<reponame>thodk/proteostasis_imprinting_across_evolution<gh_stars>0
# -*- coding: utf-8 -*-

import pandas
import os
from constant_variables import define_main_dir
from constant_variables import ensembl_classes


def collect_data(main_dir, species_abbr, hsp):
    try:
        f = main_dir+'ensembl_data/'+hsp+'_raw_files/'+species_abbr+'.tsv'
        df = pandas.read_csv(f, sep='\t', index_col=0)
        ensembl_ids = df.ensembl_gene_id.tolist()
    except (IOError, AttributeError):
        ensembl_ids = []
    try:
        f = main_dir+'uniprot_data/'+hsp+'_raw_files/'+species_abbr+'.tsv'
        df = pandas.read_csv(f, sep='\t', index_col=0)
        uniprot_ids = df.id.tolist()
    except (IOError, AttributeError):
        uniprot_ids = []
    return {'ensembl':ensembl_ids, 'uniprot':uniprot_ids}
        

def worker(ensembl_class):
    
    main_dir = define_main_dir(ensembl_class)
    output_dir = main_dir+'filtered_HSPs_annotation/'
    files_dir = main_dir+'files/'
    
    # load the organism names and get their abbreviations
    df = pandas.read_csv(files_dir+'final_species_names.tsv', sep='\t', index_col=0)
    
    # 1. for each abbreviation search if there are available annotation for the HSPs
    # 2. use of 'collect_data' data function to search in the 'raw' folders 
    annotation_results = []
    data = {}
    for species_abbr in df.abbreviation.tolist():
        hsp70_annotation = collect_data(main_dir, species_abbr, 'hsp70')
        hsp40_annotation = collect_data(main_dir, species_abbr, 'hsp40')
        data.update({species_abbr : {'hsp70':hsp70_annotation, 
                                     'hsp40':hsp40_annotation}})
        # True if count of annotated hsp70 > 0
        s70 = False if sum([len(i) for i in hsp70_annotation.values()]) == 0 else True
        # True if count of annotated hsp40 > 0
        s40 = False if sum([len(i) for i in hsp40_annotation.values()]) == 0 else True
        annotation_results.append([species_abbr, s70, s40])

    columns = ['abbreviation', 'hsp70_bool', 'hsp40_bool']
    df = pandas.DataFrame(annotation_results, columns=columns)

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
        
    pos_bool1 = df.hsp70_bool == True 
    pos_bool2 = df.hsp40_bool == True
    # 1. combine the two booleans to check if True & True and filter the organisms
    # 2. save the data frame for the following steps
    filtered_df = df.loc[((pos_bool1) & (pos_bool2)), :]
    filtered_df.to_csv(main_dir+'01_species_with_HSPs_annotation.tsv', sep='\t')
    
    # save the HSPs annotation for each organism in the 'filtered_hsp_annotation'
    # folder. Each data frame should contain protein id, hsp family name, database
    for species_abbr in filtered_df.abbreviation.tolist():
        tmp_annotation = data[species_abbr]
        tmp_list = []
        for hsp, tmp_dict in tmp_annotation.items():
            # key: ensembl or uniprot
            # hsp: hsp40 or hsp70
            # ids: protein ids
            for key, ids in tmp_dict.items():
                tmp_list = tmp_list + [[i,hsp,key] for i in ids]
        tmp_df = pandas.DataFrame(tmp_list, columns=['gene_symbol', 'hsp', 'resource'])
        tmp_df.to_csv(output_dir+species_abbr+'.tsv', sep='\t')
    


'''
DESCRIPTION:

That script reads the HSP annotations from UniProt and Ensembl databases, as
they have been retrieved with scripts 10A and 10B. The annotated entries
are gather together to examine which species have adequate annotation and
which should be filtrered out.

Two outputs:

1. 01_organisms_with_HSPs_annotation.tsv : contains only the species with annotation.
They passed the first filtering. In order to be included in the final set of species,
for the analysis, they should also pass the filtering for rRNA data and 
genomic annotation

2. 'filtered_HSPs_annotation/': contains the annotation file for each species:

example:

gene_symbol	hsp	     resource
Q03751          hsp40	     uniprot
Q9VPQ2	     hsp40	     uniprot
P82910	     hsp70	     uniprot
Q9VG58	     hsp70	     uniprot

It will be used to retrieve the fasta files in the next steps
'''

if __name__ == "__main__":
    # execute the worker for each ensembl class
    [worker(c) for c in ensembl_classes]
