Recommended resources to learn bioinformatics
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This is a list of some resources I find useful to conduct data analysis in Life Sciences Research, some of them more specific to the field of Genomics, Transcriptomics, and RNA-seq derived methods.
General programming
- Command Line Tools for Genomic Data Science by Liliana Florea. Great course to learn the most essential tools included in UNIX systems, unlocking a powerfull digital workbench.
- Algorithms for DNA Sequencing. A great course into key concepts of DNA sequence data analysis, which are useful as well for RNA-seq data analysis.
- The Data Scientist’s Toolbox. A course that introduces to a set of tools for scientific work in data analyis, centering in the programming language R, Rstudio, and Git’s version control. I highly recommend this course, especially if you plan to use R and Rstudio as your main workbench. Version control good practices will make your work more organized and keep track of changes to all your code; and Rmarkdown will show you that communicable and reproducible scientific results are possible and should be painless.
R
- R programming by Roger D. Peng. A nicely paced introductory course to R. After this course you will be able to use R for many basic data analyisi tasks
- You can also find hiss ebooks at Leanpub, (Beginner lvl.).
R for data science by Hadley Wickham & Garrett Grolemund. An amazing guide on how to explore, manipulate, program, model, and communicate for Data Science, (Intermediate lvl.).
R packages by Hadley Wickham A great practical reference to creating R packages using the R package devtools. This book alone is what allowed me to create my first R package txtools.
- Advanced R.
Bioconductor
- Rsamtools
- GenomicAlignments
- txtools. This is a package I developed to retrieve count data from alignment files (BAM) to perform analysis that requires single-nucleotide resolution.
Bioinformatics
Just found this nice Introduction to Bioinformatics and Computational Biology. It is a collection of videos of lectures in bioinformatics and statistics for Harvard students. The lecturers are from top institutes (Harvard, Broad, etc) And it seems very well structured.
Unix
- Make: a minimal tutorial on make
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