Evidence map›Paper›PMID 36018788›Full record

ArticleNucleic acids research2022

PRIESSTESS: interpretable, high-performing models of the sequence and structure preferences of RNA-binding proteins.

Kaitlin U Laverty, Arttu Jolma, Sara E Pour, Hong Zheng, Debashish Ray, Quaid Morris, Timothy R Hughes

Abstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kaitlin U LavertyDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.
Arttu JolmaDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.
Sara E PourDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.
Hong ZhengDonnelly Centre, University of Toronto, Toronto, Canada.
Debashish RayDonnelly Centre, University of Toronto, Toronto, Canada.
Quaid MorrisDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.
Timothy R HughesDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.ORCID 0000-0002-8721-4719

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Systematic characterization of unconventional RNA binding proteinsR01HG008613 · NHGRI · UNIVERSITY OF TORONTO · PI BLENCOWE, BENJAMIN, GREENBLATT, JACK · 2015 to 2017
$1.0M
CIHR FDN-148403CIHR PJT-162255NCI NIH HHS P30 CA008748NHGRI NIH HHS R01 HG008613
6 · The paper itself

Abstract

Modelling both primary sequence and secondary structure preferences for RNA binding proteins (RBPs) remains an ongoing challenge. Current models use varied RNA structure representations and can be difficult to interpret and evaluate. To address these issues, we present a universal RNA motif-finding/scanning strategy, termed PRIESSTESS (Predictive RBP-RNA InterpretablE Sequence-Structure moTif regrESSion), that can be applied to diverse RNA binding datasets. PRIESSTESS identifies dozens of enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. Importantly, these core motifs are easily visualized and interpreted, and provide a measure of RBP secondary structure specificity. We used PRIESSTESS to interrogate new HTR-SELEX data for 23 RBPs with diverse RNA binding modes and captured known primary sequence and secondary structure preferences for each. Moreover, when applying PRIESSTESS to 144 RBPs across 202 RNA binding datasets, 75% showed an RNA secondary structure preference but only 10% had a preference besides unpaired bases, suggesting that most RBPs simply recognize the accessibility of primary sequences.

Indexed as

AlgorithmsRNA-Binding ProteinsBinding SitesNucleotide MotifsProtein BindingRNARNARNA-Binding Proteins

Identifiers

PMID36018788
PMCPMC9638913

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.