An efficient machine learning approach to low-complexity filtering in biological sequences

Biological sequences contain low-complexity regions (LCRs) which produce superfluous matches in homology searches, and lead to slow execution of database search algorithms such as BLAST. These regions are efficiently identified by low-complexity filtering algorithms such as SDUST and SEG, which are included in the BLAST tool-suite. These algorithms target differing notions of complexity, so an algorithm which combines their sensitivities is pursued. A variety of features are derived from these algorithms, as well as a new filtering algorithm based on Lempel-Ziv complexity. Artificial sequences with known LCRs are used to train and evaluate an SVM classifier, which significantly outperforms the standalone filtering algorithms.