Evidence map›Paper›PMID 38183205›Full record

ArticleNucleic acids research2024

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Max Schubach, Thorben Maass, Lusiné Nazaretyan, Sebastian Röner, Martin Kircher

Abstract read
In one paragraph

Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 297 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
297citing papers in PubMed, 2 pooled it
–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

297 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. AlphaGenome Atlas:medRxiv : the preprint server for health sciences · 2026
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237 more citing papers are in PubMed but not listed here.

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

5 authors.

Max SchubachExploratory Diagnostic Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0002-2032-6679
Thorben MaassInstitute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.ORCID 0000-0003-4764-2740
Lusiné NazaretyanExploratory Diagnostic Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0001-5820-4143
Sebastian RönerExploratory Diagnostic Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0002-8578-1269
Martin KircherExploratory Diagnostic Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0001-9278-5471

Funding

Massively parallel characterization of variants and elements impacting transcriptional regulation in dynamic cellular systemsUM1HG011966 · NHGRI · UNIVERSITY OF WASHINGTON · PI Nadav Ahituv, Jay Ashok Shendure · 2021 to 2026
$9.7M
NHGRI NIH HHS 1UM1HG011966-01NHGRI NIH HHS UM1 HG011966
6 · The paper itself

Abstract

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

Indexed as

Genetic VariationGenome, HumanMachine LearningSoftwareHumansNucleotidesNucleotides

Identifiers

PMID38183205
PMCPMC10767851

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.