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Identification of allele-specific alternative mRNA processing via transcriptome sequencing.

Li G, Bahn JH, Lee JH, Peng G, Chen Z, Nelson SF, Xiao X - Nucleic Acids Res. (2012)

Bottom Line: Establishing the functional roles of genetic variants remains a significant challenge in the post-genomic era.Finally, many genes identified in our study were also reported as disease/phenotype-associated genes in genome-wide association studies.Future applications of our approach may provide ample insights for a better understanding of the genetic basis of gene regulation underlying phenotypic diversity and disease mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology and Physiology, David Geffen School of Medicine and Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA 90095, USA.

ABSTRACT
Establishing the functional roles of genetic variants remains a significant challenge in the post-genomic era. Here, we present a method, allele-specific alternative mRNA processing (ASARP), to identify genetically influenced mRNA processing events using transcriptome sequencing (RNA-Seq) data. The method examines RNA-Seq data at both single-nucleotide and whole-gene/isoform levels to identify allele-specific expression (ASE) and existence of allele-specific regulation of mRNA processing. We applied the methods to data obtained from the human glioblastoma cell line U87MG and primary breast cancer tissues and found that 26-45% of all genes with sufficient read coverage demonstrated ASE, with significant overlap between the two cell types. Our methods predicted potential mechanisms underlying ASE due to regulations affecting either whole-gene-level expression or alternative mRNA processing, including alternative splicing, alternative polyadenylation and alternative transcriptional initiation. Allele-specific alternative splicing and alternative polyadenylation may explain ASE in hundreds of genes in each cell type. Reporter studies following these predictions identified the causal single nucleotide variants (SNVs) for several allele-specific alternative splicing events. Finally, many genes identified in our study were also reported as disease/phenotype-associated genes in genome-wide association studies. Future applications of our approach may provide ample insights for a better understanding of the genetic basis of gene regulation underlying phenotypic diversity and disease mechanisms.

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Number of genes with ASE patterns in the U87MG cells classified into different categories. A total of 488 genes were included that could be classified using our approach (see ‘Materials and Methods’ section). The numbers shown outside the pie chart represent the total number of genes in each category. The percentages for the alternative mRNA processing events were calculated relative to the union of all 291 genes with such events. ‘Gene Level’ events are not included in the percentage calculation because they are not comparable with the mRNA processing events (e.g. the latter only applies to genes with alternative mRNA processing). Since some genes may be classified into more than one category, the sum of the percentages of all types may be larger than 100%.
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gks280-F4: Number of genes with ASE patterns in the U87MG cells classified into different categories. A total of 488 genes were included that could be classified using our approach (see ‘Materials and Methods’ section). The numbers shown outside the pie chart represent the total number of genes in each category. The percentages for the alternative mRNA processing events were calculated relative to the union of all 291 genes with such events. ‘Gene Level’ events are not included in the percentage calculation because they are not comparable with the mRNA processing events (e.g. the latter only applies to genes with alternative mRNA processing). Since some genes may be classified into more than one category, the sum of the percentages of all types may be larger than 100%.

Mentions: Based on the above observations, it is possible to distinguish the potential regulatory mechanisms of ASE patterns using RNA-Seq data. We thus classified genes into the aforementioned categories via an automatic pipeline named ASARP (see ‘Materials and Methods’ section and Supplementary Figure S1). This analysis compares the expression patterns of all heterozygous SNVs of a gene and combines SNV expression with expression of alternatively processed mRNA regions as illustrated by the examples in Figure 3. Note that the specific SNVs associated with ASAS, ASAP or ASTI events were not required to have statistically significant ASE because, despite a large allelic bias, such SNVs often fail to pass the power requirement due to the fact that they reside in regions that by definition are sometimes absent in the mRNA. Altogether, 488 genes were classified into the aforementioned categories (Supplementary Table S2). Specifically, 197 genes showed ASE at the whole-gene-level and 291 genes demonstrated allele-specific mRNA processing patterns. As summarized in Figure 4, ASAS events constitute the largest category (66%) among all allele-specific mRNA processing events considered in this study, followed by the ASAP events. It should be noted that some genes identified with ASE difference at the whole-gene level might also show allele-specific mRNA processing patterns, which is not considered in the above results. We estimated the FDR of the above analyses for each type of event using read-randomization similarly as for the ASE events (see ‘Materials and Methods’ section). The numbers of genes identified with ASAS, ASAP and ASTI in the randomized data were 34, 16 and 1, yielding an FDR of 18, 12 and 1%, respectively.Figure 4.


Identification of allele-specific alternative mRNA processing via transcriptome sequencing.

Li G, Bahn JH, Lee JH, Peng G, Chen Z, Nelson SF, Xiao X - Nucleic Acids Res. (2012)

Number of genes with ASE patterns in the U87MG cells classified into different categories. A total of 488 genes were included that could be classified using our approach (see ‘Materials and Methods’ section). The numbers shown outside the pie chart represent the total number of genes in each category. The percentages for the alternative mRNA processing events were calculated relative to the union of all 291 genes with such events. ‘Gene Level’ events are not included in the percentage calculation because they are not comparable with the mRNA processing events (e.g. the latter only applies to genes with alternative mRNA processing). Since some genes may be classified into more than one category, the sum of the percentages of all types may be larger than 100%.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3401465&req=5

gks280-F4: Number of genes with ASE patterns in the U87MG cells classified into different categories. A total of 488 genes were included that could be classified using our approach (see ‘Materials and Methods’ section). The numbers shown outside the pie chart represent the total number of genes in each category. The percentages for the alternative mRNA processing events were calculated relative to the union of all 291 genes with such events. ‘Gene Level’ events are not included in the percentage calculation because they are not comparable with the mRNA processing events (e.g. the latter only applies to genes with alternative mRNA processing). Since some genes may be classified into more than one category, the sum of the percentages of all types may be larger than 100%.
Mentions: Based on the above observations, it is possible to distinguish the potential regulatory mechanisms of ASE patterns using RNA-Seq data. We thus classified genes into the aforementioned categories via an automatic pipeline named ASARP (see ‘Materials and Methods’ section and Supplementary Figure S1). This analysis compares the expression patterns of all heterozygous SNVs of a gene and combines SNV expression with expression of alternatively processed mRNA regions as illustrated by the examples in Figure 3. Note that the specific SNVs associated with ASAS, ASAP or ASTI events were not required to have statistically significant ASE because, despite a large allelic bias, such SNVs often fail to pass the power requirement due to the fact that they reside in regions that by definition are sometimes absent in the mRNA. Altogether, 488 genes were classified into the aforementioned categories (Supplementary Table S2). Specifically, 197 genes showed ASE at the whole-gene-level and 291 genes demonstrated allele-specific mRNA processing patterns. As summarized in Figure 4, ASAS events constitute the largest category (66%) among all allele-specific mRNA processing events considered in this study, followed by the ASAP events. It should be noted that some genes identified with ASE difference at the whole-gene level might also show allele-specific mRNA processing patterns, which is not considered in the above results. We estimated the FDR of the above analyses for each type of event using read-randomization similarly as for the ASE events (see ‘Materials and Methods’ section). The numbers of genes identified with ASAS, ASAP and ASTI in the randomized data were 34, 16 and 1, yielding an FDR of 18, 12 and 1%, respectively.Figure 4.

Bottom Line: Establishing the functional roles of genetic variants remains a significant challenge in the post-genomic era.Finally, many genes identified in our study were also reported as disease/phenotype-associated genes in genome-wide association studies.Future applications of our approach may provide ample insights for a better understanding of the genetic basis of gene regulation underlying phenotypic diversity and disease mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology and Physiology, David Geffen School of Medicine and Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA 90095, USA.

ABSTRACT
Establishing the functional roles of genetic variants remains a significant challenge in the post-genomic era. Here, we present a method, allele-specific alternative mRNA processing (ASARP), to identify genetically influenced mRNA processing events using transcriptome sequencing (RNA-Seq) data. The method examines RNA-Seq data at both single-nucleotide and whole-gene/isoform levels to identify allele-specific expression (ASE) and existence of allele-specific regulation of mRNA processing. We applied the methods to data obtained from the human glioblastoma cell line U87MG and primary breast cancer tissues and found that 26-45% of all genes with sufficient read coverage demonstrated ASE, with significant overlap between the two cell types. Our methods predicted potential mechanisms underlying ASE due to regulations affecting either whole-gene-level expression or alternative mRNA processing, including alternative splicing, alternative polyadenylation and alternative transcriptional initiation. Allele-specific alternative splicing and alternative polyadenylation may explain ASE in hundreds of genes in each cell type. Reporter studies following these predictions identified the causal single nucleotide variants (SNVs) for several allele-specific alternative splicing events. Finally, many genes identified in our study were also reported as disease/phenotype-associated genes in genome-wide association studies. Future applications of our approach may provide ample insights for a better understanding of the genetic basis of gene regulation underlying phenotypic diversity and disease mechanisms.

Show MeSH
Related in: MedlinePlus