Sentence examples for motif discovery algorithm from inspiring English sources

In this paper, we present a highly parallel formulation and implementation of the MEME motif discovery algorithm using the CUDA programming model.

Finally, we use DME, a proven deterministic motif discovery algorithm [11], [14], [20], to discover de novo TFBS motifs for specific TFs and their co-factors.

For every binding site, we retrieved repeat-masked sequence and used de novo motif discovery algorithm MEME [26] to look for shared sequence motifs.

Because motif models are typically constructed from a relatively small number of experimentally verified binding sites (or from an output of a motif discovery algorithm) they can contain a considerable amount of uncertainty.

The motif discovery algorithm Gibbs Motif Sampler [50], [51] was applied to sequences lacking canonical binding sites to seek over-represented motifs as additional candidate Ac-DAF-16 response elements.

The binding preferences can either be the output of a motif discovery algorithm or they can be experimentally measured, such as those reported in curated databases (TRANSFAC [13] and JASPAR [14]).

We previously showed that DME outperforms other motif discovery algorithms on both synthetic and mammalian data.

Two other motif discovery algorithms, MEME [54] and Mdscan [55], were also attempted for de novo motif discovery; however, unlike the robust Gibbs Motif Sampler, either their performance in detecting the consensus DBE with a set of positive control sequences was poor or the parameter setting was not flexible.

There are two ways of finding shared motifs in sets of sequences: (i) applying ab initio motif discovery algorithms which search for recurring patterns in a set of DNA sequences, or (ii) assessing whether previously characterized motifs present in transcription factor binding site databases are statistically over-represented in the sequences [47].

MEME-ChIP employs two motif discovery algorithms with complementary characteristics.

Further, we used motif discovery algorithms to identify novel upstream regulatory sequences.