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iRNA-seq: computational method for genome-wide assessment of acute transcriptional regulation from total RNA-seq data.

Madsen JG, Schmidt SF, Larsen BD, Loft A, Nielsen R, Mandrup S - Nucleic Acids Res. (2015)

Bottom Line: However, as it does not provide an account of transcriptional activity per se, other technologies are needed to more precisely determine acute transcriptional responses.Here, we have developed an easy, sensitive and accurate novel computational method, IRNA-SEQ: , for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data.However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M 5230, Denmark NNF Center of Basic Metabolic Research, University of Copenhagen, Copenhagen 2200, Denmark.

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Outline of the iRNA-seq pipeline. (A) iRNA-seq takes SAM/BAM input files and counts reads within intron regions of the longest isoform for each gene. All regions associated with Genbank mRNAs are subtracted from the regions to be counted and the remaining intron reads are summarized for each transcript. edgeR is then used to perform differential expression analysis. (B) Screenshot from the UCSC genome browser illustrating how differential exon usage (arrow A) and incomplete annotation (arrow B) result exon reads contributing to coverage in introns of PPARG1. In the iRNA-seq pipeline, such regions are excluded using the Genbank mRNA track.
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Figure 2: Outline of the iRNA-seq pipeline. (A) iRNA-seq takes SAM/BAM input files and counts reads within intron regions of the longest isoform for each gene. All regions associated with Genbank mRNAs are subtracted from the regions to be counted and the remaining intron reads are summarized for each transcript. edgeR is then used to perform differential expression analysis. (B) Screenshot from the UCSC genome browser illustrating how differential exon usage (arrow A) and incomplete annotation (arrow B) result exon reads contributing to coverage in introns of PPARG1. In the iRNA-seq pipeline, such regions are excluded using the Genbank mRNA track.

Mentions: To investigate this at a genome-wide level, we designed a Perl pipeline, iRNA-seq, which for every gene quantifies and sums intron reads not overlapping with any other transcripts or non-coding RNA. Intron reads are counted and summed using featureCounts (23), and differential expression analysis is performed using edgeR (24) (Figure 2A). To minimize problems arising from inclusion of exon reads due to differential exon usage (Figure 2B, arrow A) and incomplete annotation (Figure 2B, arrow B), all regions associated with mRNA were subtracted from the regions subjected to counting (see Materials and Methods). In addition to the default quantification of intron reads, the iRNA-seq tool also allows quantification of reads in exons or reads in full-length genes. Thus, the iRNA-seq tool allows fast and easy parallel assessment of mature transcript levels as well as active transcription from one total RNA-seq experiment.


iRNA-seq: computational method for genome-wide assessment of acute transcriptional regulation from total RNA-seq data.

Madsen JG, Schmidt SF, Larsen BD, Loft A, Nielsen R, Mandrup S - Nucleic Acids Res. (2015)

Outline of the iRNA-seq pipeline. (A) iRNA-seq takes SAM/BAM input files and counts reads within intron regions of the longest isoform for each gene. All regions associated with Genbank mRNAs are subtracted from the regions to be counted and the remaining intron reads are summarized for each transcript. edgeR is then used to perform differential expression analysis. (B) Screenshot from the UCSC genome browser illustrating how differential exon usage (arrow A) and incomplete annotation (arrow B) result exon reads contributing to coverage in introns of PPARG1. In the iRNA-seq pipeline, such regions are excluded using the Genbank mRNA track.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4381047&req=5

Figure 2: Outline of the iRNA-seq pipeline. (A) iRNA-seq takes SAM/BAM input files and counts reads within intron regions of the longest isoform for each gene. All regions associated with Genbank mRNAs are subtracted from the regions to be counted and the remaining intron reads are summarized for each transcript. edgeR is then used to perform differential expression analysis. (B) Screenshot from the UCSC genome browser illustrating how differential exon usage (arrow A) and incomplete annotation (arrow B) result exon reads contributing to coverage in introns of PPARG1. In the iRNA-seq pipeline, such regions are excluded using the Genbank mRNA track.
Mentions: To investigate this at a genome-wide level, we designed a Perl pipeline, iRNA-seq, which for every gene quantifies and sums intron reads not overlapping with any other transcripts or non-coding RNA. Intron reads are counted and summed using featureCounts (23), and differential expression analysis is performed using edgeR (24) (Figure 2A). To minimize problems arising from inclusion of exon reads due to differential exon usage (Figure 2B, arrow A) and incomplete annotation (Figure 2B, arrow B), all regions associated with mRNA were subtracted from the regions subjected to counting (see Materials and Methods). In addition to the default quantification of intron reads, the iRNA-seq tool also allows quantification of reads in exons or reads in full-length genes. Thus, the iRNA-seq tool allows fast and easy parallel assessment of mature transcript levels as well as active transcription from one total RNA-seq experiment.

Bottom Line: However, as it does not provide an account of transcriptional activity per se, other technologies are needed to more precisely determine acute transcriptional responses.Here, we have developed an easy, sensitive and accurate novel computational method, IRNA-SEQ: , for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data.However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M 5230, Denmark NNF Center of Basic Metabolic Research, University of Copenhagen, Copenhagen 2200, Denmark.

Show MeSH