DCAlign v1.0:使用协同进化模型和知情先验对生物序列进行对齐。

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad537

Anna Paola Muntoni, Andrea Pagnani

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引用次数: 0

摘要

摘要:DCAlign是一种新的比对方法，能够处理同源序列多序列比对列的保守性和协同进化信号。然而，对齐候选序列所需的预处理步骤在计算上要求很高。我们在v1.0中展示了如何通过在反映插入和删除存在的一组信息变量上包含经验先验来显著减少总体计算时间。可用性和实现:DCAlign v1.0是在Julia中实现的，可以在https://github.com/infernet-h2020/DCAlign上完全获得。

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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DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors.

Summary: DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We show in v1.0 how to dramatically reduce the overall computing time by including an empirical prior over an informative set of variables mirroring the presence of insertions and deletions.

Availability and implementation: DCAlign v1.0 is implemented in Julia and it is fully available at https://github.com/infernet-h2020/DCAlign.

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.