A systematic evaluation of pattern discovery algorithms

ABSTRACT: Pattern discovery algorithms are methods for discovering recurrent, non-random motifs widely used in the analysis of biological sequences. Many algorithms exist but few comparisons have been made amongst them. We systematically profile eight representative methods at multiple parameter settings across 174 diverse experimental datasets, including ten novel ChIP-on-chip datasets. We executed 16,777 pattern discovery analyses to assess prediction accuracy, CPU usage and memory consumption. For 144 datasets we developed a gold-standard using machine-learning algorithms; cross-validation was used for the remaining datasets. Performance was highly disparate, with median accuracy ranging from 32% to 96%. Importantly we were unable to replicate previously reported algorithm-rankings, emphasizing t

ORGANISM(S): Homo sapiens

SUBMITTER: Igor Jurisica 

PROVIDER: E-GEOD-15370 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress