A simple use-case comparing Blast2GO with R chunks The Blast2GO feature “Pairwise Differential Expression Analysis” is designed to perform differential expression analysis of count data arising from RNA-seq technology. This tool allows the identification of differential expressed genes considering two different conditions based on the software package ‘edgeR’, which belongs to the Bioconductor project. This use case shows the basic
This use case shows how to perform a functional analysis of pancreatic cancer expression data with Blast2GO. The performed steps are explained more in detail with short video tutorial linked to each section. Malignant (PDAC), benign (CP) and normal tissue (NP) gene lists are compared against each other. The human genome functional annotation data is retrieved via BioMart and used
Introduction You have: Newly aligned genome of a bacterial non-model organism. You want: Perform functional annotation and analysis of its potential proteins. You need: Predict all potential genes or coding regions before proceeding to the functional annotation: Gene-Finding How can this be done? Use Glimmer, a set of algorithms which uses interpolated Markov models to distinguish coding from non-coding DNA in bacteria, archaea, and viruses. Glimmer
(This feature is now obsolete.) In the following article, I will explain how to set up an EC2 instance with the Blast+ AMI. This NCBI webpage takes us to the most recent Blast+ AMI in the AWS Marketplace, which we want to configure after hitting “Continue” on the right-hand side. We can now play around with the Region and EC2
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