Natural Parameter Values for Generalized Gene Adjacency
Author(s) -
Zhenyu Yang,
David Sankoff
Publication year - 2009
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/978-3-642-04744-2_2
Subject(s) - genome , adjacency list , probabilistic logic , computer science , class (philosophy) , value (mathematics) , gene , computational biology , theoretical computer science , biology , algorithm , genetics , artificial intelligence , machine learning
Given the gene orders in two modern genomes, it may be difficult to decide if some genes are close enough in both genomes to infer some ancestral proximity or some functional relationship. Current methods all depend on arbitrary parameters. We explore a two-parameter class of gene proximity criteria, and find natural values for these parameters. One has to do with the parameter value where the expected information contained in two genomes about each other is maximized. The other has to do with parameter values beyond which all genes are clustered. We analyse these using combinatorial and probabilistic arguments as well as simulations.
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