If you are a complete beginner, don’t aim to ‘understand’ everything discussed in a course or lecture or book. It’s okay to be partially ignorant but still moving forward. Try to go through 60-70% content of
1. Bioinformatics Methods I and II, offered by Toronto University in massive-open-online-couse (MOOC) Coursera.org has pretty good materials (video+tutorial). One can download the course materials from this Google drive folder.
2. On Shikkhok.com, a MOOC platform in Bengali language, there is a very short course on Bioinformatics, বায়োইনফরমেটিক্স পরিচিতি, offered by Bio-Bio-1 Foundation.
3. Reading books is the best way. I’ve found ‘Essential Bioinformatics’ by Jin Xiong an easy to understand book.
1. Start reading computational biology/bioinformatics-related research papers. It’s
The journal Nature has a series of educational articles where experts describe different concepts in Bioinformatics, Statistics and Data Visualizations. Dr. Xianjun Dong from Harvard University has compiled an index of those papers in a PDF document. I encourage everyone to use this resource as a syllabus.
- Codecademy.com has
- This site contains several slides at its very bottom section ‘Introduction to Programming for Bioinformatics in Python’. I actually
learntPython from these slides. Just write the commands and try to get sameanswers, do the exercise. It’s very easy to understand.
- Rosalind.info is a site where one can learn and improve his/her skill
in bioinformaticsprogramming. You can learn python in it’s ‘Python Village’ section. After that, I suggest solving problems in ‘Bioinformatics Stronghold’. The structure of Rosalind.info is very interesting. Initially, the problems will be easy. But as you start to solvethem, the problems will be harder.
Well, have a look into Rosalind Country Ranking, Bangladesh is currently in the 3rd position world-wide!
3. Learn R. More on this later.
I have used different packages like DESeq2, EdgeR, Limma to do
Video Explaining RNA-Seq Normalization Methods
- A Gentle Introduction to RNA-seq
- A Gentle Introduction to ChIP-seq
- edgeR, part1: Library Normalization
- DESeq2, part1: Library Normalization
- edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
- RNA-seq – The Problem with Technical Replicates
- RPKM, FPKM, and TPM
Pipeline for doing RNA-Seq analysis
Also there are some pipe-line I have followed for doing the analysis. Here’s some link to them:
- From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline
- DESeq2 analysis template in R
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
[Updated: April 8, 2019]