Research Computing supports all UVA researchers who are interested in writing code to address their scientific inquiries. Whether these programming tasks are implemented interactively, in a series of scripts or as an open-source software package, services are available to provide guidance and enable collaborative development. RC has specific expertise in object oriented programming in Matlab, R, and Python.
Examples of service areas include:
Collaborating on package development Reviewing and debugging code Preparing scripts to automate or expedite tasks Developing web interfaces for interactive data exploration Advising on integration of existing software tools UVA has two local computational facilities available to researchers: Rivanna and Ivy.
UVA Research Computing (RC) can help with your bioinformatics project.
Next-generation sequence data analyis 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 two computing facilities available to researchers: Rivanna, 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.
Research Computing can help with accessing, preparing, visualizing and analyzing data. We can assist you by implementing your analysis strategy in an appropriate computing language, including R, Python and Matlab. We will work with you to prepare scripts that are reproducible, efficient and flexible.
Manipulation Data analysis generally involves a significant effort to transform, aggregate, subset or otherwise prepare a dataset. That could include dealing with missing values as well as merging or joining multiple datasets. We can help you wrangle your data into a “tidy” format in order to analyze the features and observations relevant to your question.
Image Processing and Scientific Visualization are two separate processes within the scientific research lifecycle, yet the two concepts often play off of one another. Image processing refers to the enhancement and transformation of images to prepare them for quantitative analysis. Scientific visualization is the graphical communication of data so that trends and anomalies can be more easily recognized. UVa Research Computing offers many services and resources to help researchers augment their work with image processing and scientific visualization techniques. Image Processing Overview Image processing encompasses a variety of techniques to prepare images for analysis. Researchers often need to remove noise artifacts from their imaging data, or they need to analyze particular regions of interest.