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Record W3107659143 · doi:10.1038/s41467-020-19787-6

Direct RNA sequencing reveals m6A modifications on adenovirus RNA are necessary for efficient splicing

2020· article· en· W3107659143 on OpenAlexfundno aff
Alexander M. Price, Katharina E. Hayer, Alexa B. R. McIntyre, Nandan S. Gokhale, Jonathan S. Abebe, Ashley N. Della Fera, Christopher E. Mason, Stacy M. Horner, Angus C. Wilson, Daniel P. Depledge, Matthew D. Weitzman

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer InstituteSchool of Medicine, New York UniversityU.S. Department of Health and Human ServicesNational Institutes of HealthIstituto Italiano di TecnologiaUniversity of PennsylvaniaYork University
KeywordsRNARNA splicingComputational biologyBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Adenovirus is a nuclear replicating DNA virus reliant on host RNA processing machinery. Processing and metabolism of cellular RNAs can be regulated by METTL3, which catalyzes the addition of N6 -methyladenosine (m 6 A) to mRNAs. While m 6 A-modified adenoviral RNAs have been previously detected, the location and function of this mark within the infectious cycle is unknown. Since the complex adenovirus transcriptome includes overlapping spliced units that would impede accurate m 6 A mapping using short-read sequencing, here we profile m 6 A within the adenovirus transcriptome using a combination of meRIP-seq and direct RNA long-read sequencing to yield both nucleotide and transcript-resolved m 6 A detection. Although both early and late viral transcripts contain m 6 A, depletion of m 6 A writer METTL3 specifically impacts viral late transcripts by reducing their splicing efficiency. These data showcase a new technique for m 6 A discovery within individual transcripts at nucleotide resolution, and highlight the role of m 6 A in regulating splicing of a viral pathogen.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.317
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations211
Published2020
Admission routes1
Has abstractyes

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