BOL

The Database for Annotation, Visualization and Integrated Discovery (DAVID ) are able to visualize genes on BioCarta & KEGG pathway maps. Other options for pathway-mining such as gene functional classification, functional annotation chart or clustering and functional annotation table are also available in DAVID.

http://david.abcc.ncifcrf.gov/

http://www.nature.com/nprot/journal/v4/n1/abs/nprot.2008.211.html

Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

Paper:

http://bioinformatics.oxfordjournals.org/content/29/14/1830.full

I would recommend python and lower level language C because of easier syntax and simple oops approach. Moreover, I do a lot of boinformatics programming, but I don’t use any of the “Bio*” projects, as they never seem to have much that is of use to me, and what little is useful to me is easier for me to code myself than to install any Bio* project and deal with its idiosyncracies.

Read this report:

http://qz.com/298635/these-programming-languages-will-earn-you-the-most-money/

Python is beating all others https://towardsdatascience.com/the-most-in-demand-skills-for-data-scientists-4a4a8db896db

R is an exceptionally powerful language in manipulation and transformation of data, statistical analysis and graphics. It doesn have support for the wide array of statistical functionality. So in my opinion R is great if all you're doing is statistics. It's got a nice interactive interface and visualization tools.

R discussion http://bioinformaticsonline.com/discussion/view/119/which-are-the-best-statistical-programming-languages-to-study-for-a-bioinformatician

You can also found several other material at http://bioinformaticsonline.com/groups/profile/93/r-and-bioconductor

My vote goes to R (http://cran.at.r-project.org) environment is an easy to use and write programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience.

R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use. R is really important to the point that it’s hard to overvalue it and also allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems. Therefore, my vote goes to R.