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A computational approach for genome-wide mapping of splicing factor binding sites.

Akerman M, David-Eden H, Pinter RY, Mandel-Gutfreund Y - Genome Biol. (2009)

Bottom Line: Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns.Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements.The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, The Technion, Israel Institute of Technology, Haifa, Israel. makerman@tx.technion.ac.il

ABSTRACT
Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns. Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements. The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

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Specificity calculated by the COS(WR) method. The percent of accurate predictions derived from a screening of experimentally validated sequences with 30 different SFBS queries. The x-axis shows the rank of the true positive hits (that is, experimentally validated SFBSs) among the list of predictions derived from the screening. The top curve displays the percent of predictions higher than the COS(WR) threshold and the bottom curve shows the percent of predictions below the threshold.
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Figure 3: Specificity calculated by the COS(WR) method. The percent of accurate predictions derived from a screening of experimentally validated sequences with 30 different SFBS queries. The x-axis shows the rank of the true positive hits (that is, experimentally validated SFBSs) among the list of predictions derived from the screening. The top curve displays the percent of predictions higher than the COS(WR) threshold and the bottom curve shows the percent of predictions below the threshold.

Mentions: In order to evaluate the specificity of our method, we measured its ability to predict experimentally verified binding sites of a known SF amongst all other 19 possible SFs. For this purpose we screened a set of core binding sites from experimentally confirmed SFBSs (Additional data file 3) against 30 motifs corresponding to 20 SFs (Table S1 in Additional data file 1). For every core binding site the resulting scores were ranked; ties were given the same ranking index. In cases where the literature reports more than one possible motif for a given SF, we report the highest ranked result. Figure 3 displays the percent of correct predictions amongst the top ranked scores. As shown, for more than 30% of the predictions the highest scored hit (that is, the best prediction) was the 'known binding site' reported in the literature; for almost 60% of the samples the experimentally verified SF was amongst the three best predictions, and in more than 80% of the cases it was amongst the five best predictions. It is important to note that in many cases the core binding site is not clearly defined; therefore, one would expect to find additional SFs in a regulatory sequence that have not been reported in the literature. Moreover, misprediction of some SFBSs could arise from the lack of representation of other sites in the motif set (that is, some motif sets contain only one known SFBS). Nevertheless, when applying the thresholds to the COS(WR) values (described in Materials and methods) we observed that the vast majority of the predictions that were ranked 5 and higher fell above the threshold, while predictions at position 6 or below fell under the threshold (Figure 3).


A computational approach for genome-wide mapping of splicing factor binding sites.

Akerman M, David-Eden H, Pinter RY, Mandel-Gutfreund Y - Genome Biol. (2009)

Specificity calculated by the COS(WR) method. The percent of accurate predictions derived from a screening of experimentally validated sequences with 30 different SFBS queries. The x-axis shows the rank of the true positive hits (that is, experimentally validated SFBSs) among the list of predictions derived from the screening. The top curve displays the percent of predictions higher than the COS(WR) threshold and the bottom curve shows the percent of predictions below the threshold.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691001&req=5

Figure 3: Specificity calculated by the COS(WR) method. The percent of accurate predictions derived from a screening of experimentally validated sequences with 30 different SFBS queries. The x-axis shows the rank of the true positive hits (that is, experimentally validated SFBSs) among the list of predictions derived from the screening. The top curve displays the percent of predictions higher than the COS(WR) threshold and the bottom curve shows the percent of predictions below the threshold.
Mentions: In order to evaluate the specificity of our method, we measured its ability to predict experimentally verified binding sites of a known SF amongst all other 19 possible SFs. For this purpose we screened a set of core binding sites from experimentally confirmed SFBSs (Additional data file 3) against 30 motifs corresponding to 20 SFs (Table S1 in Additional data file 1). For every core binding site the resulting scores were ranked; ties were given the same ranking index. In cases where the literature reports more than one possible motif for a given SF, we report the highest ranked result. Figure 3 displays the percent of correct predictions amongst the top ranked scores. As shown, for more than 30% of the predictions the highest scored hit (that is, the best prediction) was the 'known binding site' reported in the literature; for almost 60% of the samples the experimentally verified SF was amongst the three best predictions, and in more than 80% of the cases it was amongst the five best predictions. It is important to note that in many cases the core binding site is not clearly defined; therefore, one would expect to find additional SFs in a regulatory sequence that have not been reported in the literature. Moreover, misprediction of some SFBSs could arise from the lack of representation of other sites in the motif set (that is, some motif sets contain only one known SFBS). Nevertheless, when applying the thresholds to the COS(WR) values (described in Materials and methods) we observed that the vast majority of the predictions that were ranked 5 and higher fell above the threshold, while predictions at position 6 or below fell under the threshold (Figure 3).

Bottom Line: Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns.Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements.The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, The Technion, Israel Institute of Technology, Haifa, Israel. makerman@tx.technion.ac.il

ABSTRACT
Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns. Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements. The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

Show MeSH