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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

A bar plot of the PSI (Percent Spliced-In) values of exon 17 in PRKCG gene. It illustrates the difference in the PSI values between normal and tumor samples. The red bars represent the PSI of tumor samples while the green bars represent the normal samples. This figure is generated using TCGA Spliceseq [42]
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Fig9: A bar plot of the PSI (Percent Spliced-In) values of exon 17 in PRKCG gene. It illustrates the difference in the PSI values between normal and tumor samples. The red bars represent the PSI of tumor samples while the green bars represent the normal samples. This figure is generated using TCGA Spliceseq [42]

Mentions: We further investigated the exons in the genes that have this SRE set and identified by CoSREM utilizing TCGA Spliceseq [42]. TCGA is an AS database that utilizes RNA-Seq samples from The Cancer Genome Atlas project to provide the splicing patterns differences between different tumor samples and between tumor and normal samples. Several of these exons were found to be included in several samples of different cancer types and skipped in the normal samples. For example, exon 17 in the PRKCG gene is included in 100 % of all the transcripts of the samples for lung squamous cell carcinoma (LUSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), prostate adenocarcinoma (PRAD), and kidney chromophobe (KICH), while skipped in 100 % of all the transcripts of the normal samples, as shown in Fig. 9. The inclusion or exclusion of these exons may be related to the antagonistic behavior of their positive and negative regulators that we identify. PRKCG is known to be a major receptor for phorbol esters, a class of tumor promoters. As abnormal splicing events are a major contributor to cancer development [43], understanding the reasons behind specific exon inclusion or exclusion can play a role in understanding cancer. The complete list of exon skipping events is shown in Additional file 1: Table S3.Fig. 9


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

Badr E, Heath LS - BMC Bioinformatics (2015)

A bar plot of the PSI (Percent Spliced-In) values of exon 17 in PRKCG gene. It illustrates the difference in the PSI values between normal and tumor samples. The red bars represent the PSI of tumor samples while the green bars represent the normal samples. This figure is generated using TCGA Spliceseq [42]
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig9: A bar plot of the PSI (Percent Spliced-In) values of exon 17 in PRKCG gene. It illustrates the difference in the PSI values between normal and tumor samples. The red bars represent the PSI of tumor samples while the green bars represent the normal samples. This figure is generated using TCGA Spliceseq [42]
Mentions: We further investigated the exons in the genes that have this SRE set and identified by CoSREM utilizing TCGA Spliceseq [42]. TCGA is an AS database that utilizes RNA-Seq samples from The Cancer Genome Atlas project to provide the splicing patterns differences between different tumor samples and between tumor and normal samples. Several of these exons were found to be included in several samples of different cancer types and skipped in the normal samples. For example, exon 17 in the PRKCG gene is included in 100 % of all the transcripts of the samples for lung squamous cell carcinoma (LUSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), prostate adenocarcinoma (PRAD), and kidney chromophobe (KICH), while skipped in 100 % of all the transcripts of the normal samples, as shown in Fig. 9. The inclusion or exclusion of these exons may be related to the antagonistic behavior of their positive and negative regulators that we identify. PRKCG is known to be a major receptor for phorbol esters, a class of tumor promoters. As abnormal splicing events are a major contributor to cancer development [43], understanding the reasons behind specific exon inclusion or exclusion can play a role in understanding cancer. The complete list of exon skipping events is shown in Additional file 1: Table S3.Fig. 9

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