The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity.
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
16 May 2023
16 May 2023
Historique:
pubmed:
9
6
2023
medline:
9
6
2023
entrez:
9
6
2023
Statut:
epublish
Résumé
The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3' end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3' processing are deployed across human tissues, with nearly half of multi-transcript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.
Identifiants
pubmed: 37292896
doi: 10.1101/2023.05.15.540865
pmc: PMC10245583
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NIA NIH HHS
ID : U01 AG046152
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG009380
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG061356
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG012367
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG017917
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG010161
Pays : United States
Organisme : NHGRI NIH HHS
ID : U24 HG009446
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG015819
Pays : United States
Organisme : NHGRI NIH HHS
ID : UM1 HG009443
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG072975
Pays : United States
Organisme : NHGRI NIH HHS
ID : U24 HG009397
Pays : United States
Organisme : NHGRI NIH HHS
ID : UM1 HG009382
Pays : United States