Evidence map›Paper›PMID 41777615›Full record

ArticleHuman mutation2026

Assessment of In-Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning.

David E Rauch, Meng Wang, Muhammad Jafar Hussain Hafiz, Daniel C Brock, Yumei Li, Molly Marra, Mark E Pennesi, Paul Yang, Everett Lesley, Irma Lopez and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

14 authors.

David E RauchDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0009-0007-8388-7485
Meng WangDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0000-0003-2742-3559
Muhammad Jafar Hussain HafizDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0000-0001-5269-8971
Daniel C BrockDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0000-0002-9218-8594
Yumei LiDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0000-0002-4908-8459
Molly MarraMcGill Ocular Genetics Laboratory and Centre, Department of Paediatric Surgery, Human Genetics, and Ophthalmology, McGill University Health Centre, Montreal, Quebec, Canada, mcgill.ca.
Mark E PennesiDepartment of Ophthalmology, Oregon Health & Science University Casey Eye Institute, Portland, Oregon, USA.ORCID https://orcid.org/0000-0002-4943-6599
Paul YangDepartment of Ophthalmology, Oregon Health & Science University Casey Eye Institute, Portland, Oregon, USA.
Everett LesleyDepartment of Ophthalmology, Oregon Health & Science University Casey Eye Institute, Portland, Oregon, USA.ORCID https://orcid.org/0000-0002-7373-2056
Irma LopezMcGill Ocular Genetics Laboratory and Centre, Department of Paediatric Surgery, Human Genetics, and Ophthalmology, McGill University Health Centre, Montreal, Quebec, Canada, mcgill.ca.
Robert KoenekoopMcGill Ocular Genetics Laboratory and Centre, Department of Paediatric Surgery, Human Genetics, and Ophthalmology, McGill University Health Centre, Montreal, Quebec, Canada, mcgill.ca.
Edward Ryan CollantesBroad Institute, Cambridge, Massachusetts, USA, broadinstitute.org.ORCID https://orcid.org/0000-0003-2150-5844
Joanne BolinaoAmerican Eye Institute, Pasig, Philippines.
Rui ChenDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA, bcm.edu.ORCID https://orcid.org/0000-0002-4387-9735

Funding

Proteomics CoreP30EY010572 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI Kate E Keller · 1995 to 2026
$19.4M
VISION RESEARCH CENTERP30EY002520 · NEI · BAYLOR COLLEGE OF MEDICINE · PI Samuel M Wu · 1985 to 2026
$14.8M
MEASURES OF HUMAN RECEPTOR AND POST RECEPTOR ACTIVITYR01EY009076 · NEI · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI BIRCH, DAVID G, HOOD, DONALD C. · 1991 to 2021
$8.3M
GENETICS OF EARLY ONSET RETINAL DISEASESR01EY018571 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI RUI CHEN · 2008 to 2026
$7.2M
Molecular Basis of Human Visual System DisordersR01EY022356 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI RUI CHEN · 2012 to 2026
$6.1M
NEI UCI Center Core Grant for Vision ResearchP30EY034070 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Rachel Wagner Martin · 2022 to 2026
$3.7M
Mentored Vision Clinician-Scientist Program at OHSUK12EY027720 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI David Huang · 2018 to 2026
$2.2M
Antisense therapy for the treatment of visual loss in Usher syndromeR01EY030499 · NEI · LSU HEALTH SCIENCES CENTER · PI KOENEKOOP, ROBERT, LENTZ, JENNIFER JEAN · 2019 to 2023
$1.7M
High Throughput Genomic Sequencer at BCM Core FacilityS10OD023469 · OD · BAYLOR COLLEGE OF MEDICINE · PI CHEN, RUI · 2017 to 2017
$600k
NEI NIH HHS K12 EY027720NEI NIH HHS P30 EY002520NEI NIH HHS P30 EY010572NEI NIH HHS P30 EY034070NEI NIH HHS R01 EY009076NEI NIH HHS R01 EY018571NEI NIH HHS R01 EY022356NEI NIH HHS R01 EY030499NIH HHS S10 OD023469
6 · The paper itself

Abstract

The standard for in silico pathogenicity prediction of in-frame insertions and deletions (indels) is less established compared to other types of variations. We aimed to systematically assess the performance of in silico machine learning (ML) tools on a patient cohort with inherited retinal diseases (IRDs). The performance of four ML tools (CADD, FATHMM-indel, VEST4, and MetaRNN-indel) was compared. Among them, MetaRNN-indel showed the best overall results. MetaRNN-indel was then applied to 1013 unsolved IRD patients, identifying two likely pathogenic causal variants in two unrelated IRD patients by confirming clinical phenotypes. Hence, our findings indicate that reliable prediction of the pathogenicity of in-frame indels can be achieved using existing ML tools with proper evaluation and tuning.

Indexed as

INDEL MutationMachine LearningRetinal DiseasesCohort StudiesComputational BiologyComputer SimulationGenetic Predisposition to DiseaseHumansPhenotypein-frame indelin-silicoIRDmachine learningunsolved

Identifiers

PMID41777615
PMCPMC12951207

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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.