R: R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
RStudio: RStudio is an active member of the R community.
Python: Python is a programming language that lets you work quicklyand integrate systems more effectively.
CUDA: CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
Shiny: Shiny is an R package that makes it easy to build interactive web apps straight from R.
Java: Java is a general-purpose computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible.
Perl: Perl is a family of high-level, general-purpose, interpreted, dynamic programming languages. The languages in this family include Perl 5 and Perl 6.
Matlab: MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment. A proprietary programming language developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, C#, Java, Fortran and Python.
GenomeSpace: GenomeSpace is a cloud-based interoperability framework to support integrative genomics analysis through an easy-to-use Web interface. GenomeSpace provides access to a diverse range of bioinformatics tools, and bridges the gaps between the tools, making it easy to leverage the available analyses and visualizations in each of them. The tools retain their native look and feel, with GenomeSpace providing frictionless conduits between them through a lightweight interoperability layer. More info can be found from the related paper: Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace
NGS WikiBook: a dynamic collaborative online training effort with long-term sustainability for next-generation sequencing (NGS) analysis. Nine rules to begin with NGS analysis: 1) Do not fear the command line; 2) Known the conventions; 3)Read introductory reviews; 4) Start with quality checking; 5) Plan for mistakes and document workflow; 6) Always get informed and get help if stuck; 7) Use an efficient integrative approach; 8) Avoid reinventing the wheel; 9) Education is important.
Wardrobe: BioWardrobe Experiment Management System, which allows users to store, visualize and analyze epigenomic and transcriptomic next-generation sequencing data using a biologist-friendly, web-based graphical user interface without the need for programming expertize.