Sequence alignment kernel for recognition of promoter regions

Leo Gordon; Alexey Ya Chervonenkis; Alex J. Gammerman; Ilham A. Shahmuradov; Victor V. Solovyev

doi:10.1093/bioinformatics/btg265

Sequence alignment kernel for recognition of promoter regions

Leo Gordon^*, Alexey Ya Chervonenkis, Alex J. Gammerman, Ilham A. Shahmuradov, Victor V. Solovyev

^*Corresponding author for this work

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

109 Scopus citations

Abstract

In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ⁷⁰-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

Original language	English (US)
Pages (from-to)	1964-1971
Number of pages	8
Journal	Bioinformatics
Volume	19
Issue number	15
DOIs	https://doi.org/10.1093/bioinformatics/btg265
State	Published - Oct 12 2003
Externally published	Yes

Bibliographical note

Funding Information:
The authors are grateful to Heladia Salgado, member of RegulonDB (Salgado et al., 2000 http://www.cifn.unam.mx/ Computational_Genomics/regulondb/) database support group and Jose Carlos Gonzalez of Universidad Politec-nica de Madrid (UPM) for useful discussions and Ruti Hershberg, member of PromEC (Hershberg et al., 2001, http://bioinfo.md.huji.ac.il/marg/promec/) database support group for the data for the experiments and other useful information. We also wish to thank Spanish Ministerio de Educacion, Culltura y Deporte and School of Telecommunications, UPM, for their support through grant no. SAB2001-0057. We also wish to thank the anonymous referees for their valuable comments that helped us to improve the paper.

Funding Information:
This work is supported by BBSRC grant no.111/BIO14428, ‘Pattern recognition techniques for gene identification in plant genomic sequences’ and EPSRC grant GR/M14937, ‘Predictive complexity: recursion-theoretic variants’.

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btg265

Cite this

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title = "Sequence alignment kernel for recognition of promoter regions",

abstract = "In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.",

author = "Leo Gordon and Chervonenkis, {Alexey Ya} and Gammerman, {Alex J.} and Shahmuradov, {Ilham A.} and Solovyev, {Victor V.}",

note = "Funding Information: The authors are grateful to Heladia Salgado, member of RegulonDB (Salgado et al., 2000 http://www.cifn.unam.mx/ Computational_Genomics/regulondb/) database support group and Jose Carlos Gonzalez of Universidad Politec-nica de Madrid (UPM) for useful discussions and Ruti Hershberg, member of PromEC (Hershberg et al., 2001, http://bioinfo.md.huji.ac.il/marg/promec/) database support group for the data for the experiments and other useful information. We also wish to thank Spanish Ministerio de Educacion, Culltura y Deporte and School of Telecommunications, UPM, for their support through grant no. SAB2001-0057. We also wish to thank the anonymous referees for their valuable comments that helped us to improve the paper. Funding Information: This work is supported by BBSRC grant no.111/BIO14428, {\textquoteleft}Pattern recognition techniques for gene identification in plant genomic sequences{\textquoteright} and EPSRC grant GR/M14937, {\textquoteleft}Predictive complexity: recursion-theoretic variants{\textquoteright}.",

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PY - 2003/10/12

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N2 - In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

AB - In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

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Sequence alignment kernel for recognition of promoter regions

Abstract

Bibliographical note

ASJC Scopus subject areas

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