Data Carpentry Genomics beta 2.0

These files are intended for use with the Data Carpentry Genomics curriculum (https://datacarpentry.org/genomics-workshop/). Files will be useful for instructors teaching this curriculum in a workshop setting, as well as individuals working through these materials on their own.


This curriculum is normally taught using Amazon Web Services (AWS). Data Carpentry maintains an AWS image that includes all of the data files needed to use these lesson materials. For information on how to set up an AWS instance from that image, see https://datacarpentry.org/genomics-workshop/setup.html. Learners and instructors who would prefer to teach on a different remote computing system can access all required files from this FigShare dataset.


This curriculum uses data from a long term evolution experiment published in 2016: Tempo and mode of genome evolution in a 50,000-generation experiment (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988878/) by Tenaillon O, Barrick JE, Ribeck N, Deatherage DE, Blanchard JL, Dasgupta A, Wu GC, Wielgoss S, Cruveiller S, Médigue C, Schneider D, and Lenski RE. (doi: 10.1038/nature18959). All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA294072 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294072).


backup.tar.gz: contains original fastq files, reference genome, and subsampled fastq files. Directions for obtaining these files from public databases are given during the lesson https://datacarpentry.org/wrangling-genomics/02-quality-control/index.html). On the AWS image, these files are stored in ~/.backup directory. 1.3Gb in size.


shell_data.tar.gz: contains the files used as input to the Introduction to the Command Line for Genomics lesson (https://datacarpentry.org/shell-genomics/).


sub.tar.gz: contains subsampled fastq files that are used as input to the Data Wrangling and Processing for Genomics lesson (https://datacarpentry.org/wrangling-genomics/). 109Mb in size.


solutions: contains the output files of the Shell Genomics and Wrangling Genomics lessons, including fastqc output, sam, bam, bcf, and vcf files.


vcf_clean_script.R: converts vcf output in .solutions/wrangling_solutions/variant_calling_auto to single tidy data frame.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px 'Andale Mono'; color: #29f914; background-color: #000000} span.s1 {font-variant-ligatures: no-common-ligatures}


combined_tidy_vcf.csv: output of vcf_clean_script.R