File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Ultrafast and scalable variant annotation and prioritization with big functional genomics data

TitleUltrafast and scalable variant annotation and prioritization with big functional genomics data
Authors
Issue Date2020
Citation
Genome Research, 2020, v. 31, n. 12, p. 1789-1801 How to Cite?
AbstractThe advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.
Persistent Identifierhttp://hdl.handle.net/10722/324509
ISSN
2023 Impact Factor: 6.2
2023 SCImago Journal Rankings: 4.403
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Dandan-
dc.contributor.authorYi, Xianfu-
dc.contributor.authorZhou, Yao-
dc.contributor.authorYao, Hongcheng-
dc.contributor.authorXu, Hang-
dc.contributor.authorWang, Jianhua-
dc.contributor.authorZhang, Shijie-
dc.contributor.authorNong, Wenyan-
dc.contributor.authorWang, Panwen-
dc.contributor.authorShi, Lei-
dc.contributor.authorXuan, Chenghao-
dc.contributor.authorLi, Miaoxin-
dc.contributor.authorWang, Junwen-
dc.contributor.authorLi, Weidong-
dc.contributor.authorKwan, Hoi Shan-
dc.contributor.authorSham, Pak Chung-
dc.contributor.authorWang, Kai-
dc.contributor.authorLi, Mulin Jun-
dc.date.accessioned2023-02-03T07:03:35Z-
dc.date.available2023-02-03T07:03:35Z-
dc.date.issued2020-
dc.identifier.citationGenome Research, 2020, v. 31, n. 12, p. 1789-1801-
dc.identifier.issn1088-9051-
dc.identifier.urihttp://hdl.handle.net/10722/324509-
dc.description.abstractThe advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.-
dc.languageeng-
dc.relation.ispartofGenome Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleUltrafast and scalable variant annotation and prioritization with big functional genomics data-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1101/gr.267997.120-
dc.identifier.pmid33060171-
dc.identifier.pmcidPMC7706736-
dc.identifier.scopuseid_2-s2.0-85097112939-
dc.identifier.volume31-
dc.identifier.issue12-
dc.identifier.spage1789-
dc.identifier.epage1801-
dc.identifier.eissn1549-5469-
dc.identifier.isiWOS:000596027700001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats