Sentence examples for motif detection algorithms from inspiring English sources
In all tests we observed some reoccurring effects that can be explained by the algorithmic specificities of the applied motif detection algorithms.
Three motif detection algorithms were used: 'MEME' [21], 'Phylogibbs' [18], and 'Phylogenetic sampler' [20].
Several motif detection algorithms have therefore integrated the use of orthology in addition to the frequently used coregulation information [4].
Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information.
In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model.
In this work, we tested the impact of using coregulation and/or orthologous information on the efficiency of regulatory motif discovery by two representative motif detection algorithms with an evolutionary model.
However, short, degenerate and/or relatively rare motifs are unlikely to be detectable by any ab initio motif detection algorithm, so a truly comprehensive analysis may never be possible.
As a comparison we included MEME [21] as a representative of algorithms that cannot explicitly incorporate phylogenetic relations (therefore referred to as a non-phylogenetic motif detection algorithm).
The best results were obtained for a proximity of 0.50 (Figure 3(C)) and under these optimal conditions, algorithms that use an evolutionary model clearly outperform the non-phylogenetic motif detection algorithm in finding high quality motifs (both Sens and PPV).
The HIV-Host protein interaction network was analyzed for network motifs using a motif detection algorithm implemented in Prolog (see additional files 13, 14, 15 and 16).
Actually, using the network motif detection algorithm, FANDOM (49), we can identify all the three-node motifs 'TF-miRNA-gene' in the human and mouse regulatory networks, respectively.
