Comparative_genomics

Here you will find some util scripts useful for comparative genomics analysis.

1. Obtaining the core and pangenome from genomes assemblies wiht pan_core_matrix.annotated.sh.

-Pipeline uses external dependencies:

Prokka (https://github.com/tseemann/prokka). It uses prokka for local gen prediction and annotation.
get_homologues (https://github.com/eead-csic-compbio/get_homologues). It uses get_homologues for orthologs obtaining and comparison for getting core and pan-genome matrices.

Make sure that these programs are installed and are in your system's executable search path. To test, in a terminal type:

      prokka -h
      get_homologues.pl -h

Running:

Download Comparative_genomics

    git clone https://github.com/avera1988/Comparative_genomics.git

Make executable all scripts

      cd Comparative_genomics/scripts
      chmod +x *.*

Obtaining core and pangenome matrix:

    ./pan_core_matrix.annotated.sh
    usage: ./pan_core_matrix.annotated.sh extention_file num_cpus path_to_comparative_genomics_scripts

This will result in a folder with all core genome genes from analyzed genomes.

2. Average Aminoacid Identity (AAI) Matrix using sigle copy orthologous genes.

-External dependencies

enveomics/aai.rb (https://github.com/lmrodriguezr/enveomics) For AAI pairwise comparisson.

-For obtaining single copy orthologous you can use the following bash script.

      ./single.copy.sh
      usage: ./single.copy.sh tmp number_of_genomes_used_in_get_homologues output_dir_for_single_copy_genes

-To oabtain the single copy orthologs genes for each genome use

          ./getcore.sh
          usage: /home/avera/bin/Comparative_genomics/scripts/getcore.sh list

-AAI comparisson: aai.sh

  ./aai.sh
  usage: ./aai.sh core_gen_extention_file cpu path_to_enveomics_scripts

-Matrix generation

    ./matrix.sh
    usage: ./matrix.sh list

For a Heatmap we can use the following simple R code:

        library("gplots")
        table <- read.table(file="matrix_aai_core.csv", header=TRUE, sep=",")
        mat_dat <- data.matrix(table[,2:ncol(table)])
        rnames <- table[,1]
        rownames(mat_dat) <-rnames
        heatmap.2(as.matrix(mat_dat)), cellnote=round(as.matrix(mat_dat),2), main="AAI core genome.", notecol="black", density.info="none", trace="none", dendrogram="row", margins=c(25,30), lhei = c(1,5))
        dev.off()