Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Annotating pathogenic non-coding variants in genic regions.

Nature communications - 9 Aug 2017

Gelfman Sahar, Wang Quanli, McSweeney K Melodi, Ren Zhong, La Carpia Francesca, Halvorsen Matt, Schoch Kelly, Ratzon Fanni, Heinzen Erin L, Boland Michael J, Petrovski Slavé, Goldstein David B

Abstract excerpt

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Databases, Genetic
Share to Epilepsy

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Frequency

Share to Genetic Variation

Share to Introns

Share to Molecular Sequence Annotation

Source

Open in PubMed Open full text

Identifiers

PMID: 28794409
DOI: 10.1038/s41467-017-00141-2
PMCID: PMC5550444

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Annotating pathogenic non-coding variants in genic regions.PMID 28794409Read ↗

Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants.Semantic proximity Read ↗

A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect predictionSemantic proximity Read ↗

Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.Semantic proximity Read ↗

Classification of non-coding variants with high pathogenic impact.Semantic proximity Read ↗

Actionable exomic incidental findings in 6503 participants: challenges of variant classification.Semantic proximity Read ↗

Pathogenic variations illuminate functional constraints in intrinsically disordered proteinsSemantic proximity Read ↗

Variant annotation across homologous proteins (“Paralogue Annotation”) identifies disease-causing missense variants with high precision, and is widely applicable across protein familiesSemantic proximity Read ↗

DITTO: An explainable machine-learning model for transcript- specific variant pathogenicity predictionSemantic proximity Read ↗

AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity predictionSemantic proximity Read ↗

Classification of non-coding variants with high pathogenic impactSemantic proximity Read ↗

Critical assessment of missense variant effect predictors on disease-relevant variant dataSemantic proximity Read ↗

S-CAP extends clinical-grade pathogenicity prediction to genetic variants that affect RNA splicingSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.