来自测序数据的无参考系统发育。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Petr Ryšavý, Filip Železný
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引用次数: 0

摘要

动机:基因序列聚类是生物信息学分析的关键部分之一。由此产生的系统发育树有利于解决许多研究问题,包括追踪物种的历史,研究过去的迁移,或追踪病毒爆发的来源。与此同时,生物学家以原始形式的reads或仅在contig级组装上提供更多数据。因此,需要开发能够在没有监督的情况下处理这些数据的工具。结果:在本文中,我们提出了一个无参考系统发育的工具,能够处理没有成熟级别组装可用的数据。该工具允许对原始读取、配置和后者的组合进行距离计算。该工具提供序列之间Levenshtein距离的估计,从而估计生物体之间的突变数量。与以往的研究相比,该方法的新颖之处在于将读取和配置测度结合起来,采用了一种新的读取-配置映射方法,并实现了配置的高效嵌入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reference-free phylogeny from sequencing data.

Reference-free phylogeny from sequencing data.

Reference-free phylogeny from sequencing data.

Reference-free phylogeny from sequencing data.

Motivation: Clustering of genetic sequences is one of the key parts of bioinformatics analyses. Resulting phylogenetic trees are beneficial for solving many research questions, including tracing the history of species, studying migration in the past, or tracing a source of a virus outbreak. At the same time, biologists provide more data in the raw form of reads or only on contig-level assembly. Therefore, tools that are able to process those data without supervision need to be developed.

Results: In this paper, we present a tool for reference-free phylogeny capable of handling data where no mature-level assembly is available. The tool allows distance calculation for raw reads, contigs, and the combination of the latter. The tool provides an estimation of the Levenshtein distance between the sequences, which in turn estimates the number of mutations between the organisms. Compared to the previous research, the novelty of the method lies in a newly proposed combination of the read and contig measures, a new method for read-contig mapping, and an efficient embedding of contigs.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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