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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

CoSREM time performance using different values for α in case of θ=100 and 300
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Fig11: CoSREM time performance using different values for α in case of θ=100 and 300

Mentions: Performance analysis of CoSREM is depicted in Fig. 11 with the actual numbers of generated MCSs and MCS collections are illustrated in Table 5. The first level of CoSREM, GenMCS, running time mainly depends on the number of discovered patterns as well as the number of explored branches [34]. For building the MCSTree, the time complexity is O(2r) in the worst case, where r is the number of MCSs. However, as stated before, multiple pruning strategies are used to reduce the time taken to build the tree. Traversing the tree utilizing the classic BFS algorithm takes O/V/ where V is the number of nodes in the MCStree.Table 5


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

Badr E, Heath LS - BMC Bioinformatics (2015)

CoSREM time performance using different values for α in case of θ=100 and 300
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig11: CoSREM time performance using different values for α in case of θ=100 and 300
Mentions: Performance analysis of CoSREM is depicted in Fig. 11 with the actual numbers of generated MCSs and MCS collections are illustrated in Table 5. The first level of CoSREM, GenMCS, running time mainly depends on the number of discovered patterns as well as the number of explored branches [34]. For building the MCSTree, the time complexity is O(2r) in the worst case, where r is the number of MCSs. However, as stated before, multiple pruning strategies are used to reduce the time taken to build the tree. Traversing the tree utilizing the classic BFS algorithm takes O/V/ where V is the number of nodes in the MCStree.Table 5

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