tidy code and renamed kd.py to kd_df.py
This commit is contained in:
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6 changed files with 156 additions and 194 deletions
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@ -1,109 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Feb 6 12:18:24 2020
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@author: tanu
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"""
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#=======================================================================
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# Task: Residue Depth (rd) values for amino acid sequence using the
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# Depth server.
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# Depth server: http://cospi.iiserpune.ac.in/depth/htdocs/index.html
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# FIXME: for now input is a valid pdb code NOT a valid pdb file that you can upload
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# Input: PDB file (valid pdb code)
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# Output:
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# useful links
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# http://foldxsuite.crg.eu/faq-page#
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# after fold x downlaoded, extract and run it from
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# https://biopython.org/DIST/docs/api/Bio.PDB.ResidueDepth%27-module.html
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# proDepth: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0007072
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# needs biopython and msms
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#=======================================================================
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#%% load packages
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import sys, os
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import re
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import pandas as pd
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from Bio.PDB.ResidueDepth import ResidueDepth
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from Bio.PDB.PDBParser import PDBParser
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from Bio.PDB.ResidueDepth import get_surface
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#%% specify input and output variables
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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#==========
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# data dir
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#==========
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#indir = 'git/Data/pyrazinamide/input/original'
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datadir = homedir + '/' + 'git/Data'
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#=======
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# input
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#=======
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indir = datadir + '/' + drug + '/' + 'input'
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in_filename = '3pl1.pdb'
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infile = indir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path:', indir)
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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# specify output file
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out_filename = 'XXX'
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outfile = outdir + '/' + out_filename
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print('Output filename: ', out_filename
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, '\nOutput path: ', outdir)
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#%% end of variable assignment for input and output files
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#================================================================
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# Read input pdb file
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parser = PDBParser()
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# extract the 3 letter pdb code
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pdb_code = re.search(r'(^[0-9]{1}\w{3})', in_filename).group(1)
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#structure = parser.get_structure("3pl1", "/home/tanu/git/3pl1.pdb")
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structure = parser.get_structure(pdb_code, infile)
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model = structure[0]
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surface = get_surface(model)
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rd = ResidueDepth(model)
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print(rd['A',(' ', 152, ' ')])
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rd.keys()
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foo = rd.property_dict
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rd.property_keys
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baz = rd.property_list
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# To calculate the residue depth (average atom depth of the atoms in a residue):
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from Bio.PDB.ResidueDepth import residue_depth
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chain = model['A']
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res152 = chain[152]
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rd2 = residue_depth(res152, surface)
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# df from dict
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foo1 = pd.DataFrame.from_dict(baz, orient='index', columns = ['res_depth', 'surface'])
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test = pd.Series(foo, name = "test")
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# df from list
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foo2 = pd.DataFrame(baz, columns = ['residue', 'residue depth'])
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### iterate
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for i in range(185):
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print(i)
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rd3 = residue_depth(res+i, surface)
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print(rd3)
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@ -439,11 +439,11 @@ dr_WF0['dr_mutations_pyrazinamide'] = dr_WF0['dr_mutations_pyrazinamide'].str.ls
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# extract only the samples/rows with pncA_p.
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dr_pnca_WF0 = dr_WF0.loc[dr_WF0.dr_mutations_pyrazinamide.str.contains(gene_match)]
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print('lengths after tidy split and extracting', gene_match, 'muts:',
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'\nold length:' , len(meta_pnca_dr),
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'\nlen after split:', len(dr_WF0),
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'\ndr_pnca_WF0 length:', len(dr_pnca_WF0),
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'\nexpected len:', dr_pnca_count)
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print('lengths after tidy split and extracting', gene_match, 'muts:'
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, '\nold length:' , len(meta_pnca_dr)
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, '\nlen after split:', len(dr_WF0)
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, '\ndr_pnca_WF0 length:', len(dr_pnca_WF0)
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, '\nexpected len:', dr_pnca_count)
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if len(dr_pnca_WF0) == dr_pnca_count:
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print('PASS: length of dr_pnca_WF0 match with expected length')
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@ -1,8 +1,22 @@
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#!/home/tanu/anaconda3/envs/ContactMap/bin/python3
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# Read a DSSP file into a data frame and pretty-print it
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Feb 18 10:10:12 2020
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@author: tanu
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"""
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#=======================================================================
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# Task: Read a DSSP file into a data frame and output to a csv file
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# Input: '.dssp' i.e gene associated.dssp file (output from run_dssp.sh)
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# Output: '.csv' file containing DSSP output as a df ith ASA, RSA, etc.
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# useful links:
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#https://jbloomlab.github.io/dms_tools2/dms_tools2.dssp.html
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#https://jbloomlab.github.io/dms_tools2/dms_tools2.dssp.html
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#=======================================================================
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#%% load packages
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import sys, os
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import re
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import pandas as pd
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@ -13,16 +27,16 @@ import pprint as pp
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#from Bio.PDB.PDBParser import PDBParser
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import dms_tools2
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import dms_tools2.dssp
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#%% specify input and output variables
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#=======================================================================#
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#%% specify homedir and curr dir
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homedir = os.path.expanduser('~')
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#%% set working dir
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output paths & filenames
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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#gene_match = gene + '_p.'
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@ -57,7 +71,7 @@ print('Output filename:', out_filename
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,'\nOutfile: ', outfile)
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#%% end of variable assignment for input and output files
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#================================================================
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#=======================================================================
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# Process dssp output and extract into df
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dssp_file = infile
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dssp_df = dms_tools2.dssp.processDSSP(dssp_file, chain = my_chain)
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@ -65,6 +79,11 @@ dssp_df = dms_tools2.dssp.processDSSP(dssp_file, chain = my_chain)
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# Link: https://en.wikipedia.org/wiki/Relative_accessible_surface_area
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pp.pprint(dssp_df)
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#=====================
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# Renaming amino-acid
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# and site columns
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#=====================
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# Rename column (amino acid) as 'wild_type' and (site} as 'position'
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# to be the same names as used in the file required for merging later.
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dssp_df.columns
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@ -82,4 +101,6 @@ dssp_df.to_csv(outfile, header=True, index = False)
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print('Finished writing:', out_filename
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, '\nNo. of rows:', len(dssp_df)
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, '\nNo. of cols:', len(dssp_df.columns))
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print('======================================================================')
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print('======================================================================')
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#%% end of script
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#=======================================================================
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@ -9,6 +9,10 @@ Created on Tue Aug 6 12:56:03 2019
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# Task: Hydrophobicity (Kd) values for amino acid sequence using the
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# Kyt&-Doolittle.
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# Same output as using the expasy server https://web.expasy.org/protscale/
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# Input: fasta file
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# Output: csv file with
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# useful links
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# https://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParamData-pysrc.html
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@ -24,8 +28,8 @@ from Bio import SeqIO
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import pandas as pd
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import numpy as np
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import sys, os
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#%% specify input and output variables
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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@ -33,7 +37,7 @@ os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output paths & filenames
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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@ -54,6 +58,7 @@ infile = indir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path:', indir)
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print('======================================================================')
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#=======
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# output
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#=======
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@ -63,8 +68,10 @@ outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir)
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#%%11
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# specify window size for hydropathy profile computation
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print('======================================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%%specify window size for hydropathy profile computation
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# https://web.expasy.org/protscale/pscale/protscale_help.html
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my_window = 3
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offset = round((my_window/2)-0.5)
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@ -115,27 +122,48 @@ dfVals.index = np.arange(offset + 1, len(dfVals) + 1 + offset)
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max(dfVals['kd_values'])
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min(dfVals['kd_values'])
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#============
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# merging dfs
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#============
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# Merge the two on index (as these are now reflective of the aa position numbers): df1 and df2
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# This will introduce NaN where there is missing values. In our case this will be 2 (first and last ones)
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# Conveniently, the last position in this case is not part of the struc, so not much loss of info
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# Needless to state that this will be variable for other targets.
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df = pd.concat([dfSeq, dfVals], axis = 1)
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# rename index to position
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df = df.rename_axis('position')
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print(df)
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kd_df = pd.concat([dfSeq, dfVals], axis = 1)
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#============================
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# Renaming index to position
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#============================
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kd_df = kd_df.rename_axis('position')
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kd_df.head
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print('======================================================================')
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print('position col i.e. index should be numeric')
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print('======================================================================')
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if kd_df.index.dtype == 'int64':
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print('PASS: position col is numeric'
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, '\ndtype is:', kd_df.index.dtype)
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else:
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print('FAIL: position col is not numeric'
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, '\nConverting to numeric')
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kd_df.index.astype('int64')
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print('Checking dtype for after conversion:\n'
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,'\ndtype is:', kd_df.index.dtype)
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print('======================================================================')
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#%% write file
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print('Writing file:', out_filename
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, '\nFilename:', out_filename
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, '\nPath:', outdir)
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df.to_csv(outfile, header = True, index = True)
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kd_df.to_csv(outfile, header = True, index = True)
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print('Finished writing:', out_filename
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, '\nNo. of rows:', len(df)
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, '\nNo. of cols:', len(df.columns))
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, '\nNo. of rows:', len(kd_df)
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, '\nNo. of cols:', len(kd_df.columns))
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#%% Plot
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#%% plot
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# http://www.dalkescientific.com/writings/NBN/plotting.html
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# FIXME: save fig
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@ -147,4 +175,7 @@ xlabel('Residue Number')
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ylabel('Hydrophobicity')
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title('K&D Hydrophobicity for ' + id)
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show()
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print('======================================================================')
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#%% end of script
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#=======================================================================
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@ -10,15 +10,17 @@ Created on Tue Aug 6 12:56:03 2019
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#=======================================================================
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# TASK: calculate how many mutations result in
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# electrostatic changes wrt wt
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# Input: mcsm and AF_OR file
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# Output: mut_elec_changes_results.txt
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#=======================================================================
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#%% load libraries
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import os, sys
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import pandas as pd
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#import numpy as np
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#%% specify homedir as python doesn't recognise tilde
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#=======================================================================
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#%% specify homedir and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output paths & filenames
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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@ -59,7 +61,6 @@ print('Output filename: ', out_filename
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% Read input files
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#in_filename = gene.lower() + '_meta_data_with_AFandOR.csv'
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print('Reading input file (merged file):', infile)
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comb_df = pd.read_csv(infile, sep = ',')
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@ -136,17 +137,11 @@ print('Total number of electrostatic changes resulting from Mutation is (%):', e
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no_change_muts = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == True]
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no_change_muts.mut_count.sum()
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###########
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# Step 5: output from console
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###########
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#%% output from console
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#sys.stdout = open(file, 'w')
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sys.stdout = open(outfile, 'w')
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#print(no_change_muts, '\n',
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# all_prop_change)
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print('======================\n'
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,'Unchanged muts'
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,'\n=====================\n'
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@ -156,16 +151,11 @@ print('======================\n'
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, '\n============================\n'
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, all_prop_change)
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#print('======================================================================')
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#print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
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#print('Total no. of muts: ', total_muts)
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#print('Total no. of changed muts: ', all_prop_change.mut_count.sum())
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#print('Total no. of unchanged muts: ', no_change_muts.mut_count.sum() )
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#print('=======================================================================')
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print('========================================================================'
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, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
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, '\nTotal no. of muts: ', total_muts
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, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
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, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum()
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, '\n=========================================================================')
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#%% end of script
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#=======================================================================
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111
meta_data_analysis/reference_dict.py
Executable file → Normal file
111
meta_data_analysis/reference_dict.py
Executable file → Normal file
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@ -5,58 +5,82 @@ Created on Tue Jun 18 11:32:28 2019
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@author: tanushree
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"""
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############################################
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# load libraries
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#=======================================================================
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# TASK: creating an aa dict to map 3 letter and other combinations of
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# aa codes to one-letter aa code and also with aa properties.
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# Input: .csv file containing aa_code
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# Output: is called by other .py script to perform this mapping.
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#=======================================================================
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#%% load packages
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import pandas as pd
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import os
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#############################################
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#=======================================================================
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#%% specify homedir and curr dir
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homedir = os.path.expanduser('~')
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#!#########################!
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# REQUIREMNETS:
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# Data/ must exist
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# containing GWAS data
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#!#########################!
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print(os.getcwd())
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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os.chdir(homedir + '/git/Data/pyrazinamide/input/original')
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print(os.getcwd())
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#%%
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############# specify variables for input and output paths and filenames
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# set working dir
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#os.getcwd()
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#os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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#os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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#gene = 'pnca'
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datadir = homedir + '/git/Data'
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basedir = datadir + '/' + drug + '/input'
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gene = 'pncA'
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gene_match = gene + '_p.'
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#==========
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# data dir
|
||||
#==========
|
||||
#indir = 'git/Data/pyrazinamide/input/original'
|
||||
datadir = homedir + '/' + 'git/Data'
|
||||
|
||||
#=======
|
||||
# input
|
||||
inpath = "/original"
|
||||
in_filename = "/aa_codes.csv"
|
||||
infile = basedir + inpath + in_filename
|
||||
print(infile)
|
||||
#=======
|
||||
indir = datadir + '/' + drug + 'input'
|
||||
in_filename = 'aa_codes.csv'
|
||||
infile = indir + '/' + in_filename
|
||||
print('Input filename:', in_filename
|
||||
, '\nInput path:', indir)
|
||||
|
||||
#==========
|
||||
#read file
|
||||
#==========
|
||||
#=======
|
||||
# output: No output
|
||||
#=======
|
||||
|
||||
#outdir = datadir + '/' + drug + '/' + 'output'
|
||||
#out_filename = ''
|
||||
#outfile = outdir + '/' + out_filename
|
||||
#print('Output filename:', out_filename
|
||||
# , '\nOutput path:', outdir)
|
||||
|
||||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
#%% Read input file
|
||||
my_aa = pd.read_csv(infile) #20, 6
|
||||
#assign the one_letter code as the row names so that it is easier to create a dict of dicts using index
|
||||
|
||||
# assign the one_letter code as the row names so that it is easier to create
|
||||
# a dict of dicts using index
|
||||
#my_aa = pd.read_csv('aa_codes.csv', index_col = 0) #20, 6 #a way to it since it is the first column
|
||||
my_aa = my_aa.set_index('three_letter_code_lower') #20, 5
|
||||
|
||||
#=========================================================
|
||||
#convert file to dict of dicts
|
||||
#=========================================================
|
||||
#convert each row into a dict of dicts so that there are 20 aa and 5 keys within
|
||||
#with your choice of column name that you have assigned to index as the "primary key".
|
||||
#using 'index' creates a dict of dicts
|
||||
#using 'records' creates a list of dicts
|
||||
#==================
|
||||
# convert file
|
||||
# to dict of dicts
|
||||
#====================
|
||||
# convert each row into a dict of dicts so that there are 20 aa and 5 keys within
|
||||
# with your choice of column name that you have assigned to index as the "primary key".
|
||||
# using 'index' creates a dict of dicts
|
||||
# using 'records' creates a list of dicts
|
||||
my_aa_dict = my_aa.to_dict('index') #20, with 5 subkeys
|
||||
|
||||
#================================================
|
||||
#dict of aa with their corresponding properties
|
||||
#This is defined twice
|
||||
# dict of aa with their corresponding properties
|
||||
# This is defined twice
|
||||
#================================================
|
||||
#7 categories: no overlap
|
||||
# 7 categories: no overlap
|
||||
qualities1 = { ('R', 'H', 'K'): 'Basic'
|
||||
, ('D', 'E'): 'Acidic'
|
||||
, ('N', 'Q'): 'Amidic'
|
||||
|
|
@ -66,7 +90,7 @@ qualities1 = { ('R', 'H', 'K'): 'Basic'
|
|||
, ('C', 'M'): 'Sulphur'
|
||||
}
|
||||
|
||||
#9 categories: allowing for overlap
|
||||
# 9 categories: allowing for overlap
|
||||
qualities2 = { ('R', 'H', 'K'): 'Basic'
|
||||
, ('D', 'E'): 'Acidc'
|
||||
, ('S', 'T', 'N', 'Q'): 'Polar'
|
||||
|
|
@ -78,6 +102,7 @@ qualities2 = { ('R', 'H', 'K'): 'Basic'
|
|||
, ('C', 'G', 'P'): 'Special'
|
||||
}
|
||||
|
||||
# taylor classification: allowing for overlap
|
||||
qualities_taylor = { ('R', 'H', 'K'): 'Basic'
|
||||
, ('D', 'E'): 'Acidc'
|
||||
, ('S', 'T', 'N', 'Q', 'C', 'Y', 'W', 'H', 'K', 'R', 'D', 'E'): 'Polar'
|
||||
|
|
@ -89,17 +114,19 @@ qualities_taylor = { ('R', 'H', 'K'): 'Basic'
|
|||
, ('C', 'G', 'P'): 'Special'
|
||||
}
|
||||
|
||||
# binary classification: hydrophilic or hydrophobic
|
||||
qualities_water = { ('D', 'E', 'N', 'P', 'Q', 'R', 'S'): 'hydrophilic'
|
||||
, ('A', 'C', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'T', 'V', 'W', 'X', 'Y'): 'hydrophobic'
|
||||
}
|
||||
|
||||
# polarity: no overlap
|
||||
qualities_polarity = { ('D', 'E'): 'acidic'
|
||||
, ('H', 'K', 'R'): 'basic'
|
||||
, ('C', 'G', 'N', 'Q', 'S', 'T', 'Y'): 'neutral'
|
||||
, ('A', 'F', 'I', 'L', 'M', 'P', 'V', 'W'): 'non-polar'
|
||||
}
|
||||
|
||||
# almost same as the one above
|
||||
# almost same as the one above but as pos, neg, polar and non-polar
|
||||
aa_calcprop = { ('D', 'E'): 'neg'
|
||||
, ('H', 'K', 'R'): 'pos'
|
||||
, ('N', 'Q', 'S', 'T', 'Y'): 'polar'
|
||||
|
|
@ -107,7 +134,7 @@ aa_calcprop = { ('D', 'E'): 'neg'
|
|||
}
|
||||
|
||||
#==============================================================================
|
||||
#adding amino acid properties to my dict of dicts
|
||||
# adding amino acid properties to my dict of dicts
|
||||
for k, v in my_aa_dict.items():
|
||||
#print (k,v)
|
||||
v['aa_prop1'] = str() #initialise keys
|
||||
|
|
@ -141,7 +168,9 @@ for k, v in my_aa_dict.items():
|
|||
if v['one_letter_code'] in group:
|
||||
v['aa_calcprop']+= aa_calcprop[group] # += for str concat
|
||||
|
||||
#COMMENT:VOILA!!! my_aa_dict is now a dict of dicts containing all associated properties for each aa
|
||||
# COMMENT:VOILA!!! my_aa_dict is now a dict of dicts containing all
|
||||
# associated properties for each aa
|
||||
#==============================================================================
|
||||
#%% end of script
|
||||
|
||||
|
||||
|
|
|
|||
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Reference in a new issue