/tag/genomics
Bioinformatics & Genomics
UVA Research Computing (RC) can help with your bioinformatics project.
Next-generation sequence data analysis RC staff can help you start to use popular bioinformatics software for functions such as
Genome assembly, reference-based and/or de-novo Whole-Genome/Exome sequence analysis for variant calling/annotation RNA-Seq data analysis to quantify, discover and profile RNAs Mircobiome data analysis, including 16S rRNA surveys, OTU clustering, microbial profiling, taxonomic and functional analysis from whole shotgun metagenomic/metatranscriptomic datasets Epigenetic analysis from BSAS/ChIP-Seq/ATAC-Seq Computing Platforms UVA has three computing facilities available to researchers: Rivanna and Afton, for non-sensitive data, and Ivy, for sensitive data. In addition, cloud-based services offer a computing environment for running flexible, scalable on-demand applications.
Bioinformatics Resources and UVA HPC
The UVA research community has access to numerous bioinformatics software installed directly or available through the bioconda Python modules.
Click here for a comprehensive list of currently-installed bioinformatics software.
Popular Bioinformatics Software Below are some popular tools and useful links for their documentation and usage:
.tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#f0f0f0;} .tg .tg-hy9w{background-color:#eceeef;border-color:inherit;vertical-align:middle;} .tg .tg-dc35{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;} .tg .tg-hy9w-nw{background-color:#eceeef;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-dc35-nw{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-0qmj{font-weight:bold;background-color:#eceeef;border-color:inherit;vertical-align:middle;} .scroll thead, .scroll tbody {display: block} .scroll tbody {overflow-y: auto; height: 500px;} .scroll thead tr:after {content: ‘';overflow-y: scroll; visibility: hidden; height: 0;} Tool Version Description Useful Links BEDTools 2.
Cardiovascular Genomics
Coronary artery disease (CAD) is the major cause of morbidity and mortality worldwide. Recent genome wide association studies (GWAS) have revealed more than 50 genomic loci that are associated with increased risk for CAD. However, the pathological mechanisms for the majority of the GWAS loci leading to increased susceptibility to this complex disorder are still unclear. Many of the CAD loci appear to act through the vessel wall, presumably affecting smooth muscle cell (SMC) function.
UVA Research Computing (RC) is working with Redouane Aherrahrou from the Center for Public Health Genomics who aims to study the impact of the CAD-associated genetic factors on the cellular and molecular SMC phenotypes, as well as the underlying biological pathways that are perturbed by these genetic factors.
