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MapGL:通过系统发育最大简约法推断短基因组序列特征的进化增益和损失。

MapGL: inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony.

机构信息

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.

出版信息

BMC Bioinformatics. 2020 Sep 22;21(1):416. doi: 10.1186/s12859-020-03742-9.

Abstract

BACKGROUND

Comparative genomics studies are growing in number partly because of their unique ability to provide insight into shared and divergent biology between species. Of particular interest is the use of phylogenetic methods to infer the evolutionary history of cis-regulatory sequence features, which contribute strongly to phenotypic divergence and are frequently gained and lost in eutherian genomes. Understanding the mechanisms by which cis-regulatory element turnover generate emergent phenotypes is crucial to our understanding of adaptive evolution. Ancestral reconstruction methods can place species-specific cis-regulatory features in their evolutionary context, thus increasing our understanding of the process of regulatory sequence turnover. However, applying these methods to gain and loss of cis-regulatory features historically required complex workflows, preventing widespread adoption by the broad scientific community.

RESULTS

MapGL simplifies phylogenetic inference of the evolutionary history of short genomic sequence features by combining the necessary steps into a single piece of software with a simple set of inputs and outputs. We show that MapGL can reliably disambiguate the mechanisms underlying differential regulatory sequence content across a broad range of phylogenetic topologies and evolutionary distances. Thus, MapGL provides the necessary context to evaluate how genomic sequence gain and loss contribute to species-specific divergence.

CONCLUSIONS

MapGL makes phylogenetic inference of species-specific sequence gain and loss easy for both expert and non-expert users, making it a powerful tool for gaining novel insights into genome evolution.

摘要

背景

比较基因组学研究的数量不断增加,部分原因是它们具有独特的能力,可以深入了解物种之间共同和不同的生物学特性。特别有趣的是,利用系统发育方法推断顺式调控序列特征的进化历史,这些特征对表型分化有很大的贡献,并且在真兽类基因组中经常获得和丢失。了解顺式调控元件周转产生新表型的机制对于我们理解适应性进化至关重要。祖先重建方法可以将物种特异性的顺式调控特征置于其进化背景下,从而增加我们对调控序列周转过程的理解。然而,这些方法在过去应用于顺式调控特征的获得和丢失,需要复杂的工作流程,从而阻止了广泛的科学社区采用。

结果

MapGL 通过将必要的步骤组合到一个具有简单输入和输出的单一软件中,简化了短基因组序列特征进化历史的系统发育推断。我们表明,MapGL 可以可靠地区分在广泛的系统发育拓扑和进化距离下,差异调控序列内容的潜在机制。因此,MapGL 提供了必要的背景,以评估基因组序列的获得和丢失如何导致物种特异性的分化。

结论

MapGL 使得专家和非专家用户都能够轻松地进行物种特异性序列获得和丢失的系统发育推断,使其成为深入了解基因组进化的有力工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/280d/7510305/1b4e4037498e/12859_2020_3742_Fig1_HTML.jpg

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