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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome
Nicolas Philippe1, Elias Bou Samra, Anthony Boureux
1Transcriptomics, bioinformatics and myeloid leukaemia, INSERM, U1040, Institute for Research in Biotherapy, Montpellier F-34197, France, Université Montpellier 2, Montpellier, France, Institut de Biologie Computationnelle, Maison de la modélisation, Université Montpellier 2, France, LIRMM, MAB, CNRS UMR 5506, Université Montpellier 2, Montpellier, France and Genomic instability of pluripotent stem cells, INSERM, U1040, Institute for Research in Biotherapy, Montpellier F-34197, France.
Nucleic Acids Research
|December 21, 2013
Summary
Researchers identified ~34,000 novel transcribed regions using digital gene expression (DGE) and RNA-sequencing. This bioinformatics approach aids in discovering tissue-specific transcripts and understanding genome complexity.
Area of Science:
- Genomics
- Bioinformatics
- Transcriptomics
Background:
- Next-generation sequencing, particularly digital gene expression (DGE), enables massive parallel production of short reads for transcriptome analysis.
- DGE technologies offer a large dynamic range by generating short tag signatures for cell transcripts, which can be mapped to a reference genome.
- These tags can identify new transcribed regions, further characterized by RNA-sequencing (RNA-Seq) reads.
Purpose of the Study:
- To explore novel transcriptional regions and their biological features, including tissue expression and conservation.
- To integrate diverse data types, including DGE tags, RNA-Seq, tiling array expression data, and species comparison.
- To develop and apply a bioinformatics approach for comprehensive transcriptome analysis.
Main Methods:
- Analysis of a large digital gene expression dataset ('TranscriRef').
- Annotation of 750,000 uniquely mapped tags to the human genome using Ensembl.
- Integration of DGE tags, RNA-Seq, tiling array data, and species comparison to identify and categorize novel transcribed regions.
- Categorization of transcripts based on origin (protein-coding, antisense, intronic, intergenic) and overlap with non-coding transcripts.
Main Results:
- Identification of approximately 34,000 novel transcribed regions outside the boundaries of known protein-coding genes.
- Annotation of 750,000 unique tags mapped to the human genome.
- Characterization of novel transcripts originating from both DNA strands, including antisense, intronic, and intergenic regions.
- Demonstration of biological validation using sequencing data from human pluripotent stem cells.
Conclusions:
- The integrated bioinformatics approach successfully identified a significant number of novel transcribed regions.
- The method facilitates the discovery of tissue-specific candidate transcripts.
- This approach enhances the understanding of genome complexity and transcriptional landscape.
- The DigitagCT tool is available for broader application in transcriptome analysis.
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