github.com - The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc...
github.com - Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is to process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!
Bactopia can be split into two main...
github.com - Mesquite is modular, extendible software for evolutionary biology, designed to help biologists organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics,...
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment,...
http://genometools.org/ - The GenomeTools genome analysis system is a free collection of bioinformatics tools (in the realm of genome informatics) combined into a single binary named gt. It is based on a C library named...
The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods...
github.com - ProteoClade is a Python library for taxonomic-based annotation and quantification of bottom-up proteomics data. It is designed to be user-friendly, and has been optimized for speed and storage requirements.
ProteoClade helps you analyze two...
master.bioconductor.org - Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified to the reference transcripts, and prepare gene-level count...
the sequenced reads can be mapped to the organism’s genes to assess how differently the genes are expressed under the experimental circumstances as opposed to the control scenario. This is known as differential expression (DE) analysis