Bioinformatics Tools
The Department of Immunology provides access to many software tools which can aid researchers in the analysis of their data. Some tools are open-source while others are restricted to specific computers in the IT Suite in NRB 933.
CLC Genomics Workbench:
User-friendly bioinformatics software solutions allow for comprehensive analysis of your NGS data, including whole genome and transcriptome de novo assembly, targeted resequencing analysis, variant calling, ChIP-seq and DNA methylation (bisulfite sequencing analysis).RNA-seq and small RNA (miRNA, lncRNA) transcriptomics workflows for differential expression analysis at gene and transcript levels.
Cell Profiler:
CellProfiler is free, open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically.
ImageJ:
ImageJ is an open source image processing program designed for scientific multidimensional images.ImageJ is highly extensible, with thousands of plugins and scripts for performing a wide variety of tasks, and a large user community.r
Seurat:
Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.
Cell Ranger:
Cell Ranger is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate feature-barcode matrices and perform clustering and gene expression analysis.
Deseq2:
An R package to estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
DNARchitect:
A web application developed in house, used to analyze connectivity within nuclear DNA through the visualization of 2D loops and networks.
RNA-seq Processing Pipeline:
We have several different pipelines in place to help you process your RNASeq data. These range from the use of QC tools such as FastQC, to mapping with STAR aligner, and with the use of differential expression tools such as DESeq and or EdgeR. We also have pipelines in place to help with the processing of Single Cell RNA-seq data. These run on the O2 computing cluster.