Deconvolution of Expression for Nascent RNA sequencing data (DENR) highlights pre-RNA isoform diversity in human cells.

Zhao, Yixin, Dukler, Noah, Barshad, Gilad, Toneyan, Shushan, Danko, Charles G, Siepel, Adam (August 2021) Deconvolution of Expression for Nascent RNA sequencing data (DENR) highlights pre-RNA isoform diversity in human cells. Bioinformatics. ISSN 1367-4803

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URL: https://www.ncbi.nlm.nih.gov/pubmed/34382072
DOI: 10.1093/bioinformatics/btab582

Abstract

MOTIVATION: Quantification of isoform abundance has been extensively studied at the mature-RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing. RESULTS: We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA sequencing data (DENR), which models nascent RNA sequencing read counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical "shape profile" of read counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes. AVAILABILITY: DENR and nascentRNASim are freely available at https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > transcription
bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Koo Lab
CSHL labs > Siepel lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 11 August 2021
Date Deposited: 18 Aug 2021 13:35
Last Modified: 15 Dec 2021 20:43
PMCID: PMC8665767
URI: https://repository.cshl.edu/id/eprint/40327

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