Towards the identification of essential genes using targeted genome sequencing and comparative analysis.

Full Abstract

BACKGROUND:
The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value.

RESULTS:
We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature.

CONCLUSION:
Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability.

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Author information

Author/s: Gustafson, Adam M (AM); Snitkin, Evan S (ES); Parker, Stephen C J (SC); DeLisi, Charles (C); Kasif, Simon (S);

Affiliation: Bioinformatics Graduate Program, Boston University, Boston, MA 02215 USA. gustafad(-atsign-)bu.edu

Grants: 1P20GM066401 (Agency:NIGMS NIH HHS) ; 1T32GM070409 (Agency:NIGMS NIH HHS) ; R01 HG003367-01A1 (Agency:NHGRI NIH HHS)

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.

Journal: BMC genomics (BMC Genomics), published in England. (Language: eng)

Reference: 2006-; vol 7 (issue ) : pp 265

Dates: Created 2006/10/26; Completed 2006/11/20; Revised 2008/11/20;

PMID: 17052348, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Animals
DNA, Bacterial - genetics
DNA, Fungal - genetics
Escherichia coli - cytology; genetics
Escherichia coli Proteins - chemistry; genetics
Evolution, Molecular
Genes, Bacterial
Genes, Fungal
Genes, Plant
Genomics - methods

Genomics - methods
Humans
Invertebrates - genetics
Plants - genetics
ROC Curve
Saccharomyces cerevisiae - cytology; genetics
Saccharomyces cerevisiae Proteins - chemistry; genetics
Sequence Analysis, DNA
Sequence Homology, Nucleic Acid
Vertebrates - genetics

Associated Chemicals: DNA, Bacterial (0) ; DNA, Fungal (0) ; Escherichia coli Proteins (0) ; Saccharomyces cerevisiae Proteins (0)

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