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Transcriptome variation in human tissues revealed by long-read sequencing
New York Genome Ctr, New York, NY 10013 USA.;Columbia Univ, Dept Syst Biol, New York, NY 10027 USA..ORCID iD: 0000-0001-5556-0222
Broad Inst MIT & Harvard, Med & Populat Genet Program, Cambridge, MA 02142 USA..ORCID iD: 0000-0003-0316-461X
New York Genome Ctr, New York, NY 10013 USA..
Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA USA..
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2022 (English)In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 608, no 7922, p. 353-359Article in journal (Refereed) Published
Abstract [en]

Regulation of transcript structure generates transcript diversity and plays an important role in human disease(1-7). The advent oflong-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure(8-)(16). In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 608, no 7922, p. 353-359
National Category
Bioinformatics and Systems Biology Genetics
Identifiers
URN: urn:nbn:se:kth:diva-319751DOI: 10.1038/s41586-022-05035-yISI: 000838658900028PubMedID: 35922509Scopus ID: 2-s2.0-85135345083OAI: oai:DiVA.org:kth-319751DiVA, id: diva2:1701739
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QC 20221007

Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2022-10-07Bibliographically approved

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Lappalainen, Tuuli

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