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CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.

Badr E, Heath LS - BMC Bioinformatics (2015)

Bottom Line: Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy.We show that our results intersect with previous results, including some that are experimental.Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.

ABSTRACT

Background: Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches.

Results: We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues.

Conclusion: The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

No MeSH data available.


Related in: MedlinePlus

Possible combinatorial effect of the overlapped SREs (GGGAGGA,GAGGAC). One possible scenario is having SF2/ASF splicing factor with great affinity. It binds to the ESE and stimulate exon inclusion. Another possibility is if the splicing repressor hnRNP A1 exists, it may inhibit the exon inclusion by binding to the silencer sequence and recruit the binding of other inhibitory factors which extend to the exon boundary and prohibit the binding of the SF2/ASF protein. As a result, the exon will be skipped. The rectangles in this figure represent exons and lines represent introns
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Fig8: Possible combinatorial effect of the overlapped SREs (GGGAGGA,GAGGAC). One possible scenario is having SF2/ASF splicing factor with great affinity. It binds to the ESE and stimulate exon inclusion. Another possibility is if the splicing repressor hnRNP A1 exists, it may inhibit the exon inclusion by binding to the silencer sequence and recruit the binding of other inhibitory factors which extend to the exon boundary and prohibit the binding of the SF2/ASF protein. As a result, the exon will be skipped. The rectangles in this figure represent exons and lines represent introns

Mentions: This is one of the known classical examples of the combinatorial effect of having both an ESE and an ESS in adjacent positions. There are several studies that report the antagonistic behavior between the SF2/ASF and hnRNP A1 splicing factors [8, 23]. For example, in exon 3 of the HIV1 tat gene, the hnRNP A1 splicing factor may bind to an ESS and inhibit splicing by propagating hnRNP A1 molecules further towards the 3’ splicing site. That propagation behavior can be inhibited by the SF2/ASF splicing factor when it binds to an ESE that resides upstream of the ESS, as in our sequence [8, 23, 38–40]. Furthermore, Mayeda et al. [41] showed in vitro that having different ratios of SF2/ASF to hnRNP A1 promotes exon skipping or inclusion by binding to different ESEs or ESSs. Therefore, that could provide us with an understanding of what might be the possible outcomes of combinatorial splicing regulation (Fig. 8).Fig. 8


CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.

Badr E, Heath LS - BMC Bioinformatics (2015)

Possible combinatorial effect of the overlapped SREs (GGGAGGA,GAGGAC). One possible scenario is having SF2/ASF splicing factor with great affinity. It binds to the ESE and stimulate exon inclusion. Another possibility is if the splicing repressor hnRNP A1 exists, it may inhibit the exon inclusion by binding to the silencer sequence and recruit the binding of other inhibitory factors which extend to the exon boundary and prohibit the binding of the SF2/ASF protein. As a result, the exon will be skipped. The rectangles in this figure represent exons and lines represent introns
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4559876&req=5

Fig8: Possible combinatorial effect of the overlapped SREs (GGGAGGA,GAGGAC). One possible scenario is having SF2/ASF splicing factor with great affinity. It binds to the ESE and stimulate exon inclusion. Another possibility is if the splicing repressor hnRNP A1 exists, it may inhibit the exon inclusion by binding to the silencer sequence and recruit the binding of other inhibitory factors which extend to the exon boundary and prohibit the binding of the SF2/ASF protein. As a result, the exon will be skipped. The rectangles in this figure represent exons and lines represent introns
Mentions: This is one of the known classical examples of the combinatorial effect of having both an ESE and an ESS in adjacent positions. There are several studies that report the antagonistic behavior between the SF2/ASF and hnRNP A1 splicing factors [8, 23]. For example, in exon 3 of the HIV1 tat gene, the hnRNP A1 splicing factor may bind to an ESS and inhibit splicing by propagating hnRNP A1 molecules further towards the 3’ splicing site. That propagation behavior can be inhibited by the SF2/ASF splicing factor when it binds to an ESE that resides upstream of the ESS, as in our sequence [8, 23, 38–40]. Furthermore, Mayeda et al. [41] showed in vitro that having different ratios of SF2/ASF to hnRNP A1 promotes exon skipping or inclusion by binding to different ESEs or ESSs. Therefore, that could provide us with an understanding of what might be the possible outcomes of combinatorial splicing regulation (Fig. 8).Fig. 8

Bottom Line: Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy.We show that our results intersect with previous results, including some that are experimental.Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.

ABSTRACT

Background: Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches.

Results: We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues.

Conclusion: The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

No MeSH data available.


Related in: MedlinePlus