Evidence map›Paper›PMID 41676484›Full record

ArticlebioRxiv : the preprint server for biology2026

MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores.

Runjia Li, Jason Ernst

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Runjia LiBioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.ORCID 0009-0009-6088-1042
Jason ErnstBioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.ORCID 0000-0003-4026-7853

Funding

1/2 Genomic Strategies to Identify High-impact Psychiatric Risk VariantsU01MH105578 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B., GESCHWIND, DANIEL H · 2014 to 2018
$8.5M
Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotencyU01HG012079 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Kathrin Plath · 2021 to 2026
$6.9M
Spatiotemporal epigenomic and chromosomal architectural cell atlas of developing human brainsU01MH130995 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Mercedes Paredes · 2022 to 2026
$4.4M
1/3 Building integrative CNS networks for genomic analysis of autismR01MH109912 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GESCHWIND, DANIEL H · 2016 to 2020
$2.6M
Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing DataDP1DA044371 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ERNST, JASON · 2017 to 2021
$2.3M
NHGRI NIH HHS U01 HG012079NIDA NIH HHS DP1 DA044371NIMH NIH HHS R01 MH109912NIMH NIH HHS U01 MH105578NIMH NIH HHS U01 MH130995
6 · The paper itself

Abstract

Many computational predictors of missense variant pathogenicity are available. To capture information across various predictors, we propose MissenseHMM, which learns states corresponding to combinatorial patterns of variant prioritizations. We applied MissenseHMM to 43 predictors, annotating over 70 million missense variants with 20 states that showed distinct predictor scores patterns, amino acid substitutions and other genomic annotation enrichments. MissenseHMM state annotations enhanced individual predictors' associations with clinical pathogenic variants and deep mutational scanning data, and also provided insight into the performances of various protein language models. Overall, MissenseHMM complements pathogenicity predictors and is an annotation resource for missense variant interpretation.

Indexed as

deep mutational scanningHidden Markov ModelsMissense variantsprotein language modelsvariant effect predictor

Identifiers

PMID41676484
PMCPMC12889460

What OpenQuestion holds

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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.