From 78c4fba5f795b48c365492ede470214bcd0ceaf5 Mon Sep 17 00:00:00 2001
From: GwennyGit <81755070+GwennyGit@users.noreply.github.com>
Date: Wed, 8 Feb 2023 21:49:01 +0100
Subject: [PATCH] Created parse due to refactoring #52

---
 refinegems/parse.py | 118 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 refinegems/parse.py

diff --git a/refinegems/parse.py b/refinegems/parse.py
new file mode 100644
index 00000000..3bd49170
--- /dev/null
+++ b/refinegems/parse.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+import re
+import gffutils
+import pandas as pd
+from Bio import Entrez, SeqIO
+
+__author__ = "Famke Baeuerle and Gwendolyn O. Gusak"
+
+
+def parse_fasta_headers(filepath: str) -> pd.DataFrame:
+    """Parses FASTA file headers to obtain:
+        - the protein_id
+        - and the model_id (like it is obtained from CarveMe)
+        corresponding to the locus_tag
+        
+        Args:
+            filepath (str): Path to FASTA file
+            
+        Returns:
+            a pandas dataframe containing the columns locus_tag, Protein_id & Model_id
+    """
+    keyword_list = ['protein', 'locus_tag']
+    tmp_dict = dict()
+    locus2ids = {
+        'locus_tag': [],
+        'Protein_id': [],
+        'Model_id': []
+    }
+   
+    with open(filepath, 'r') as handle:
+        for record in SeqIO.parse(handle, 'fasta'):
+            header = record.description
+            protein_id = record.id.split('|')[1].split('prot_')[1].split('.')[0]
+            descriptors = re.findall('\[+(.*?)\]', header)
+            model_id = re.sub("\||\.", "_", record.id)
+            model_id = f'G_{model_id}'
+         
+            descriptors.insert(0, protein_id)
+            
+            tmp_dict['protein_id'] = protein_id
+            
+            for entry in descriptors:
+                entry = entry.split('=')
+               
+                if entry[0] in keyword_list:
+                    if entry[0] == 'protein_id':
+                        tmp_dict[entry[0]] = entry[1].split('.')[0]
+                    else:
+                        tmp_dict[entry[0]] = entry[1]
+                    
+            locus2ids.get('locus_tag').append(tmp_dict.get('locus_tag'))
+            locus2ids.get('Protein_id').append(tmp_dict.get('protein_id'))
+            locus2ids.get('Model_id').append(model_id)
+            
+    return pd.DataFrame(locus2ids)
+ 
+
+# Function originally from analysis_kegg
+def get_name_from_locus(locus):
+    """fetches protein name from NCBI
+
+    Args:
+        locus (string): NCBI compatible locus_tag
+
+    Returns:
+        str: protein name / description
+    """
+    handle = Entrez.efetch(
+        db="protein",
+        id=locus,
+        rettype="gbwithparts",
+        retmode='text')
+    records = SeqIO.parse(handle, "gb")
+
+    for i, record in enumerate(records):
+        return record.description
+ 
+
+# Function originally from analysis_kegg
+def get_locus_gpr(gff_file):
+    """Searches gff file of organism for gene protein reactions based on locus tags
+
+    Args:
+        gff_file (Str): path to gff file of organism of interest
+
+    Returns:
+        df: table containing mapping from locus tag to GPR
+    """
+    db = gffutils.create_db(
+        gff_file,
+        ':memory:',
+        merge_strategy='create_unique')
+    mapping_cds = {}
+    for feature in db.all_features():
+        attr = dict(feature.attributes)
+        try:
+            if str(attr['gbkey'][0]) == 'CDS':
+                mapping_cds[attr['Name'][0]] = attr['Parent'][0]
+        except BaseException:
+            pass
+    mapping_df = pd.DataFrame.from_dict(
+        mapping_cds,
+        columns=['Parent'],
+        orient='index').reset_index().rename(
+        columns={
+            'index': 'GPR'})
+
+    def extract_locus(feature):
+        try:
+            return db[feature].attributes['old_locus_tag'][0]
+        except BaseException:
+            pass
+        return None
+
+    mapping_df['locus_tag'] = mapping_df.apply(
+        lambda row: extract_locus(row['Parent']), axis=1)
+    return mapping_df.drop('Parent', axis=1)
+ 
\ No newline at end of file