Supplementary materials and scripts.
Release: https://doi.org/10.5281/zenodo.1309979
link to NCBI prokaryote tables: https://www.ncbi.nlm.nih.gov/genome/browse#!/prokaryotes/
link to genome reports FTP: ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/
- Download the genome report file for all all of prokaryotes
# download the file
wget "ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/prokaryotes.txt"Column 2: Taxonomy information, Column 20: Reference Genome flag, and Column 21: ftp download path
All genomes and annotation files denoted "REPR" in column 20 were downloaded following the ftp links.
The script gene_extractor.py was used to extract genes sequences from genomes based on gff annotations.
Annotations for 16S rRNA and each housekeeping genes used in this study are hard coded into the script. For example, the 16S genes must contain "16S" in the annotation and be of type ("Rrna", "rrna","rRNA").
- V4 dataset specific method
The V4 region of the 16S rRNA was extracted in silico using fasta_amplify
- MLSA dataset specific method
Single-copy genes used in the MLSA-like dataset were concatenated together using concat_housekeeping_genes.py
The script variation_stats.py was used to output gene copy number, lengths, and variation stats from each genome.
Marker gene clustering was completed using uclustPipeline.py
UCLUST is available from https://drive5.com/usearch/manual/uclust_algo.html
Parsing clustering data was completeed using parse_uclust.py
Python scripts beginning with "clustering" were used to construct input tables for "marker gene clustering" figures (currently Figure 4).
Given a list of genomes to compare the script auto_orthofinder.py runs orthofinder on all genomes.
The program orthofinder.py is available from https://github.com/davidemms/OrthoFinder
The script orthofinder_parse_for_percent_shared_genes.py is used to parse orthofinder output and provide a table of percent shared genes.
Given parsed output from "Sequencing clustering" and "Percent shared genes" the script combine_ortho_and_uclust.py constructs a table with the columns percent identity, and percent shared genes.
All scripts used to construct figures and data tables for the functional analysis are available in the functional_methods/