BOL

soap.genomics.org.cn - SOAPaligner/soap2 is a member of the SOAP (Short Oligonucleotide Analysis Package). It is an updated version of SOAP software for short oligonucleotide alignment. The new program features in super fast and accurate alignment for huge amounts of...

sourceforge.net - Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.Commonly used biological software and...

sourceforge.net - Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools. Commonly used biological...

github.com - netGO is an R/Shiny package for network-integrated pathway enrichment analysis.netGO provides user-interactive visualization of enrichment analysis results and related networks. Currently, netGO supports analysis for four species...

www.bioinf.jku.at - The kebabs package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define...

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...