MapGL: inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony
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MapGL: inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony Adam G. Diehl1*
and Alan P. Boyle1,2
* Correspondence: adadiehl@umich. edu 1 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA Full list of author information is available at the end of the article
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. Keywords: Phylogenetic analysis, Genomics, Genome evolution, Regulatory evolution, Genomic algorithms, Ancestral reconstruction
Background Comparative genomics uses sequence-level differences between species to gain insights into how genomes function and evolve [1]. According to Google Scholar, published comparative genomics studies have increased every year since 2009. These studies rely on the ability to detect and assign provenance to lineage-specific sequence variations © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless
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