Genomics

During my daily work with R for genomic data analysis, I encountered several instances that R gives me some (bad) surprises. 1. The devil 1 and 0 coordinate system read detail here https://github.com/crazyhottommy/DNA-seq-analysis#tips-and-lessons-learned-during-my-dna-seq-data-analysis-journey some files such as bed file is 0 based. Two genomic regions: chr1 0 1000 chr1 1001 2000 when you import that bed file into R using rtracklayer::import(), it will become chr1 1 1000 chr1 1002 2000 The function convert it to 1 based internally (R is 1 based unlike python).

Googling is a required technique for programmers. Once I have a programming problem in mind, the first thing I do is to google to see if other people have encountered the same problem and maybe they already have a solution. Do not re-invent the wheels. Actually, reading other people’s code and mimicing their code is a great way of learning. Today, I am going to show you how to compute binned averages/sums along a genome or any genomic regions of interest.

readings links: http://yeolab.github.io/onboarding/geo.html http://www.hildeschjerven.net/Protocols/Submission_of_HighSeq_data_to_GEO.pdf https://www.ncbi.nlm.nih.gov/geo/info/submissionftp.html 1. create account Go to NCBI GEO: http://www.ncbi.nlm.nih.gov/geo/ Create User ID and password. my username is research_guru I used my google account. 2. fill in the xls sheet Downloaded the meta xls sheet from https://www.ncbi.nlm.nih.gov/geo/info/seq.html

bgzip the fastqs cd 01seq find *fastq | parallel bgzip md5sum *fastq.gz > fastq_md5.txt # copy to excle cat fastq_md5.txt | awk ‘{print $2}’ #copy to excle cat fastq_md5.