GAPK: genetic algorithms with prior knowledge for motif discovery in DNA sequences
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Moitf GibbsGA: Sampling Transcription Factor Binding Sites Coupled with PSFM Optimization by GA
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
An improved genetic algorithm for DNA motif discovery with public domain information
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
iGAPK: improved GAPK algorithm for regulatory DNA motif discovery
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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Motivation: Position weight matrices (PMWs) are simple models commonly used in motif-finding algorithms to identify short functional elements, such as cis-regulatory motifs, on genes. When few experimentally verified motifs are available, estimation of the PWM may be poor. The resultant PWM may not reliably discriminate a true motif from a false one. While experimentally identifying such motifs remains time-consuming and expensive, low-resolution binding data from techniques such as ChIP-on-chip and ChIP-PET have become available. We propose a novel but simple method to improve a poorly estimated PWM using ChIP data. Methodology: Starting from an existing PWM, a set of ChIP sequences, and a set of background sequences, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area under the receiver operating characteristic (ROC) curve. GAPWM can easily incorporate prior information such as base conservation. We tested our method on two PMWs (Oct4/Sox2 and p53) using three recently published ChIP data sets (human Oct4, mouse Oct4 and human p53). Results: GAPWM substantially increased the sensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM. Furthermore, it still functioned when the starting PWM contained a major error. The ROC performance of GAPWM compared favorably with that of MEME and others. With increasing availability of ChIP data, our method provides an alternative for obtaining high-quality PWMs for genome-wide identification of transcription factor binding sites. Availability: The C source code and all data used in this report are available at http://dir.niehs.nih.gov/dirbb/gapwm Contact: li3@niehs.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.