Day 3
Today I spent most of my time reading journals about RNA-seq and RNA-seq data analysis. I started the day by reading A Quick Guide to Organizing Computational Biology Projects which then pushed me to reorganize my repository; I changed codes folder to src (short for source codes) and I also added results and data folders for storing results and data of my project in the future.
I haven't decided what I'm going to do for the project but there's a high probability that I'm going to do RNA-seq data analysis of available p53 (tumor suppressor protein) -related series/data sets in GEO Database. At the moment I'm still learning how to perform RNA-seq data analysis and I will try to dive deeper this weekend.
Some of my-future-project-related-articles that I read today:
- p53 Tumor Suppressor Protein
- Primary Information of p53 Gene
- p21
- Peto's Paradox
- How to calculate Differential Gene expression between samples with replicates
- How To Calculate Differential Gene Expression In Rnaseq Experiments?
- Computational methods for transcriptome annotation and quantification using RNA-seq
- About GEO2R NCBI
- A comparison of methods for differential expression analysis of RNA-seq data
- Up-to-date RNA-Seq Analysis Training/Courses/Papers (Oct 2016)
I also re-installed r-studio for my Ubuntu because I believe I'm also going to need it for my project and installed some packages from Bioconductor that I found useful; genefilter, ballgown and edgeR.
Also the best part is I found this useful rna_seq tutorial repository on GitHub and they had their paper published too Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud
######tldr; today I spent most of my time reading rna-seq related articles