UVA Research Computing

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/tag/bioinformatics

  • Center for Diabetes Technology PriMed

    In their research around constant glucose monitoring and the automated maintenance of insulin for patients, the CDT is exploring data drawn from external data sources such as DexCom and FitBit. RC has assisted the CDT by designing a secure computing footprint in Amazon Web Services to pull in these data, parse and process them, in order to perform deeper analytics through machine learning. In January 2018, CDT sponsored a ski camp at Wintergreen Resort for a group of youth diagnosed with Type I diabetes with the goal of importing glucose, insulin, and exercise metrics at the end of each day through remote web APIs.
  • epihet

    RC is working with researchers in the Center for Public Health Genomics to write an R package to calculate Relative Proportion of Sites with Intermediate Methylation (RPIM) scores, which represent the epigenetic heterogeneity in a bisulfite sequencing sample. https://github.com/databio/epihet PI: Nathan Sheffield (Center for Public Health Genomics)
  • LOLAweb

    The past few years have seen an explosion of interest in understanding the role of regulatory DNA. This interest has driven large-scale production of functional genomics data resources and analytical methods. One popular analysis is to test for enrichment of overlaps between a query set of genomic regions and a database of region sets. In this way, annotations from external data sources can be easily connected to new genomic data. SOM Research Computing is working with faculty in the UVA Center for Public Health Genomics to implement LOLAweb, an online tool for performing genomic locus overlap annotations and analyses. This project, written in the statistical programming language R, allows users to specify region set data in BED format for automated enrichment analysis.
  • Microbiome Analysis of Hospital Sink Drains

    Sink drains are notoriously characterized as reservoirs of pathogens causing nosocomial transmissions in hospitals worldwide. Outbreaks where sinks have been implicated as source of antibiotic resistant bacteria have upsurged over the last few years. To understand transmission dynamics University of Virginia School of Medicine has established a unique “Sink Lab” for this research. This one-of-the kind laboratory establishes UVa as worldwide frontrunners in investigating sink related antibiotic resistant bacteria and how they spread. RC is working with the UVa Sink Lab for genomic analysis of the sink biomass. RC is contributing to: Comparative genomic analysis of gram-negative bacterial isolates: The analysis aims at tracking the mobile genetic element blaKPC gene, which encodes for Klebsiella pneumoniae carbapenemase (KPC) enzyme that confers resistance to all beta lactam agents including penicillins, cephalosporins, monobactams and carbapenems.
  • simpleCache

    In partnership with researchers in the Center for Public Health Genomics, School of Medicine Research Computing has contributed to the development of a novel package for computationally efficient caching and loading of data in R. simpleCache provides an interface to a series of functions to store and retrieve cached objects, including in the context batch processing or HPC environments. The package further extends base R functionality of saving and loading external representations of objects by enabling caching to pre-defined directories and timed cache operations. RC helped document and develop new functions for the package ahead of its release to the Comprehensive R Archive Network (CRAN).
  • Bioinformatics Packages on Ivy Linux VM

    Available Packages The following bioinformatics packages are available on the Ivy Linux Virtual Machines Bowtie2 Bowtie2 is a memory-efficient tool for aligning short sequences to long reference genomes. For bowtie2 usage information, please click here HISAT2 HISAT2 is a fast and sensitive tool for aligning short reads against the general human population (as well as single reference genome) * Requires approval before installation For HISAT2 usage information, please click here
  • Bioinformatics Packages on Windows VM

    Available Packages The following bioinformatics packages are available on the Windows Virtual Machines Bowtie2 For more information on bowtie2, please click here HISAT2 Requires approval before installation. For more information on HISAT2, please click here
  • Bowtie2 on Ivy Linux VM

    Bowtie2 is a memory-efficient tool for aligning short sequences to long reference genomes. It indexes the genome using FM Index, which is based on Burrows-Wheeler Transform algorithm, to keep its memory footprint small. Bowtie2 supports gapped, local and paired-end alignment modes. Alignment to a known reference using Bowtie2 is often an essential first step in a myriad of NGS analyses workflows. Bowtie2 Usage Alignment using bowtie2 is a 2-step process - indexing the reference genome, followed by aligning the sequence data. Create indexes of your reference genome of interest stored in reference.fasta file: bowtie2-build [option(s)] <reference.fasta> <bt2-index-basename> This will create new files with the provided basename and extensions .
  • Bowtie2 on Ivy Windows VM

    Bowtie2 is a memory-efficient tool for aligning short sequences to long reference genomes. It indexes the genome using FM Index, which is based on Burrows-Wheeler Transform algorithm, to keep its memory footprint small. Bowtie2 supports gapped, local and paired-end alignment modes. Alignment to a known reference using Bowtie2 is often an essential first step in a myriad of NGS analyses workflows. Bowtie2 Usage Alignment using bowtie2 is a 2-step process - indexing the reference genome, followed by aligning the sequence data. Create indexes of your reference genome of interest stored in reference.fasta file: bowtie2-build [option(s)] <reference.fasta> <bt2-index-basename> This will create new files with the provided basename and extensions .
  • HISAT2 on Ivy Linux VM

    * Please note that HISAT2 requires approval prior to installation on the VM HISAT2 is a fast and sensitive tool for aligning short reads against the general human population (as well as single reference genome). It indexes the genome using a Hierarchical Graph FM Index (HGFM) strategy, i.e. a large set of small indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp). HISAT2 Usage: Alignment using HISAT2 is a 2-step process - indexing the reference genome, followed by aligning the sequence data. Create indexes of your reference genome of interest stored in reference.
  • HISAT2 on Ivy Windows VM

    * Please note that HISAT2 requires approval prior to installation on the VM HISAT2 is a fast and sensitive tool for aligning short reads against the general human population (as well as single reference genome). It indexes the genome using a Hierarchical Graph FM Index (HGFM) strategy, i.e. a large set of small indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp). HISAT2 Usage: Alignment using HISAT2 is a 2-step process - indexing the reference genome, followed by aligning the sequence data. Create indexes of your reference genome of interest stored in reference.
  • Bioinformatics Resources on Rivanna

    UVA research community has access to numerous bioinformatics software installed and ready-to-use on Rivanna. They are all available via the LMod module system. In addition, Click here for a comprehensive list. 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.
  • Bioinformatics User Guides

    Bioinformatics on Rivanna UVA’s High-performance Computing Cluster All faculty, research staff and graduate students of UVA have access to Rivanna, university’s high-performance computing system with 290+ compute nodes (6500+ cores) for high-throughput multithreaded jobs, parallel jobs as well as memmory intensive large-scale data analyses. The architecture is specifically suited for large scale distributed genomic data analysis, with 100+ bioinformatics software packages installed and ready to use. Learn more Bioinformatics using FireCloud FireCloud Home