Literature DB >> 12935338

Tests for gene clustering.

Dannie Durand1, David Sankoff.   

Abstract

Comparing chromosomal gene order in two or more related species is an important approach to studying the forces that guide genome organization and evolution. Linked clusters of similar genes found in related genomes are often used to support arguments of evolutionary relatedness or functional selection. However, as the gene order and the gene complement of sister genomes diverge progressively due to large scale rearrangements, horizontal gene transfer, gene duplication and gene loss, it becomes increasingly difficult to determine whether observed similarities in local genomic structure are indeed remnants of common ancestral gene order, or are merely coincidences. A rigorous comparative genomics requires principled methods for distinguishing chance commonalities, within or between genomes, from genuine historical or functional relationships. In this paper, we construct tests for significant groupings against null hypotheses of random gene order, taking incomplete clusters, multiple genomes, and gene families into account. We consider both the significance of individual clusters of prespecified genes and the overall degree of clustering in whole genomes.

Mesh:

Year:  2003        PMID: 12935338     DOI: 10.1089/10665270360688129

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  17 in total

1.  Statistical evidence for a more than 800-million-year-old evolutionarily conserved genomic region in our genome.

Authors:  Etienne G J Danchin; Pierre Pontarotti
Journal:  J Mol Evol       Date:  2004-11       Impact factor: 2.395

2.  Identification of genomic features using microsyntenies of domains: domain teams.

Authors:  Sophie Pasek; Anne Bergeron; Jean-Loup Risler; Alexandra Louis; Emmanuelle Ollivier; Mathieu Raffinot
Journal:  Genome Res       Date:  2005-05-17       Impact factor: 9.043

3.  Thermus thermophilus bacteriophage phiYS40 genome and proteomic characterization of virions.

Authors:  Tatyana Naryshkina; Jing Liu; Laurence Florens; Selene K Swanson; Andrey R Pavlov; Nadejda V Pavlova; Ross Inman; Leonid Minakhin; Sergei A Kozyavkin; Michael Washburn; Arcady Mushegian; Konstantin Severinov
Journal:  J Mol Biol       Date:  2006-09-06       Impact factor: 5.469

4.  Many nonuniversal archaeal ribosomal proteins are found in conserved gene clusters.

Authors:  Jiachen Wang; Indrani Dasgupta; George E Fox
Journal:  Archaea       Date:  2009-04-28       Impact factor: 3.273

5.  Statistics for approximate gene clusters.

Authors:  Katharina Jahn; Sascha Winter; Jens Stoye; Sebastian Böcker
Journal:  BMC Bioinformatics       Date:  2013-12-13       Impact factor: 3.169

6.  Identifying metabolic enzymes with multiple types of association evidence.

Authors:  Peter Kharchenko; Lifeng Chen; Yoav Freund; Dennis Vitkup; George M Church
Journal:  BMC Bioinformatics       Date:  2006-03-29       Impact factor: 3.169

7.  WordCluster: detecting clusters of DNA words and genomic elements.

Authors:  Michael Hackenberg; Pedro Carpena; Pedro Bernaola-Galván; Guillermo Barturen; Angel M Alganza; José L Oliver
Journal:  Algorithms Mol Biol       Date:  2011-01-24       Impact factor: 1.405

8.  Bidirectional best hit r-window gene clusters.

Authors:  Melvin Zhang; Hon Wai Leong
Journal:  BMC Bioinformatics       Date:  2010-01-18       Impact factor: 3.169

9.  i-ADHoRe 3.0--fast and sensitive detection of genomic homology in extremely large data sets.

Authors:  Sebastian Proost; Jan Fostier; Dieter De Witte; Bart Dhoedt; Piet Demeester; Yves Van de Peer; Klaas Vandepoele
Journal:  Nucleic Acids Res       Date:  2011-11-18       Impact factor: 16.971

10.  Gene cluster statistics with gene families.

Authors:  Narayanan Raghupathy; Dannie Durand
Journal:  Mol Biol Evol       Date:  2009-01-15       Impact factor: 16.240

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