Similarity-based gene detection: using COGs to find evolutionarily-conserved ORFs

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  • Powell, Bradford C
    • Affiliation: School of Medicine, Curriculum in Genetics and Molecular Biology
  • Hutchison, Clyde A
    • Affiliation: School of Medicine, Department of Microbiology and Immunology
    • Other Affiliation: J. Craig Venter Institute, Rockville, Maryland, USA
Abstract
  • Abstract Background Experimental verification of gene products has not kept pace with the rapid growth of microbial sequence information. However, existing annotations of gene locations contain sufficient information to screen for probable errors. Furthermore, comparisons among genomes become more informative as more genomes are examined. We studied all open reading frames (ORFs) of at least 30 codons from the genomes of 27 sequenced bacterial strains. We grouped the potential peptide sequences encoded from the ORFs by forming Clusters of Orthologous Groups (COGs). We used this grouping in order to find homologous relationships that would not be distinguishable from noise when using simple BLAST searches. Although COG analysis was initially developed to group annotated genes, we applied it to the task of grouping anonymous DNA sequences that may encode proteins. Results "Mixed COGs" of ORFs (clusters in which some sequences correspond to annotated genes and some do not) are attractive targets when seeking errors of gene predicion. Examination of mixed COGs reveals some situations in which genes appear to have been missed in current annotations and a smaller number of regions that appear to have been annotated as gene loci erroneously. This technique can also be used to detect potential pseudogenes or sequencing errors. Our method uses an adjustable parameter for degree of conservation among the studied genomes (stringency). We detail results for one level of stringency at which we found 83 potential genes which had not previously been identified, 60 potential pseudogenes, and 7 sequences with existing gene annotations that are probably incorrect. Conclusion Systematic study of sequence conservation offers a way to improve existing annotations by identifying potentially homologous regions where the annotation of the presence or absence of a gene is inconsistent among genomes.
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  • Article
Rights statement
  • In Copyright
Rights holder
  • Bradford C Powell et al.; licensee BioMed Central Ltd.
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Journal title
  • BMC Bioinformatics
Journal volume
  • 7
Journal issue
  • 1
Page start
  • 31
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1471-2105
Bibliographic citation
  • BMC Bioinformatics. 2006 Jan 19;7(1):31
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • September 5, 2012

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file details 1471-2105-7-31.pdf 2019-05-06 Public Download
file details 1471-2105-7-31.xml 2019-05-06 Public Download
file details Source code archive for a Java program to browse the database of COGs discussed in this manuscript. In gzip-compressed tar format. 2019-05-06 Public Download
file details This Excel file contains the tabulated sensitivities, specificities, and positive predictive values for classification of open reading frames as genes or not genes, compared to existing annotations. 2019-05-06 Public Download
file details Source code archive for a C++ program to find high-stringency COGs given a set of bidirectional best-hit pairs. In gzip-compressed tar format. 2019-05-06 Public Download