Title |
Screening synteny blocks in pairwise genome comparisons through integer programming
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Published in |
BMC Bioinformatics, April 2011
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DOI | 10.1186/1471-2105-12-102 |
Pubmed ID | |
Authors |
Haibao Tang, Eric Lyons, Brent Pedersen, James C Schnable, Andrew H Paterson, Michael Freeling |
Abstract |
It is difficult to accurately interpret chromosomal correspondences such as true orthology and paralogy due to significant divergence of genomes from a common ancestor. Analyses are particularly problematic among lineages that have repeatedly experienced whole genome duplication (WGD) events. To compare multiple "subgenomes" derived from genome duplications, we need to relax the traditional requirements of "one-to-one" syntenic matchings of genomic regions in order to reflect "one-to-many" or more generally "many-to-many" matchings. However this relaxation may result in the identification of synteny blocks that are derived from ancient shared WGDs that are not of interest. For many downstream analyses, we need to eliminate weak, low scoring alignments from pairwise genome comparisons. Our goal is to objectively select subset of synteny blocks whose total scores are maximized while respecting the duplication history of the genomes in comparison. We call this "quota-based" screening of synteny blocks in order to appropriately fill a quota of syntenic relationships within one genome or between two genomes having WGD events. |
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Geographical breakdown
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 5 | 4% |
Germany | 2 | 2% |
Belgium | 2 | 2% |
Australia | 1 | <1% |
Brazil | 1 | <1% |
Sweden | 1 | <1% |
Chile | 1 | <1% |
Norway | 1 | <1% |
Taiwan | 1 | <1% |
Other | 2 | 2% |
Unknown | 103 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 29 | 24% |
Student > Ph. D. Student | 27 | 23% |
Student > Master | 15 | 13% |
Student > Bachelor | 9 | 8% |
Other | 6 | 5% |
Other | 20 | 17% |
Unknown | 14 | 12% |
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Biochemistry, Genetics and Molecular Biology | 16 | 13% |
Computer Science | 2 | 2% |
Mathematics | 1 | <1% |
Nursing and Health Professions | 1 | <1% |
Other | 4 | 3% |
Unknown | 20 | 17% |