MTREE

A program to predict interactions using mirror trees.
        Created by Sergio Castillo, Joan Martí & Adrià Pérez

Install mtree

Move to the root directory of the program and run these commands:

Usage of mtree

To see all the available options run mtree with the option -h

mtree -i input.fa -db uniprot.fa --data datafolder -c 0.8 -sp 8

Output of the program

The program outputs a tabular file to the specified path (using the option -o). This file is of the following form:

SEQ1	SEQ2	Pearson	Spearman	r_Adjusted	Type
CLOCK	BMAL	0.7	0.6	0.8	Int
EGFR	TNFA	0.3	0.2	0.4	NonInt

Testing the program

Downloading the databases

mtree needs a database of FASTA sequences to work. We used Swiss-Prot. Also, for training purposes, we used INTACT and NEGATOME. These two are NOT necessary for the program to work, but we used them to evaluate our method.

Create directory

mkdir db

Download uniprot

wget ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz \
     -O db/uniprot_sprot.dat.gz

Subsetting the database

Because we are always working with animals we filtered the database. This is NOT required.

Downloading the animals list

Go to the website and download all the animal species names. Save it as animals.txt This step is NOT necessary. It was only useful for testing.

• http://www.ncbi.nlm.nih.gov/genome/browse/

Filter the FASTA

zcat db/uniprot_sprot.dat.gz | \
perl -e '
%specs = ();
open $fh, "<animals.txt ";
while(<$fh>) {
    chomp;
    $specs{$_} = 1;
};
local $/ = ">";
while(<>){
    chomp;
    ($name, @seq) = split /\n/;
    $sp = "";
    if ($name =~ m/OS=(.*?) GN/) {
        $sp = $1;
    } else {
        next
    };
    if (not exists $specs{$sp}){
        next;
    }  
    print ">$name\n", join("\n", @seq), "\n";
} ' > db/animals_uniprot.fa

Create testing sets

We needed a set of interacting proteins and a set of non-interacting proteins. We downloaded the former from INTACT, and the latter from Negatome

Download INTACT

wget ftp://ftp.ebi.ac.uk/pub/databases/intact/current/psimitab/intact.txt -O db/intact.tbl

Download NEGATOME

wget http://mips.helmholtz-muenchen.de/proj/ppi/negatome/manual_stringent.txt -O db/negatome.tbl

# Get some FASTAs from negatome
python3 bin/create_sets.py -i db/NonInt.tbl -db db/animals_uniprot.fa -o 300_negatome.fa --num 300

Get the alignment of 16S rRNAs for enhancing the correlations

wget http://www.arb-silva.de/fileadmin/silva_databases/release_123/Exports/SILVA_123_LSURef_tax_silva_full_align_trunc.fasta.gz -O db/SILVA_123_LSURef_tax_silva_full_align_trunc.fasta.gz

Plots

library(ggplot2)
data <-read.table(file="testing.tbl")
ggplot(data) +
    geom_point(aes(x=Type, y=Pearson, color=Type), position="jitter") +
    xlab("") + ylab("Coevolution (Pearson)\n") +
    theme_bw() +
    geom_hline(yintercept=0.7, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.8, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.9, linetype="dashed", alpha=0.6) +
    annotate("text", x=0.7, y=0.72, label="P = 0.78") +
    annotate("text", x=0.7, y=0.82, label="P = 0.72") +
    annotate("text", x=0.7, y=0.92, label="P = 0.63") +
    annotate("text", x=2.4, y=0.72, label="R = 0.12") +
    annotate("text", x=2.4, y=0.82, label="R = 0.07") +
    annotate("text", x=2.4, y=0.92, label="R = 0.03")

ggplot(data) +
    geom_point(aes(x=Type, y=Spearman, color=Type), position="jitter") +
    xlab("") + ylab("Coevolution (Spearman)\n") +
    theme_bw() +
    geom_hline(yintercept=0.7, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.8, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.9, linetype="dashed", alpha=0.6) +
    annotate("text", x=0.7, y=0.72, label="P = 0,77") +
    annotate("text", x=0.7, y=0.82, label="P = 0,80") +
    annotate("text", x=0.7, y=0.92, label="P = 0.71") +
    annotate("text", x=2.4, y=0.72, label="R = 0.08") +
    annotate("text", x=2.4, y=0.82, label="R = 0.06") +
    annotate("text", x=2.4, y=0.92, label="R = 0.02")

ggplot(data) +
    geom_point(aes(x=Type, y=r_Adjusted, color=Type), position="jitter") +
    xlab("") + ylab("Coevolution (Partial correlation)\n") +
    theme_bw() +
    geom_hline(yintercept=0.97, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.98, linetype="dashed", alpha=0.6) +
    geom_hline(yintercept=0.99, linetype="dashed", alpha=0.6) +
    annotate("text", x=0.7, y=0.972, label="P = 0.70") +
    annotate("text", x=0.7, y=0.982, label="P = 0.71") +
    annotate("text", x=0.7, y=0.992, label="P = 0.69") +
    annotate("text", x=2.4, y=0.972, label="R = 0.72") +
    annotate("text", x=2.4, y=0.982, label="R = 0.58") +
    annotate("text", x=2.4, y=0.992, label="R = 0.39")