A co-evolutionary framework for regulatory motif discovery

Michael A. Lones; Andy M. Tyrrell

A co-evolutionary framework for regulatory motif discovery

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs-suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.

Original language	English
Title of host publication	2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS
Place of Publication	NEW YORK
Publisher	IEEE
Pages	3894-3901
Number of pages	8
ISBN (Print)	978-1-4244-1339-3
Publication status	Published - 2007
Event	IEEE Congress on Evolutionary Computation - Singapore Duration: 25 Sep 2007 → 28 Sep 2007

Conference

Conference	IEEE Congress on Evolutionary Computation
City	Singapore
Period	25/09/07 → 28/09/07

Keywords

TRANSCRIPTIONAL REGULATION
SITES
COMPUTATION
ALGORITHMS
EXPRESSION
PROMOTERS
PROFILES
SEQUENCE
MODULES

Cite this

@inproceedings{6e8d3212eca84d8bb2866976f5494700,

title = "A co-evolutionary framework for regulatory motif discovery",

abstract = "In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs-suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.",

keywords = "TRANSCRIPTIONAL REGULATION, SITES, COMPUTATION, ALGORITHMS, EXPRESSION, PROMOTERS, PROFILES, SEQUENCE, MODULES",

author = "Lones, {Michael A.} and Tyrrell, {Andy M.}",

year = "2007",

language = "English",

isbn = "978-1-4244-1339-3",

pages = "3894--3901",

booktitle = "2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS",

publisher = "IEEE",

address = "United States",

note = "IEEE Congress on Evolutionary Computation ; Conference date: 25-09-2007 Through 28-09-2007",

}

TY - GEN

T1 - A co-evolutionary framework for regulatory motif discovery

AU - Lones, Michael A.

AU - Tyrrell, Andy M.

PY - 2007

Y1 - 2007

N2 - In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs-suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.

AB - In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs-suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.

KW - TRANSCRIPTIONAL REGULATION

KW - SITES

KW - COMPUTATION

KW - ALGORITHMS

KW - EXPRESSION

KW - PROMOTERS

KW - PROFILES

KW - SEQUENCE

KW - MODULES

M3 - Conference contribution

SN - 978-1-4244-1339-3

SP - 3894

EP - 3901

BT - 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS

PB - IEEE

CY - NEW YORK

T2 - IEEE Congress on Evolutionary Computation

Y2 - 25 September 2007 through 28 September 2007

ER -