free sequence analysis.

Abstract Image

无配位序列分析及应用。
基于大量新一代测序(NGS)数据的基因组和元基因组比较对基于比对的方法提出了巨大挑战,因为数据量巨大,读数长度相对较短。基于 NGS 数据中字模式计数的无比对方法不依赖于完整的基因组,通常计算效率较高。因此,它们对基因组和元基因组比较有很大的帮助。最近,人们开发了新的统计方法来比较长序列和霰弹枪序列。这些方法已被应用于许多问题,包括基因调控区、基因组序列、元基因组的比较,元基因组数据中等位基因的分选,病毒-宿主相互作用的鉴定,以及水平基因转移的检测。我们将对这些应用以及基于字数的无比对序列分析方法的其他相关发展进行最新综述。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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