Usage

The repository BashDataManagement provides key elements and training exercises to manage genomics data and dataframes through Bash (Bourne Again Shell) in the framework of an imaginary collaborative project between lab units dealing with fictitious input data.

• The Bash script BashDataManagement.sh aims at presenting the most useful Bash commands to manage genomics data and dataframes from different sources.
• The R script BashDataManagement.R aims at performing statistical analyses from the output of the Bash script BashDataManagement.sh.

Dependencies

Bash commands

• awk (Alfred Aho, Peter Weinberger, Brian Kernighan)
• cat (concatenate)
• cut (cut)
• cd (change current working directory)
• chmod (change mode)
• cp (copy)
• dos2unix (Unix/DOS text file format converter)
• echo (prints text to the terminal)
• for (for)
• gedit (text editor)
• git (git)
• grep (global regular expression print)
• head (head)
• hexdump (hexadecimal viewer)
• join (join)
• ls (list)
• mkdir (make directory)
• more (more)
• mv (move)
• pwd (print working directory)
• rm (remove)
• sed (stream editor)
• sort (sort)
• scp (secure copy protocol)
• ssh (secure shell)
• touch (update the access date)
• tr (translate)
• unzip (extract files from a ZIP archive)

R libraries

• ggplot2
• ggpmisc
• plyr
• reshape2

Objectives

The objectives of three laboratory units aim at answering the questions below.

• Is there a correlation between the expected and estimated depth of coverage?
• Are there significant differences between GC% of genome types?

Available input

The three laboratory units provided genomics data and dataframes.

• The Lab unit 1 is a bacteriology unit providing a transposed dataframe of genome types (i.e. dataframe-unit1-transposed.tsv)
• The Lab unit 2 is a sequencing unit providing a dataframe of expected depth of coverage (i.e. dataframe-unit2.csv)
• The Lab unit 3 is a bioinformatics unit providing dataframes of estimated depth of coverage and GC content (i.e. files with the extension .unit3)

Launch the commands

Keeping in mind the objectives of the three lab units, the commands of each script can be launched one after the other in order to decipher each of its components.

From the Bash script opened with Gedit

gedit BashDataManagement.sh

From the R script opened with RStudio

rstudio BashDataManagement.R

Expected output

The expected conclusions are described below.

• There is a significant (p-value < 2.2e-16) correlation (R2=99.98%) between the expected and estimated depth of coverage
• There is not significant differences (p-value = 0.8125) of GC content between genomes of type A (38.132±0.013) and B (38.134±0.011)
• There is significant differences (p-value = 0.05791) of GC content between genomes of type C (37.910±0.012) and B (38.134±0.011)
• There is significant differences (p-value = 0.05447) of GC content between genomes of type C (37.910±0.012) and A (38.132±0.013)

Illustration

Reference

Project learning genomics for food safety (LEGO): https://www.learngenomics.eu/

Acknowledgment

My former colleagues with whom I learned a lot about Bash and R

Author

Nicolas Radomski