added consistent style scripts to format kd & rd values
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255
scripts/kd_df.py
Executable file
255
scripts/kd_df.py
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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 Tue Aug 6 12:56:03 2019
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@author: tanu
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'''
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#=======================================================================
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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 (link below)
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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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# https://web.expasy.org/protscale/pscale/protscale_help.html
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#=======================================================================
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#%% load packages
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import sys, os
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import argparse
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import pandas as pd
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import numpy as np
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from pylab import *
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from Bio.SeqUtils import ProtParamData
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from Bio.SeqUtils.ProtParam import ProteinAnalysis
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from Bio import SeqIO
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#from Bio.Alphabet.IUPAC import IUPACProtein
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import pprint as pp
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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.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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#=======================================================================
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help='drug name', default = None)
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arg_parser.add_argument('-g', '--gene', help='gene name', default = None)
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#arg_parser.add_argument('-p', '--plot', help='show plot', action='store_true')
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arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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arg_parser.add_argument('-fasta','--fasta_file', help = 'fasta file. By default, it assmumes a file called <gene>.fasta.txt in input_dir')
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arg_parser.add_argument('--debug', action='store_true', help = 'Debug Mode')
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args = arg_parser.parse_args()
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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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drug = args.drug
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gene = args.gene
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gene_match = gene + '_p.'
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data_dir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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fasta_filename = args.fasta_file
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#plot = args.plot
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DEBUG = args.debug
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#============
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# directories
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#============
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if data_dir:
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datadir = data_dir
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else:
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datadir = homedir + '/' + 'git/Data'
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if not indir:
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indir = datadir + '/' + drug + '/' + 'input'
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if not outdir:
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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# input
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#=======
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if fasta_filename:
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in_filename_fasta = fasta_filename
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else:
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in_filename_fasta = gene.lower() + '.fasta.txt'
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infile_fasta = indir + '/' + in_filename_fasta
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print('Input fasta file:', infile_fasta
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, '\n============================================================')
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#=======
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# output
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#=======
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out_filename_kd = gene.lower() + '_kd.csv'
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outfile_kd = outdir + '/' + out_filename_kd
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print('Output file:', outfile_kd
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, '\n=============================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% kd values from fasta file and output csv
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def kd_to_csv(inputfasta, outputkdcsv, windowsize = 3):
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"""
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Calculate kd (hydropathy values) from input fasta file
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@param inputfasta: fasta file
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@type: string
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@param outputkdcsv: csv file with kd values
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@type: string
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@param windowsize: windowsize to perform KD calcs on (Kyte&-Doolittle)
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@type: numeric
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@return: none, writes kd values df as csv
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"""
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#========================
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# read input fasta file
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#========================
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fh = open(inputfasta)
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for record in SeqIO.parse(fh, 'fasta'):
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id = record.id
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seq = record.seq
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num_residues = len(seq)
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fh.close()
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sequence = str(seq)
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X = ProteinAnalysis(sequence)
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#===================
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# calculate KD values: same as the expasy server
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#===================
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my_window = windowsize
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offset = round((my_window/2)-0.5)
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# edge weight is set to default (100%)
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kd_values = (X.protein_scale(ProtParamData.kd , window = my_window))
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# sanity checks
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print('Sequence Length:', num_residues)
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print('kd_values Length:',len(kd_values))
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print('Window Length:', my_window)
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print('Window Offset:', offset)
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print('=================================================================')
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print('Checking:len(kd values) is as expected for the given window size & offset...')
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expected_length = num_residues - (my_window - offset)
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if len(kd_values) == expected_length:
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print('PASS: expected and actual length of kd values match')
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else:
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print('FAIL: length mismatch'
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,'\nExpected length:', expected_length
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,'\nActual length:', len(kd_values)
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, '\n=========================================================')
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#===================
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# creating two dfs
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#===================
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# 1) aa sequence and 2) kd_values. Then reset index for each df
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# which will allow easy merging of the two dfs.
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# df1: df of aa seq with index reset to start from 1
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# (reflective of the actual aa position in a sequence)
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# Name column of wt as 'wild_type' to be the same name used
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# in the file required for merging later.
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dfSeq = pd.DataFrame({'wild_type_kd':list(sequence)})
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dfSeq.index = np.arange(1, len(dfSeq) + 1) # python is not inclusive
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# df2: df of kd_values with index reset to start from offset + 1 and
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# subsequent matched length of the kd_values
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dfVals = pd.DataFrame({'kd_values':kd_values})
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dfVals.index = np.arange(offset + 1, len(dfVals) + 1 + offset)
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# sanity checks
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max(dfVals['kd_values'])
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min(dfVals['kd_values'])
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#===================
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# concatenating dfs
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#===================
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# Merge the two on index
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# (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
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# will be 2 (first and last ones based on window size and offset)
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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('Checking: position col i.e. index should be numeric')
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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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, '\n=========================================================')
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# Ensuring lowercase column names for consistency
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kd_df.columns = kd_df.columns.str.lower()
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#===============
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# writing file
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#===============
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print('Writing file:'
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, '\nFilename:', outputkdcsv
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, '\nExpected no. of rows:', len(kd_df)
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, '\nExpected no. of cols:', len(kd_df.columns)
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, '\n=============================================================')
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kd_df.to_csv(outputkdcsv, header = True, index = True)
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#===============
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# plot: optional!
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#===============
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# http://www.dalkescientific.com/writings/NBN/plotting.html
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# FIXME: save fig
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# extract just pdb if from 'id' to pass to title of plot
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# foo = re.match(r'(^[0-9]{1}\w{3})', id).groups(1)
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#if doplot:
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plot(kd_values, linewidth = 1.0)
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#axis(xmin = 1, xmax = num_residues)
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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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#%% end of function
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#=======================================================================
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def main():
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print('Running hydropathy calcs with following params\n'
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, '\nInput fasta file:', in_filename_fasta
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, '\nOutput:', out_filename_kd)
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kd_to_csv(infile_fasta, outfile_kd, 3)
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print('Finished writing file:'
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, '\nFile:', outfile_kd
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, '\n=============================================================')
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if __name__ == '__main__':
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main()
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#%% end of script
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#=======================================================================
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203
scripts/rd_df.py
Executable file
203
scripts/rd_df.py
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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 Tue Aug 6 12:56:03 2019
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@author: tanu
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'''
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#=============================================================================
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# Task: Residue depth (rd) processing to generate a df with residue_depth(rd)
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# values
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# FIXME: source file is MANUALLY downloaded from the website
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# Input: '.tsv' i.e residue depth txt file (output from .zip file manually
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# downloaded from the website).
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# This should be integrated into the pipeline
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# Output: .csv with 3 cols i.e position, rd_values & 3-letter wt aa code(caps)
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#=============================================================================
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#%% load packages
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import sys, os
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import argparse
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import pandas as pd
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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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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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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help='drug name', default = None)
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arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', default = None)
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arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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arg_parser.add_argument('-rd','--rd_file', help = 'residue depth file. By default, it assmumes a file called <gene>_rd.tsv in output_dir')
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arg_parser.add_argument('--debug', action='store_true', help = 'Debug Mode')
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args = arg_parser.parse_args()
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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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drug = args.drug
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gene = args.gene
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gene_match = gene + '_p.'
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data_dir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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rd_filename = args.rd_file
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DEBUG = args.debug
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#============
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# directories
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#============
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if data_dir:
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datadir = data_dir
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else:
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datadir = homedir + '/' + 'git/Data'
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if not indir:
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indir = datadir + '/' + drug + '/' + 'input'
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if not outdir:
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outdir = datadir + '/' + drug + '/' + 'output'
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#======
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# input
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#=======
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if rd_filename:
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in_filename_rd = rd_filename
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else:
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#in_filename_rd = '3pl1_rd.tsv'
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in_filename_rd = gene.lower() + '_rd.tsv'
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infile_rd = outdir + '/' + in_filename_rd
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print('Input file:', infile_rd
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, '\n=============================================================')
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#=======
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# output
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#=======
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out_filename_rd = gene.lower() + '_rd.csv'
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outfile_rd = outdir + '/' + out_filename_rd
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print('Output file:', outfile_rd
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, '\n=============================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% rd values from <gene>_rd.tsv values
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def rd_to_csv(inputtsv, outputrdcsv):
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"""
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formats residue depth values from input file
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@param inputtsv: tsv file downloaded from {INSERT LINK}
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@type inputtsv: string
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@param outputrdsv: csv file with rd values
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@type outfile_rd: string
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@return: none, writes rd values df as csv
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"""
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#========================
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# read downloaded tsv file
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#========================
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#%% Read input file
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rd_data = pd.read_csv(inputtsv, sep = '\t')
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print('Reading input file:', inputtsv
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, '\nNo. of rows:', len(rd_data)
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, '\nNo. of cols:', len(rd_data.columns))
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print('Column names:', rd_data.columns
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, '\n===========================================================')
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#========================
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# creating position col
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#========================
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# Extracting residue number from index and assigning
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# the values to a column [position]. Then convert the position col to numeric.
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rd_data['position'] = rd_data.index.str.extract('([0-9]+)').values
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# converting position to numeric
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rd_data['position'] = pd.to_numeric(rd_data['position'])
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rd_data['position'].dtype
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print('Extracted residue num from index and assigned as a column:'
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, '\ncolumn name: position'
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, '\ntotal no. of cols now:', len(rd_data.columns)
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, '\n=========================================================')
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#========================
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# Renaming amino-acid
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# and all-atom cols
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#========================
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print('Renaming columns:'
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, '\ncolname==> # chain:residue: wt_3letter_caps'
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, '\nYES... the column name *actually* contains a # ..!'
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, '\ncolname==> all-atom: rd_values'
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, '\n=========================================================')
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rd_data.rename(columns = {'# chain:residue':'wt_3letter_caps', 'all-atom':'rd_values'}, inplace = True)
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print('Column names:', rd_data.columns)
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#========================
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# extracting df with the
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# desired columns
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#========================
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print('Extracting relevant columns for writing df as csv')
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rd_df = rd_data[['position','rd_values','wt_3letter_caps']]
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if len(rd_df) == len(rd_data):
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print('PASS: extracted df has expected no. of rows'
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,'\nExtracted df dim:'
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,'\nNo. of rows:', len(rd_df)
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,'\nNo. of cols:', len(rd_df.columns))
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else:
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print('FAIL: no. of rows mimatch'
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, '\nExpected no. of rows:', len(rd_data)
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, '\nGot no. of rows:', len(rd_df)
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, '\n=====================================================')
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# Ensuring lowercase column names for consistency
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rd_df.columns = rd_df.columns.str.lower()
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#===============
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# writing file
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#===============
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print('Writing file:'
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, '\nFilename:', outputrdcsv
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# , '\nPath:', outdir
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# , '\nExpected no. of rows:', len(rd_df)
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# , '\nExpected no. of cols:', len(rd_df.columns)
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, '\n=========================================================')
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rd_df.to_csv(outputrdcsv, header = True, index = False)
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#%% end of function
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#=======================================================================
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#%% call function
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#rd_to_csv(infile_rd, outfile_rd)
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#=======================================================================
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def main():
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print('residue depth using the following params'
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, '\nInput residue depth file:', in_filename_rd
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, '\nOutput:', out_filename_rd)
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rd_to_csv(infile_rd, outfile_rd)
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print('Finished Writing file:'
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, '\nFilename:', outfile_rd
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, '\n=============================================================')
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if __name__ == '__main__':
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main()
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#%% end of script
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#=======================================================================
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