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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

An example of mining cohesive subgraphs. The graph at the top left corner represents the SRE graph . We choose R = 30 which means the SRE graph contains the top 30 6-mers in rank. The matrix on the right is the SRE profile matrix PESE. Setting α=1000 means that the connected vertices should co-occur in at least 1000 exons to be considered a cohesive subgraph. The tree in the middle shows how GenMCS proceeds. The bold boxes represent cohesive subgraphs. The dotted boxes represent subgraphs that are not cohesive and the remaining branch will be pruned. The output is 9 subgraphs as illustrated in the bottom graph
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Fig2: An example of mining cohesive subgraphs. The graph at the top left corner represents the SRE graph . We choose R = 30 which means the SRE graph contains the top 30 6-mers in rank. The matrix on the right is the SRE profile matrix PESE. Setting α=1000 means that the connected vertices should co-occur in at least 1000 exons to be considered a cohesive subgraph. The tree in the middle shows how GenMCS proceeds. The bold boxes represent cohesive subgraphs. The dotted boxes represent subgraphs that are not cohesive and the remaining branch will be pruned. The output is 9 subgraphs as illustrated in the bottom graph

Mentions: Figure 2 illustrates an example of the algorithm in case of ESEs. The output is a table called MCStable. It consists of maximal cohesive subgraphs and each subgraph is associated with a set of exons where the splicing enhancer, which this subgraph represents, resides. We apply GenMCS utilizing and PESS as inputs to get potential silencers as well.Fig. 2


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

Badr E, Heath LS - BMC Bioinformatics (2015)

An example of mining cohesive subgraphs. The graph at the top left corner represents the SRE graph . We choose R = 30 which means the SRE graph contains the top 30 6-mers in rank. The matrix on the right is the SRE profile matrix PESE. Setting α=1000 means that the connected vertices should co-occur in at least 1000 exons to be considered a cohesive subgraph. The tree in the middle shows how GenMCS proceeds. The bold boxes represent cohesive subgraphs. The dotted boxes represent subgraphs that are not cohesive and the remaining branch will be pruned. The output is 9 subgraphs as illustrated in the bottom graph
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: An example of mining cohesive subgraphs. The graph at the top left corner represents the SRE graph . We choose R = 30 which means the SRE graph contains the top 30 6-mers in rank. The matrix on the right is the SRE profile matrix PESE. Setting α=1000 means that the connected vertices should co-occur in at least 1000 exons to be considered a cohesive subgraph. The tree in the middle shows how GenMCS proceeds. The bold boxes represent cohesive subgraphs. The dotted boxes represent subgraphs that are not cohesive and the remaining branch will be pruned. The output is 9 subgraphs as illustrated in the bottom graph
Mentions: Figure 2 illustrates an example of the algorithm in case of ESEs. The output is a table called MCStable. It consists of maximal cohesive subgraphs and each subgraph is associated with a set of exons where the splicing enhancer, which this subgraph represents, resides. We apply GenMCS utilizing and PESS as inputs to get potential silencers as well.Fig. 2

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