BOL

https://bio7.org/home-2/ - The application Bio7 is an integrated development environment for ecological modeling, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge...

www.phrap.org - Supports Illumina, 454, other Next-Gen and Sanger Reads and allows mixtures of these read types Consed includes BamScape which can view bam files with unlimited numbers of reads. BamScape can bring up consed to edit reads and the reference sequence...

modeloriented.github.io - The modelStudio package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site. It...

github.com - Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. Run pip...

www.well.ox.ac.uk - Platypus is a tool designed for efficient and accurate variant-detection in high-throughput sequencing data. By using local realignment of reads and local assembly it achieves both high sensitivity and high specificity. Platypus can detect...

R package for visualising GO enrichment results as wordclouds is available at CRAN

Upcoming Webinar: Fast Semi-Parallel Linear and Logistic Regression for Genome-Wide Association Studies

There are many R software and bioconductor packages for NGS data analysis, some of them are as follows Biostrings The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It...

http://www.rstudio.com/ - RStudio IDE is a powerful and productive user interface for R. It’s free and open source, and works great on Windows, Mac, and Linux. The developers and expert trainers are the authors of several popular R packages, including ggplot2, plyr,...

For a beginner this can be is the hardest part, it is also the most important to get right. It is possible to create a vector by typing data directly into R using the combine function ‘c’ x same as x creates the vector x...