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broadinstitute.org

The Broad Institute’s mission is to understand the roots of disease and close the gap between new biological insights and impact for patients. Here’s how our research — much of it federally funded — is improving human health and accelerating biomedical discoveries.

genviz.org

Differential expression analysis is used to identify differences in the transcriptome (gene expression) across a cohort of samples. Often, it will be used to define the differences between multiple biological conditions (e.g. drug treated vs. untreated samples). There are many, many tools available to perform this type of analysis. In this course we will rely on a popular Bioconductor package DEseq2. We will then make various visualizations to help interpret our results. Dataset For this analysis we will use the RNAseq data obtained from the EBI Expression Atlas (GXA). Specifically data set E-GEOD-50760 which corresponds to PMID: 25049118. This data consists of 54 samples from 18 individuals. Each individual has a primary colorectal cancer sample, a metastatic liver sample, and a normal sample of the surrounding colonic epithilium. The quantification data required to run differential expression analysis using DEseq2 are raw readcounts for either genes or transcripts. We will use the output from HTseq as a starting point. The datafiles were originally downloaded from the GXA resource for use in this exercise (but we provide download commands below from within R): Raw counts data from here:E-GEOD-50760 raw counts Sample information from here: E-GEOD-50760 sample info. A full description of the experimental design can be found at array express and the expression atlas. How DEseq2 works DEseq2 is a popular differential expression analysis package available through Bioconductor. Its differential expression tests are based on a negative binomial generalized linear model. To get started we will first need to install the package and load the library. # Install the latest version of DEseq2 if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DESeq2", version = "3.8") # load the library library(DESeq2) Input data Input data for DEseq2 consists of non-normalized sequence read counts at either the gene or transcript level.

scilifelab.github.io

a curated list of useful and actively maintained projects at scilifelab. you can find code ranging from simple bioinformatics resources and general programming tools right through to complete data analysis pipelines.

htslib.org

samtools

itol.embl.de

phylogenetic tree viewer and annotation tool

kbase.us

Developed for bench biologists and bioinformaticians, The Department of Energy Systems Biology Knowledgebase (KBase) is a software and data science platform designed to meet the grand challenge of systems biology: predicting and designing biological function.