Publication Date:
2012-11-15
Description:
Background Applying supervised learning/classification techniques to epigenomic data may reveal properties that differentiate histone modifications. Previous analyses sought to classify nucleosomes containing histone H2A/H4 arginine 3 symmetric dimethylation (H2A/H4R3me2s) or H2A.Z using human CD4+ T-cell chromatin immunoprecipitation sequencing (ChIP-Seq) data. However, these efforts only achieved modest accuracy with limited biological interpretation. Here, we investigate the impact of using appropriate data pre-processing —deduplication, normalization, and position- (peak-) finding to identify stable nucleosome positions — in conjunction with advanced classification algorithms, notably discriminatory motif feature selection and random forests. Performance assessments are based on accuracy and interpretative yield. Results We achieved dramatically improved accuracy using histone modification features (99.0%; previous attempts, 68.3%) and DNA sequence features (94.1%; previous attempts,
Electronic ISSN:
1471-2164
Topics:
Biology
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