ArticleBMC research notes2017
Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale.
Article in BMC research notes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.Bioinformatics (Oxford, England) · 2024Article
- scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data.PLoS computational biology · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model.bioRxiv : the preprint server for biology · 2024Article
- scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data.bioRxiv : the preprint server for biology · 2024Article
- TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.Bioinformatics (Oxford, England) · 2023Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.Briefings in bioinformatics · 2021Article
- Mapping of cis-acting expression quantitative trait loci in human scalp hair follicles.BMC dermatology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveThe majority of sequence variants identified by Genome-wide association studies (GWASs) fall outside of the protein-coding regions. Unlike coding variants, it is challenging to connect these noncoding variants to the pathophysiology of complex diseases/traits due to the lack of functional annotations in the non-coding regions. To overcome this, by leveraging the rich collection of genomic and epigenomic profiles, we have developed DIVAN, or Disease/trait-specific Variant ANnotation, which enables the assignment of a measurement (D-score) for each base of the human genome in a disease/trait-specific manner. To facilitate the utilization of DIVAN, we pre-computed D-scores for every base of the human genome (hg19) for 45 different diseases/traits.
resultsIn this work, we present a detailed protocol on how to utilize DIVAN software toolkit to retrieve D-scores either by variant identifiers or by genomic regions for a disease/trait of interest. We also demonstrate the utilities of the D-scores using real data examples. We believe that the pre-computed D-scores for 45 diseases/traits is a useful resource to follow up on the discoveries made by GWASs, and the DIVAN software toolkit provides a convenient way to access this resource. DIVAN is freely available at https://sites.google.com/site/emorydivan/software .
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