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Background  Given a set of  t n -length DNA sequences,  q  satisfying 0 <  q  ≤ 1, and  l  and  d  satisfying 0 ≤  d  <  l  <  n , the quorum planted motif search (qPMS) finds  l -length strings that occur in at least  qt  input sequences with up to  d  mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g.,  t  = 20 and  n  = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more.    Results  We analyze the effects of  t  and  q  on the time performance of qPMS algorithms and find that a large  t  or a small  q  causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set  D ’ with a small  t  and a large  q  from the large input dataset  D  and then executing qPMS algorithms on  D ’. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select  D ’ efficiently and (2) that the qPMS algorithms executed on  D ’ can find implanted or real motifs in a significantly shorter time than when executed on  D .    Conclusions  We improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set  D ’, rather than take an unfeasibly long time to search the original sequence set  D . Our motif discovery method is an approximate algorithm.

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets