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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
seeQTL: a searchable database for human eQTLs
Kai Xia1, Andrey A Shabalin, Shunping Huang
1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. kxia@bios.unc.edu
Summary:
seeQTL is a comprehensive and versatile eQTL database, including various eQTL studies and a meta-analysis of HapMap eQTL information. The database presents eQTL association results in a convenient browser, using both segmented local-association plots and genome-wide Manhattan plots.
Availability And Implementation:
seeQTL is freely available for non-commercial use at http://www.bios.unc.edu/research/genomic_software/seeQTL/.
Contact:
fred_wright@unc.edu; kxia@bios.unc.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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