Promoter prediction analysis on the whole human genome

Vladimir B. Bajic; Lam Tan Sin; Yutaka Suzuki; Sumio Sugano

doi:10.1038/nbt1032

Promoter prediction analysis on the whole human genome

Vladimir B. Bajic^*, Lam Tan Sin, Yutaka Suzuki, Sumio Sugano

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

127 Scopus citations

Abstract

Promoter prediction programs (PPPs) are important for in silico gene discovery without support from expressed sequence tag (EST)/cDNA/mRNA sequences, in the analysis of gene regulation and in genome annotation. Contrary to previous expectations, a comprehensive analysis of PPPs reveals that no program simultaneously achieves sensitivity and a positive predictive value >65%. PPP performances deduced from a limited number of chromosomes or smaller data sets do not hold when evaluated at the level of the whole genome, with serious inaccuracy of predictions for non-CpG-island-related promoters. Some PPPs even perform worse than, or close to, pure random guessing.

Original language	English (US)
Pages (from-to)	1467-1473
Number of pages	7
Journal	Nature biotechnology
Volume	22
Issue number	11
DOIs	https://doi.org/10.1038/nbt1032
State	Published - Nov 2004
Externally published	Yes

ASJC Scopus subject areas

Biotechnology
Bioengineering
Applied Microbiology and Biotechnology
Molecular Medicine
Biomedical Engineering

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title = "Promoter prediction analysis on the whole human genome",

abstract = "Promoter prediction programs (PPPs) are important for in silico gene discovery without support from expressed sequence tag (EST)/cDNA/mRNA sequences, in the analysis of gene regulation and in genome annotation. Contrary to previous expectations, a comprehensive analysis of PPPs reveals that no program simultaneously achieves sensitivity and a positive predictive value >65%. PPP performances deduced from a limited number of chromosomes or smaller data sets do not hold when evaluated at the level of the whole genome, with serious inaccuracy of predictions for non-CpG-island-related promoters. Some PPPs even perform worse than, or close to, pure random guessing.",

author = "Bajic, {Vladimir B.} and Sin, {Lam Tan} and Yutaka Suzuki and Sumio Sugano",

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AU - Sin, Lam Tan

AU - Suzuki, Yutaka

AU - Sugano, Sumio

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AB - Promoter prediction programs (PPPs) are important for in silico gene discovery without support from expressed sequence tag (EST)/cDNA/mRNA sequences, in the analysis of gene regulation and in genome annotation. Contrary to previous expectations, a comprehensive analysis of PPPs reveals that no program simultaneously achieves sensitivity and a positive predictive value >65%. PPP performances deduced from a limited number of chromosomes or smaller data sets do not hold when evaluated at the level of the whole genome, with serious inaccuracy of predictions for non-CpG-island-related promoters. Some PPPs even perform worse than, or close to, pure random guessing.

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ER -

Promoter prediction analysis on the whole human genome

Abstract

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