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Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction

Overview of attention for article published in BMC Bioinformatics, November 2016
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Title
Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1262-8
Pubmed ID
Authors

Ashok Rajaraman, Jian Ma

Abstract

Reconstructing ancestral gene orders in the presence of duplications is important for a better understanding of genome evolution. Current methods for ancestral reconstruction are limited by either computational constraints or the availability of reliable gene trees, and often ignore duplications altogether. Recently, methods that consider duplications in ancestral reconstructions have been developed, but the quality of reconstruction, counted as the number of contiguous ancestral regions found, decreases rapidly with the number of duplicated genes, complicating the application of such approaches to mammalian genomes. However, such high fragmentation is not encountered when reconstructing mammalian genomes at the synteny-block level, although the relative positions of genes in such reconstruction cannot be recovered. We propose a new heuristic method, MULTIRES, to reconstruct ancestral gene orders with duplications guided by homologous synteny blocks for a set of related descendant genomes. The method uses a synteny-level reconstruction to break the gene-order problem into several subproblems, which are then combined in order to disambiguate duplicated genes. We applied this method to both simulated and real data. Our results showed that MULTIRES outperforms other methods in terms of gene content, gene adjacency, and common interval recovery. This work demonstrates that the inclusion of synteny-level information can help us obtain better gene-level reconstructions. Our algorithm provides a basic toolbox for reconstructing ancestral gene orders with duplications. The source code of MULTIRES is available on https://github.com/ma-compbio/MultiRes .

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Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 54%
Student > Bachelor 2 15%
Researcher 1 8%
Unknown 3 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 31%
Agricultural and Biological Sciences 4 31%
Computer Science 2 15%
Unknown 3 23%