Creating and Using Minimizer Sketches in Computational Genomics.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Journal of Computational Biology Pub Date : 2023-12-01 Epub Date: 2023-08-30 DOI:10.1089/cmb.2023.0094
Hongyu Zheng, Guillaume Marçais, Carl Kingsford
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引用次数: 0

Abstract

Processing large data sets has become an essential part of computational genomics. Greatly increased availability of sequence data from multiple sources has fueled breakthroughs in genomics and related fields but has led to computational challenges processing large sequencing experiments. The minimizer sketch is a popular method for sequence sketching that underlies core steps in computational genomics such as read mapping, sequence assembling, k-mer counting, and more. In most applications, minimizer sketches are constructed using one of few classical approaches. More recently, efforts have been put into building minimizer sketches with desirable properties compared with the classical constructions. In this survey, we review the history of the minimizer sketch, the theories developed around the concept, and the plethora of applications taking advantage of such sketches. We aim to provide the readers a comprehensive picture of the research landscape involving minimizer sketches, in anticipation of better fusion of theory and application in the future.

在计算基因组学中创建和使用最小化草图
处理大型数据集已成为计算基因组学的重要组成部分。来自多个来源的序列数据的可获得性大大增加,推动了基因组学及相关领域的突破,但也带来了处理大型测序实验的计算挑战。最小化草图是一种常用的序列草图绘制方法,是计算基因组学核心步骤的基础,如读图映射、序列组装、k-mer 计数等。在大多数应用中,最小化草图都是用几种经典方法之一构建的。最近,人们开始努力构建最小化草图,与经典构造相比,最小化草图具有理想的特性。在本研究中,我们将回顾最小化草图的历史、围绕最小化草图概念开发的理论以及大量利用最小化草图的应用。我们的目标是为读者提供一幅涉及最小化草图的研究全景图,以期待未来理论与应用的更好融合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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