{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:43Z","timestamp":1772138023315,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32100533"],"award-info":[{"award-number":["32100533"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32370721"],"award-info":[{"award-number":["32370721"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021177","name":"Shenzhen Bay Laboratory","doi-asserted-by":"publisher","award":["SZBL2021080601001"],"award-info":[{"award-number":["SZBL2021080601001"]}],"id":[{"id":"10.13039\/501100021177","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular Quantitative Trait Loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for bulk and single-cell xQTLs data retrieval, quality control and pre-processing from public repositories and our integrated resource. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS summary statistics. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies. xQTLbiolinks is available at https:\/\/github.com\/lilab-bioinfo\/xQTLbiolinks.<\/jats:p>","DOI":"10.1093\/bib\/bbad440","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T09:01:01Z","timestamp":1699866061000},"source":"Crossref","is-referenced-by-count":20,"title":["xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs"],"prefix":"10.1093","volume":"25","author":[{"given":"Ruofan","family":"Ding","sequence":"first","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Xudong","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Yangmei","family":"Qin","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Lihai","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Xuelian","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Shouhong","family":"Guang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology , The First Affiliated Hospital of USTC, The USTC RNA Institute, Ministry of Education Key Laboratory for Membraneless Organelles & Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, Biomedical Sciences and Health Laboratory of Anhui Province, , Hefei, Anhui 230027, China"},{"name":"University of Science and Technology of China , The First Affiliated Hospital of USTC, The USTC RNA Institute, Ministry of Education Key Laboratory for Membraneless Organelles & Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, Biomedical Sciences and Health Laboratory of Anhui Province, , Hefei, Anhui 230027, China"}]},{"given":"Chen","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Cancer Research, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]},{"given":"Gao","family":"Wang","sequence":"additional","affiliation":[{"name":"The Gertrude H. Sergievsky Center and the Department of Neurology, Columbia University , NY 10032, USA"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China"}]}],"member":"286","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"2023120706043892200_ref1","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1038\/s41588-018-0148-2","article-title":"Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits","volume":"50","author":"Hormozdiari","year":"2018","journal-title":"Nat Genet"},{"key":"2023120706043892200_ref2","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1126\/science.aaz1776","article-title":"The GTEx consortium atlas of genetic regulatory effects across human tissues","volume":"369","author":"Consortium 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