Sigma: multiple alignment of weakly-conserved non-coding DNA sequence-2

<p><b>Copyright information:</b></p><p>Taken from "Sigma: multiple alignment of weakly-conserved non-coding DNA sequence"</p><p>BMC Bioinformatics 2006;7():143-143.</p><p>Published online 16 Mar 2006</p><p>PMCID:PMC1468434.</p><p></p>un on various alignments: a measure of the sensitivity of PhyloGibbs on those alignments

Sigma: multiple alignment of weakly-conserved non-coding DNA sequence-4

<p><b>Copyright information:</b></p><p>Taken from "Sigma: multiple alignment of weakly-conserved non-coding DNA sequence"</p><p>BMC Bioinformatics 2006;7():143-143.</p><p>Published online 16 Mar 2006</p><p>PMCID:PMC1468434.</p><p></p>ce with = 0.55 (see caption of tables 1 and 2), on sequences of length l000 bp each; for = 2–10. (The AlignM program requires at least 3 input sequences.

Performance of various programs in detecting discriminative motifs, on the same data as in Figure 3.

<p>Performance of various programs in detecting discriminative motifs, on the same data as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000156#pcbi-1000156-g003" target="_blank">Figure 3</a>.</p

Performance of discriminative motif-finders on pairs of regulatory sequence from fly.

<p>Performance of discriminative motif-finders on pairs of regulatory sequence from fly.</p

Sigma: multiple alignment of weakly-conserved non-coding DNA sequence-3

<p><b>Copyright information:</b></p><p>Taken from "Sigma: multiple alignment of weakly-conserved non-coding DNA sequence"</p><p>BMC Bioinformatics 2006;7():143-143.</p><p>Published online 16 Mar 2006</p><p>PMCID:PMC1468434.</p><p></p>redictions, by Phylogibbs run on various alignments: a measure of the specificity of PhyloGibbs on those alignments, and indirectly a measure of the quality of those alignments

Performance of PhyloGibbs-MP (with flyreg priors, and 2 species) in detecting known CRMs in fly, compared with four other module finders.

<p>Dotted lines indicate the performance expected if programs made predictions at random (that is, if, for each input sequence, the same number of site predictions were made but at random locations). Note that, in this data, 816457 bp out of 2448515 bp is in annotated CRMs; so a completely random program would exhibit roughly a specificity of 0.33, in agreement with the dotted lines at high sensitivity.</p

The performance of various motif-finders on predicting binding sites in <i>D. melanogaster</i> taken from REDfly 2.0.

<p>The interpretation is similar to that in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000156#pcbi-1000156-g001" target="_blank">Figure 1</a>.</p

For fifteen transcription factors bound by between 4 and 9 sequences with <i>p</i><0.001 in ChIP-chip experiments reported by Harbison et al. [31], weight matrices reported by those authors, in both orientations, compared with predictions of four discriminative motif-finders on binding sequences discriminated against randomly chosen non-binding sequences.

<p>No other prior information was used. PhyloGibbs-MP does not internally characterise discriminative sets as “positive” or “negative” but only predictions from the positive set (including, in some cases, multiple predictions) are reported. Other programs make at most one prediction per set. All programs report position weight matrices, which were used directly to generate sequence logos (using WebLogo <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000156#pcbi.1000156-Crooks1" target="_blank">[41]</a> and some helper scripts). The predictions are discussed, qualitatively, in the text.</p

Sigma: multiple alignment of weakly-conserved non-coding DNA sequence-0

<p><b>Copyright information:</b></p><p>Taken from "Sigma: multiple alignment of weakly-conserved non-coding DNA sequence"</p><p>BMC Bioinformatics 2006;7():143-143.</p><p>Published online 16 Mar 2006</p><p>PMCID:PMC1468434.</p><p></p>"0.", are locally aligned: the aligned piece goes into one fragment with two sequence labels, and the remaining pieces go into their own fragments. The sequence labels increase from left to right on any sequence, and are used to maintain consistency in alignments

Results of running PhyloGibbs-MP, in module-prediction mode, on the 8 kb sequence upstream of the <i>eve</i> gene in Drosophila.

<p>When run without priors, predictions lie on or close to all four annotated modules in this region from the REDfly database. When weight matrices for the gap transcription factors are used as priors, PhyloGibbs-MP fails to find the proximal promoter, but the stripe 2 and stripes 3+7 enhancers are detected with increased confidence. Predicted sites for individual motifs, as well as cumulative predictions over all motifs, are shown.</p