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parse_parameters.py
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parse_parameters.py
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#! /usr/bin/env python3
'''
Python script to parse out all relevant parameters from the result files created by IQ-Tree2
after running the EvoNAPS workflow (using the modfied IQ-Tree 2 version).
Created: August 2022
Last updated: 08.03.2022
Author: Franziska Reden
'''
import pandas as pd
import numpy as np
import re
import sys
from os import path
import gzip
from Bio import SeqIO
import math
from collections import Counter
from datetime import datetime
from parse_tree import ParseTree
def AA_models():
global aa_models
aa_models = {'POISSON': {'FREQ_A': 0.05, 'FREQ_R': 0.05, 'FREQ_N': 0.05, 'FREQ_D': 0.05, 'FREQ_C': 0.05, 'FREQ_Q': 0.05, 'FREQ_E': 0.05, 'FREQ_G': 0.05, 'FREQ_H': 0.05, 'FREQ_I': 0.05, 'FREQ_L': 0.05, 'FREQ_K': 0.05, 'FREQ_M': 0.05, 'FREQ_F': 0.05, 'FREQ_P': 0.05, 'FREQ_S': 0.05, 'FREQ_T': 0.05, 'FREQ_W': 0.05, 'FREQ_Y': 0.05, 'FREQ_V': 0.05}, \
'Dayhoff': {'FREQ_A': 0.08712691, 'FREQ_R': 0.04090396, 'FREQ_N': 0.04043196, 'FREQ_D': 0.04687195, 'FREQ_C': 0.03347397, 'FREQ_Q': 0.03825496, 'FREQ_E': 0.04952995, 'FREQ_G': 0.08861191, 'FREQ_H': 0.03361897, 'FREQ_I': 0.03688596, 'FREQ_L': 0.08535691, 'FREQ_K': 0.08048092, 'FREQ_M': 0.01475299, 'FREQ_F': 0.03977196, 'FREQ_P': 0.05067995, 'FREQ_S': 0.06957693, 'FREQ_T': 0.05854194, 'FREQ_W': 0.01049399, 'FREQ_Y': 0.02991597, 'FREQ_V': 0.06471794}, \
'DCMut': {'FREQ_A': 0.08712691, 'FREQ_R': 0.04090396, 'FREQ_N': 0.04043196, 'FREQ_D': 0.04687195, 'FREQ_C': 0.03347397, 'FREQ_Q': 0.03825496, 'FREQ_E': 0.04952995, 'FREQ_G': 0.08861191, 'FREQ_H': 0.03361897, 'FREQ_I': 0.03688596, 'FREQ_L': 0.08535691, 'FREQ_K': 0.08048092, 'FREQ_M': 0.01475299, 'FREQ_F': 0.03977196, 'FREQ_P': 0.05067995, 'FREQ_S': 0.06957693, 'FREQ_T': 0.05854194, 'FREQ_W': 0.01049399, 'FREQ_Y': 0.02991597, 'FREQ_V': 0.06471794}, \
'JTT': {'FREQ_A': 0.07674792, 'FREQ_R': 0.05169095, 'FREQ_N': 0.04264496, 'FREQ_D': 0.05154395, 'FREQ_C': 0.01980298, 'FREQ_Q': 0.04075196, 'FREQ_E': 0.06182994, 'FREQ_G': 0.07315193, 'FREQ_H': 0.02294398, 'FREQ_I': 0.05376095, 'FREQ_L': 0.09190391, 'FREQ_K': 0.05867594, 'FREQ_M': 0.02382598, 'FREQ_F': 0.04012596, 'FREQ_P': 0.05090095, 'FREQ_S': 0.06876493, 'FREQ_T': 0.05856494, 'FREQ_W': 0.01426099, 'FREQ_Y': 0.03210197, 'FREQ_V': 0.06600493}, \
'mtREV': {'FREQ_A': 0.072, 'FREQ_R': 0.019, 'FREQ_N': 0.039, 'FREQ_D': 0.019, 'FREQ_C': 0.006, 'FREQ_Q': 0.025, 'FREQ_E': 0.024, 'FREQ_G': 0.056, 'FREQ_H': 0.028, 'FREQ_I': 0.088, 'FREQ_L': 0.169, 'FREQ_K': 0.023, 'FREQ_M': 0.054, 'FREQ_F': 0.061, 'FREQ_P': 0.054, 'FREQ_S': 0.072, 'FREQ_T': 0.086, 'FREQ_W': 0.029, 'FREQ_Y': 0.033, 'FREQ_V': 0.043}, \
'WAG': {'FREQ_A': 0.08662791, 'FREQ_R': 0.043972, 'FREQ_N': 0.0390894, 'FREQ_D': 0.05704511, 'FREQ_C': 0.0193078, 'FREQ_Q': 0.0367281, 'FREQ_E': 0.05805891, 'FREQ_G': 0.08325181, 'FREQ_H': 0.0244313, 'FREQ_I': 0.048466, 'FREQ_L': 0.08620901, 'FREQ_K': 0.06202861, 'FREQ_M': 0.0195027, 'FREQ_F': 0.0384319, 'FREQ_P': 0.0457631, 'FREQ_S': 0.06951791, 'FREQ_T': 0.06101271, 'FREQ_W': 0.0143859, 'FREQ_Y': 0.0352742, 'FREQ_V': 0.07089561}, \
'rtREV': {'FREQ_A': 0.0646, 'FREQ_R': 0.0453, 'FREQ_N': 0.0376, 'FREQ_D': 0.0422, 'FREQ_C': 0.0114, 'FREQ_Q': 0.0606, 'FREQ_E': 0.0607, 'FREQ_G': 0.0639, 'FREQ_H': 0.0273, 'FREQ_I': 0.0679, 'FREQ_L': 0.1018, 'FREQ_K': 0.0751, 'FREQ_M': 0.015, 'FREQ_F': 0.0287, 'FREQ_P': 0.0681, 'FREQ_S': 0.0488, 'FREQ_T': 0.0622, 'FREQ_W': 0.0251, 'FREQ_Y': 0.0318, 'FREQ_V': 0.0619}, \
'cpREV': {'FREQ_A': 0.0755, 'FREQ_R': 0.0621, 'FREQ_N': 0.041, 'FREQ_D': 0.0371, 'FREQ_C': 0.0091, 'FREQ_Q': 0.0382, 'FREQ_E': 0.0495, 'FREQ_G': 0.0838, 'FREQ_H': 0.0246, 'FREQ_I': 0.0806, 'FREQ_L': 0.1011, 'FREQ_K': 0.0504, 'FREQ_M': 0.022, 'FREQ_F': 0.0506, 'FREQ_P': 0.0431, 'FREQ_S': 0.0622, 'FREQ_T': 0.0543, 'FREQ_W': 0.0181, 'FREQ_Y': 0.0307, 'FREQ_V': 0.066}, \
'VT': {'FREQ_A': 0.077076462, 'FREQ_R': 0.050081937, 'FREQ_N': 0.04623774, 'FREQ_D': 0.053792986, 'FREQ_C': 0.014453339, 'FREQ_Q': 0.040892361, 'FREQ_E': 0.063357934, 'FREQ_G': 0.065567236, 'FREQ_H': 0.021880269, 'FREQ_I': 0.05919697, 'FREQ_L': 0.097646128, 'FREQ_K': 0.059207941, 'FREQ_M': 0.022069588, 'FREQ_F': 0.041350852, 'FREQ_P': 0.04768716, 'FREQ_S': 0.070729517, 'FREQ_T': 0.056775916, 'FREQ_W': 0.01270198, 'FREQ_Y': 0.032374605, 'FREQ_V': 0.066919082}, \
'Blosum62': {'FREQ_A': 0.074, 'FREQ_R': 0.052, 'FREQ_N': 0.045, 'FREQ_D': 0.054, 'FREQ_C': 0.025, 'FREQ_Q': 0.034, 'FREQ_E': 0.054, 'FREQ_G': 0.074, 'FREQ_H': 0.026, 'FREQ_I': 0.068, 'FREQ_L': 0.099, 'FREQ_K': 0.058, 'FREQ_M': 0.025, 'FREQ_F': 0.047, 'FREQ_P': 0.039, 'FREQ_S': 0.057, 'FREQ_T': 0.051, 'FREQ_W': 0.013, 'FREQ_Y': 0.032, 'FREQ_V': 0.073}, \
'mtMAM': {'FREQ_A': 0.0692, 'FREQ_R': 0.0184, 'FREQ_N': 0.04, 'FREQ_D': 0.0186, 'FREQ_C': 0.0065, 'FREQ_Q': 0.0238, 'FREQ_E': 0.0236, 'FREQ_G': 0.0557, 'FREQ_H': 0.0277, 'FREQ_I': 0.0905, 'FREQ_L': 0.1675, 'FREQ_K': 0.0221, 'FREQ_M': 0.0561, 'FREQ_F': 0.0611, 'FREQ_P': 0.0536, 'FREQ_S': 0.0725, 'FREQ_T': 0.087, 'FREQ_W': 0.0293, 'FREQ_Y': 0.034, 'FREQ_V': 0.0428}, \
'LG': {'FREQ_A': 0.07906592, 'FREQ_R': 0.05594094, 'FREQ_N': 0.04197696, 'FREQ_D': 0.05305195, 'FREQ_C': 0.01293699, 'FREQ_Q': 0.04076696, 'FREQ_E': 0.07158593, 'FREQ_G': 0.05733694, 'FREQ_H': 0.02235498, 'FREQ_I': 0.06215694, 'FREQ_L': 0.0990809, 'FREQ_K': 0.06459994, 'FREQ_M': 0.02295098, 'FREQ_F': 0.04230196, 'FREQ_P': 0.04403996, 'FREQ_S': 0.06119694, 'FREQ_T': 0.05328695, 'FREQ_W': 0.01206599, 'FREQ_Y': 0.03415497, 'FREQ_V': 0.06914693}, \
'mtART': {'FREQ_A': 0.054116, 'FREQ_R': 0.018227, 'FREQ_N': 0.039903, 'FREQ_D': 0.02016, 'FREQ_C': 0.009709, 'FREQ_Q': 0.018781, 'FREQ_E': 0.024289, 'FREQ_G': 0.068183, 'FREQ_H': 0.024518, 'FREQ_I': 0.092638, 'FREQ_L': 0.148658, 'FREQ_K': 0.021718, 'FREQ_M': 0.061453, 'FREQ_F': 0.088668, 'FREQ_P': 0.041826, 'FREQ_S': 0.09103, 'FREQ_T': 0.049194, 'FREQ_W': 0.029786, 'FREQ_Y': 0.039443, 'FREQ_V': 0.0577}, \
'mtZOA': {'FREQ_A': 0.06887993, 'FREQ_R': 0.02103698, 'FREQ_N': 0.03038997, 'FREQ_D': 0.02069598, 'FREQ_C': 0.00996599, 'FREQ_Q': 0.01862298, 'FREQ_E': 0.02498898, 'FREQ_G': 0.07196793, 'FREQ_H': 0.02681397, 'FREQ_I': 0.08507191, 'FREQ_L': 0.15671684, 'FREQ_K': 0.01927598, 'FREQ_M': 0.05065195, 'FREQ_F': 0.08171192, 'FREQ_P': 0.04480296, 'FREQ_S': 0.08053492, 'FREQ_T': 0.05638594, 'FREQ_W': 0.02799797, 'FREQ_Y': 0.03740396, 'FREQ_V': 0.06608293}, \
'PMB': {'FREQ_A': 0.07559244, 'FREQ_R': 0.05379462, 'FREQ_N': 0.03769623, 'FREQ_D': 0.04469553, 'FREQ_C': 0.02849715, 'FREQ_Q': 0.03389661, 'FREQ_E': 0.05349465, 'FREQ_G': 0.0779922, 'FREQ_H': 0.029997, 'FREQ_I': 0.05989401, 'FREQ_L': 0.09579042, 'FREQ_K': 0.0519948, 'FREQ_M': 0.02189781, 'FREQ_F': 0.0449955, 'FREQ_P': 0.0419958, 'FREQ_S': 0.06819318, 'FREQ_T': 0.05639436, 'FREQ_W': 0.01569843, 'FREQ_Y': 0.0359964, 'FREQ_V': 0.07149285}, \
'HIVb': {'FREQ_A': 0.060490222, 'FREQ_R': 0.066039665, 'FREQ_N': 0.044127815, 'FREQ_D': 0.042109048, 'FREQ_C': 0.020075899, 'FREQ_Q': 0.053606488, 'FREQ_E': 0.071567447, 'FREQ_G': 0.072308239, 'FREQ_H': 0.022293943, 'FREQ_I': 0.069730629, 'FREQ_L': 0.098851122, 'FREQ_K': 0.056968211, 'FREQ_M': 0.019768318, 'FREQ_F': 0.028809447, 'FREQ_P': 0.046025282, 'FREQ_S': 0.05060433, 'FREQ_T': 0.053636813, 'FREQ_W': 0.033011601, 'FREQ_Y': 0.028350243, 'FREQ_V': 0.061625237}, \
'HIVw': {'FREQ_A': 0.0377494, 'FREQ_R': 0.057321, 'FREQ_N': 0.0891129, 'FREQ_D': 0.0342034, 'FREQ_C': 0.0240105, 'FREQ_Q': 0.0437824, 'FREQ_E': 0.0618606, 'FREQ_G': 0.0838496, 'FREQ_H': 0.0156076, 'FREQ_I': 0.0983641, 'FREQ_L': 0.0577867, 'FREQ_K': 0.0641682, 'FREQ_M': 0.0158419, 'FREQ_F': 0.0422741, 'FREQ_P': 0.0458601, 'FREQ_S': 0.0550846, 'FREQ_T': 0.0813774, 'FREQ_W': 0.019597, 'FREQ_Y': 0.0205847, 'FREQ_V': 0.0515638}, \
'JTTDCMut': {'FREQ_A': 0.07686192, 'FREQ_R': 0.05105695, 'FREQ_N': 0.04254596, 'FREQ_D': 0.05126895, 'FREQ_C': 0.02027898, 'FREQ_Q': 0.04106096, 'FREQ_E': 0.06181994, 'FREQ_G': 0.07471393, 'FREQ_H': 0.02298298, 'FREQ_I': 0.05256895, 'FREQ_L': 0.09111091, 'FREQ_K': 0.05949794, 'FREQ_M': 0.02341398, 'FREQ_F': 0.04052996, 'FREQ_P': 0.05053195, 'FREQ_S': 0.06822493, 'FREQ_T': 0.05851794, 'FREQ_W': 0.01433599, 'FREQ_Y': 0.03230297, 'FREQ_V': 0.06637393}, \
'FLU': {'FREQ_A': 0.04707195, 'FREQ_R': 0.05090995, 'FREQ_N': 0.07421393, 'FREQ_D': 0.04785995, 'FREQ_C': 0.02502197, 'FREQ_Q': 0.03330397, 'FREQ_E': 0.05458695, 'FREQ_G': 0.07637292, 'FREQ_H': 0.01996398, 'FREQ_I': 0.06713393, 'FREQ_L': 0.07149793, 'FREQ_K': 0.05678494, 'FREQ_M': 0.01815098, 'FREQ_F': 0.03049597, 'FREQ_P': 0.05065595, 'FREQ_S': 0.08840891, 'FREQ_T': 0.07433893, 'FREQ_W': 0.01852398, 'FREQ_Y': 0.03147397, 'FREQ_V': 0.06322894}, \
'mtMet': {'FREQ_A': 0.043793213, 'FREQ_R': 0.012957804, 'FREQ_N': 0.057001317, 'FREQ_D': 0.016899005, 'FREQ_C': 0.011330503, 'FREQ_Q': 0.018018105, 'FREQ_E': 0.022538507, 'FREQ_G': 0.047050114, 'FREQ_H': 0.017183705, 'FREQ_I': 0.089779427, 'FREQ_L': 0.155226047, 'FREQ_K': 0.039913512, 'FREQ_M': 0.06744432, 'FREQ_F': 0.088448027, 'FREQ_P': 0.037528211, 'FREQ_S': 0.093752228, 'FREQ_T': 0.063579019, 'FREQ_W': 0.022671307, 'FREQ_Y': 0.041568212, 'FREQ_V': 0.053317416}, \
'mtVer': {'FREQ_A': 0.070820265, 'FREQ_R': 0.014049893, 'FREQ_N': 0.045209877, 'FREQ_D': 0.014793693, 'FREQ_C': 0.006814197, 'FREQ_Q': 0.026340887, 'FREQ_E': 0.021495189, 'FREQ_G': 0.044239978, 'FREQ_H': 0.024230988, 'FREQ_I': 0.090735055, 'FREQ_L': 0.172309914, 'FREQ_K': 0.027381186, 'FREQ_M': 0.056193972, 'FREQ_F': 0.049775775, 'FREQ_P': 0.054386273, 'FREQ_S': 0.074421863, 'FREQ_T': 0.108809946, 'FREQ_W': 0.025652687, 'FREQ_Y': 0.026484687, 'FREQ_V': 0.045853677}, \
'mtInv': {'FREQ_A': 0.031742313, 'FREQ_R': 0.010900704, 'FREQ_N': 0.061579225, 'FREQ_D': 0.016149206, 'FREQ_C': 0.013570105, 'FREQ_Q': 0.014644106, 'FREQ_E': 0.022311209, 'FREQ_G': 0.047847519, 'FREQ_H': 0.011641805, 'FREQ_I': 0.094322338, 'FREQ_L': 0.14940706, 'FREQ_K': 0.044438718, 'FREQ_M': 0.077262531, 'FREQ_F': 0.102287041, 'FREQ_P': 0.026290211, 'FREQ_S': 0.105939042, 'FREQ_T': 0.042869117, 'FREQ_W': 0.020701008, 'FREQ_Y': 0.046556719, 'FREQ_V': 0.059540024}, \
'Q.pfam': {'FREQ_A': 0.085788, 'FREQ_R': 0.057731, 'FREQ_N': 0.042028, 'FREQ_D': 0.056462, 'FREQ_C': 0.010447, 'FREQ_Q': 0.039548, 'FREQ_E': 0.067799, 'FREQ_G': 0.064861, 'FREQ_H': 0.02104, 'FREQ_I': 0.055398, 'FREQ_L': 0.100413, 'FREQ_K': 0.059401, 'FREQ_M': 0.019898, 'FREQ_F': 0.042789, 'FREQ_P': 0.039579, 'FREQ_S': 0.069262, 'FREQ_T': 0.055498, 'FREQ_W': 0.01443, 'FREQ_Y': 0.033233, 'FREQ_V': 0.064396}, \
'Q.pfam_gb': {'FREQ_A': 0.08766, 'FREQ_R': 0.058154, 'FREQ_N': 0.037239, 'FREQ_D': 0.048117, 'FREQ_C': 0.013233, 'FREQ_Q': 0.03808, 'FREQ_E': 0.063213, 'FREQ_G': 0.059035, 'FREQ_H': 0.021871, 'FREQ_I': 0.061155, 'FREQ_L': 0.11158, 'FREQ_K': 0.056999, 'FREQ_M': 0.022763, 'FREQ_F': 0.046732, 'FREQ_P': 0.035355, 'FREQ_S': 0.065285, 'FREQ_T': 0.052818, 'FREQ_W': 0.01555, 'FREQ_Y': 0.035618, 'FREQ_V': 0.069541}, \
'Q.LG': {'FREQ_A': 0.080009, 'FREQ_R': 0.052947, 'FREQ_N': 0.041171, 'FREQ_D': 0.050146, 'FREQ_C': 0.015018, 'FREQ_Q': 0.035929, 'FREQ_E': 0.061392, 'FREQ_G': 0.064793, 'FREQ_H': 0.021709, 'FREQ_I': 0.063895, 'FREQ_L': 0.106292, 'FREQ_K': 0.057047, 'FREQ_M': 0.02344, 'FREQ_F': 0.047712, 'FREQ_P': 0.039604, 'FREQ_S': 0.06298, 'FREQ_T': 0.052863, 'FREQ_W': 0.014987, 'FREQ_Y': 0.037434, 'FREQ_V': 0.070634}, \
'Q.bird': {'FREQ_A': 0.066363, 'FREQ_R': 0.054021, 'FREQ_N': 0.037784, 'FREQ_D': 0.047511, 'FREQ_C': 0.022651, 'FREQ_Q': 0.048841, 'FREQ_E': 0.071571, 'FREQ_G': 0.058368, 'FREQ_H': 0.025403, 'FREQ_I': 0.045108, 'FREQ_L': 0.100181, 'FREQ_K': 0.061361, 'FREQ_M': 0.021069, 'FREQ_F': 0.03823, 'FREQ_P': 0.053861, 'FREQ_S': 0.089298, 'FREQ_T': 0.053536, 'FREQ_W': 0.012313, 'FREQ_Y': 0.027173, 'FREQ_V': 0.065359}, \
'Q.insect': {'FREQ_A': 0.063003, 'FREQ_R': 0.049585, 'FREQ_N': 0.04755, 'FREQ_D': 0.048622, 'FREQ_C': 0.015291, 'FREQ_Q': 0.044058, 'FREQ_E': 0.072012, 'FREQ_G': 0.03781, 'FREQ_H': 0.022358, 'FREQ_I': 0.066563, 'FREQ_L': 0.107325, 'FREQ_K': 0.080621, 'FREQ_M': 0.023976, 'FREQ_F': 0.041578, 'FREQ_P': 0.028532, 'FREQ_S': 0.081767, 'FREQ_T': 0.055167, 'FREQ_W': 0.009698, 'FREQ_Y': 0.032219, 'FREQ_V': 0.072265}, \
'Q.mammal': {'FREQ_A': 0.067997, 'FREQ_R': 0.055503, 'FREQ_N': 0.036288, 'FREQ_D': 0.046867, 'FREQ_C': 0.021435, 'FREQ_Q': 0.050281, 'FREQ_E': 0.068935, 'FREQ_G': 0.055323, 'FREQ_H': 0.02641, 'FREQ_I': 0.041953, 'FREQ_L': 0.101191, 'FREQ_K': 0.060037, 'FREQ_M': 0.019662, 'FREQ_F': 0.036237, 'FREQ_P': 0.055146, 'FREQ_S': 0.096864, 'FREQ_T': 0.057136, 'FREQ_W': 0.011785, 'FREQ_Y': 0.02473, 'FREQ_V': 0.066223}, \
'Q.plant': {'FREQ_A': 0.074923, 'FREQ_R': 0.0505, 'FREQ_N': 0.038734, 'FREQ_D': 0.053195, 'FREQ_C': 0.0113, 'FREQ_Q': 0.037499, 'FREQ_E': 0.068513, 'FREQ_G': 0.059627, 'FREQ_H': 0.021204, 'FREQ_I': 0.058991, 'FREQ_L': 0.102504, 'FREQ_K': 0.067306, 'FREQ_M': 0.022371, 'FREQ_F': 0.043798, 'FREQ_P': 0.037039, 'FREQ_S': 0.084451, 'FREQ_T': 0.04785, 'FREQ_W': 0.012322, 'FREQ_Y': 0.030777, 'FREQ_V': 0.077097}, \
'Q.yeast': {'FREQ_A': 0.059954, 'FREQ_R': 0.042032, 'FREQ_N': 0.052518, 'FREQ_D': 0.054641, 'FREQ_C': 0.008189, 'FREQ_Q': 0.040467, 'FREQ_E': 0.070691, 'FREQ_G': 0.039935, 'FREQ_H': 0.018393, 'FREQ_I': 0.069555, 'FREQ_L': 0.109563, 'FREQ_K': 0.081967, 'FREQ_M': 0.018694, 'FREQ_F': 0.046979, 'FREQ_P': 0.031382, 'FREQ_S': 0.091102, 'FREQ_T': 0.055887, 'FREQ_W': 0.010241, 'FREQ_Y': 0.033496, 'FREQ_V': 0.064313}, \
'FLAVI': {'FREQ_A': 0.0775, 'FREQ_R': 0.053813, 'FREQ_N': 0.03395, 'FREQ_D': 0.034973, 'FREQ_C': 0.014056, 'FREQ_Q': 0.030139, 'FREQ_E': 0.054825, 'FREQ_G': 0.086284, 'FREQ_H': 0.01821, 'FREQ_I': 0.063272, 'FREQ_L': 0.103857, 'FREQ_K': 0.059646, 'FREQ_M': 0.040389, 'FREQ_F': 0.03363, 'FREQ_P': 0.036649, 'FREQ_S': 0.060915, 'FREQ_T': 0.076327, 'FREQ_W': 0.030152, 'FREQ_Y': 0.020069, 'FREQ_V': 0.071343}}
def numPara():
global het_num_para
global dna_num_para
het_num_para={'uniform': 0,'+G4': 1,'+I': 1,'+I+G4': 2,'+R2': 2,'+R3': 4,'+R4': 6,'+R5': 8,'+R6': 10,'+R7': 12,'+R8': 14,'+R9': 16,'+R10': 18, '+ASC+G4': 1}
dna_num_para={'F81+F': 3, 'GTR+F': 8, 'HKY+F': 4, 'JC': 0, 'K2P': 1, 'K3P': 2, 'K3Pu+F': 5, 'SYM': 5, 'TIM+F': 6, 'TIM2+F': 6, 'TIM2e': 3, \
'TIM3+F': 6, 'TIM3e': 3, 'TIMe': 3, 'TN+F': 5, 'TNe': 2, 'TPM2': 2, 'TPM2u+F': 5, 'TPM3': 2, 'TPM3u+F': 5, 'TVM+F': 7, 'TVMe': 4}
def CheckFiles():
'''Check if all relevent files with the given prefix can be found. Should one of the files be missing, the program is being exited.'''
global initial
global unique
global unique_ctrl
unique_ctrl = True
initial = False
unique = False
ctrl = 0
if path.exists(ali_file) == False:
print('Could not find input file '+ali_file)
ctrl = 1
else:
print('...file '+ali_file+' has been detected')
for file in ['.iqtree', '.log', '.mldist', '.model.gz', '.treefile']:
if path.exists(prefix+file) == False:
print('ERROR: Could not find input file '+prefix+file+'.')
ctrl = 1
else:
print('...file '+prefix+file+' has been detected')
if path.exists(prefix+'-initialtree.iqtree') == True and path.exists(prefix+'-initialtree.treefile') == True:
print('...file '+prefix+'-initialtree.iqtree has been detected')
print('...file '+prefix+'-initialtree.treefile has been detected')
initial = True
if path.exists(prefix+'.uniqueseq.phy') == True:
print('...file '+prefix+'.uniqueseq.phy has been detected')
unique = True
for file in ['-keep_ident.iqtree', '-keep_ident.log', '-keep_ident.mldist', '-keep_ident.model.gz', '-keep_ident.treefile']:
if path.exists(prefix+file) == False:
print('WARNING: Could not find file '+prefix+file+'.')
unique_ctrl = False
else:
print('...file '+prefix+file+' has been detected')
if ctrl != 0:
sys.exit(2)
def OpenFiles():
'''The function opens and reads in all relevant files. The variables storing the lines of the read in files will be global variables.'''
global initial
global unique
global unique_ctrl
global iqtree
global log
global mldist
global check
global initial_iqtree
global iqtree_keep
global log_keep
global mldist_keep
global check_keep
initial_iqtree = None
iqtree_keep = None
log_keep = None
mldist_keep = None
check_keep = None
with open(prefix+'.iqtree') as t:
iqtree = t.readlines()
with open(prefix+'.log') as t:
log = t.readlines()
mldist = pd.read_csv(prefix+'.mldist', sep=' ', skipinitialspace = True, skiprows = 1, header = None)
mldist.pop(mldist.columns[-1])
with gzip.open(prefix+'.model.gz') as t:
check = [x.decode('utf8').strip() for x in t.readlines()]
if initial is True:
with open(prefix+'-initialtree.iqtree') as t:
initial_iqtree = t.readlines()
if unique is True and unique_ctrl is True:
with open(prefix+'-keep_ident.iqtree') as t:
iqtree_keep = t.readlines()
with open(prefix+'-keep_ident.log') as t:
log_keep = t.readlines()
mldist_keep = pd.read_csv(prefix+'-keep_ident.mldist', sep=' ', skipinitialspace = True, skiprows = 1, header = None)
mldist_keep.pop(mldist_keep.columns[-1])
with gzip.open(prefix+'-keep_ident.model.gz') as t:
check_keep = [x.decode('utf8').strip() for x in t.readlines()]
def CheckAliType():
'''
Function that checks if the underlying alignment that has been run through the workflow is a DNA or protein alignment.
It will update the global variable "type".
'''
global type
global initial
type = ''
for i in range (len(log)):
if log[i][:len('Command: ')] == 'Command: ':
if '--seqtype' in log[i]:
if log[i].split('--seqtype ')[1][:len('AA')] == 'AA':
type = 'AA'
elif log[i].split('--seqtype ')[1][:len('DNA')] == 'DNA':
type = 'DNA'
if 'initialtree' in log[i]:
initial = True
if type != '':
return
if log[i][:len('Alignment most likely contains ')] == 'Alignment most likely contains ':
if 'DNA' in log[i]:
type = 'DNA'
elif 'protein' in log[i]:
type = 'AA'
def InitialiseDataFrames():
'''Function that initialises the DataFrames that will store all the relevent data.'''
global seq_para
global ali_para
global model_para
global tree_para
global branch_para
global file_name_dic
branch_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', \
'TREE_TYPE', 'BRANCH_INDEX', 'BRANCH_TYPE', 'BL', 'SPLIT_SIZE', \
'1_MIN_PATH', '1_MAX_PATH', '1_MEAN_PATH', '1_MEDIAN_PATH', \
'2_MIN_PATH', '2_MAX_PATH', '2_MEAN_PATH', '2_MEDIAN_PATH'])
if type == 'DNA':
seq_para = pd.DataFrame(columns = ['ALI_ID', 'SEQ_INDEX', 'SEQ_NAME', 'FRAC_WILDCARDS_GAPS', 'CHI2_P_VALUE', 'CHI2_PASSED', 'EXCLUDED', 'IDENTICAL_TO', 'FREQ_A', 'FREQ_C', 'FREQ_G', 'FREQ_T', 'SEQ'])
ali_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'SEQ_TYPE','SEQUENCES', 'COLUMNS', 'DISTINCT_PATTERNS', 'PARSIMONY_INFORMATIVE_SITES', \
'SINGELTON_SITES', 'CONSTANT_SITES', 'FRAC_WILDCARDS_GAPS', 'FAILED_CHI2', 'IDENTICAL_SEQ', 'EXCLUDED_SEQ'])
model_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'KEEP_IDENT', 'MODEL', 'BASE_MODEL', 'FREQ_TYPE', 'MODEL_RATE_HETEROGENEITY', 'NUM_RATE_CAT', \
'LOGL', 'AIC', 'WEIGHTED_AIC', 'CONFIDENCE_AIC', 'AICC', 'WEIGHTED_AICC', 'CONFIDENCE_AICC', 'BIC', 'WEIGHTED_BIC', 'CONFIDENCE_BIC', \
'CAIC', 'WEIGHTED_CAIC', 'CONFIDENCE_CAIC', 'ABIC', 'WEIGHTED_ABIC', 'CONFIDENCE_ABIC', \
'NUM_FREE_PARAMETERS', 'NUM_MODEL_PARAMETERS', 'NUM_BRANCHES', 'TREE_LENGTH', \
'PROP_INVAR', 'ALPHA', 'FREQ_A', 'FREQ_C', 'FREQ_G', 'FREQ_T', 'RATE_AC', 'RATE_AG', 'RATE_AT', 'RATE_CG', 'RATE_CT', 'RATE_GT', \
'PROP_CAT_1', 'REL_RATE_CAT_1', 'PROP_CAT_2', 'REL_RATE_CAT_2', \
'PROP_CAT_3', 'REL_RATE_CAT_3', 'PROP_CAT_4', 'REL_RATE_CAT_4', 'PROP_CAT_5', 'REL_RATE_CAT_5', \
'PROP_CAT_6', 'REL_RATE_CAT_6', 'PROP_CAT_7', 'REL_RATE_CAT_7', 'PROP_CAT_8', 'REL_RATE_CAT_8', \
'PROP_CAT_9', 'REL_RATE_CAT_19', 'PROP_CAT_10', 'REL_RATE_CAT_10'])
tree_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'TREE_TYPE', 'CHOICE_CRITERIUM', 'KEEP_IDENT', \
'MODEL', 'BASE_MODEL', 'FREQ_TYPE', 'MODEL_RATE_HETEROGENEITY', 'NUM_RATE_CAT', \
'LOGL', 'UNCONSTRAINED_LOGL', 'AIC', 'AICC', 'BIC', 'CAIC', 'ABIC', 'NUM_FREE_PARAMETERS', 'NUM_MODEL_PARAMETERS', 'NUM_BRANCHES', \
'PROP_INVAR', 'ALPHA', 'FREQ_A', 'FREQ_C', 'FREQ_G', 'FREQ_T', 'RATE_AC', 'RATE_AG', 'RATE_AT', 'RATE_CG', 'RATE_CT', 'RATE_GT', \
'PROP_CAT_1', 'REL_RATE_CAT_1', 'PROP_CAT_2', 'REL_RATE_CAT_2', \
'PROP_CAT_3', 'REL_RATE_CAT_3', 'PROP_CAT_4', 'REL_RATE_CAT_4', 'PROP_CAT_5', 'REL_RATE_CAT_5', \
'PROP_CAT_6', 'REL_RATE_CAT_6', 'PROP_CAT_7', 'REL_RATE_CAT_7', 'PROP_CAT_8', 'REL_RATE_CAT_8', \
'PROP_CAT_9', 'REL_RATE_CAT_19', 'PROP_CAT_10', 'REL_RATE_CAT_10', 'TREE_LENGTH', 'SUM_IBL', 'TREE_DIAMETER', 'DIST_MIN', 'DIST_MAX', \
'DIST_MEAN', 'DIST_MEDIAN', 'DIST_VAR', \
'BL_MIN', 'BL_MAX', 'BL_MEAN', 'BL_MEDIAN', 'BL_VAR', \
'IBL_MIN', 'IBL_MAX', 'IBL_MEAN', 'IBL_MEDIAN', 'IBL_VAR', \
'EBL_MIN', 'EBL_MAX', 'EBL_MEAN', 'EBL_MEDIAN', 'EBL_VAR', \
'POT_FF_7', 'POT_FF_8', 'POT_FF_9', 'POT_FF_10', 'NEWICK_STRING'])
elif type == 'AA':
seq_para = pd.DataFrame(columns = ['ALI_ID', 'SEQ_INDEX', 'SEQ_NAME', 'FRAC_WILDCARDS_GAPS', 'CHI2_P_VALUE', 'CHI2_PASSED', 'EXCLUDED', 'IDENTICAL_TO', 'FREQ_A', 'FREQ_R','FREQ_N',\
'FREQ_D','FREQ_C','FREQ_Q','FREQ_E','FREQ_G','FREQ_H','FREQ_I','FREQ_L','FREQ_K','FREQ_M','FREQ_F',\
'FREQ_P','FREQ_S','FREQ_T','FREQ_W', 'FREQ_Y', 'FREQ_V', 'SEQ'])
ali_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'SEQ_TYPE', 'SEQUENCES', 'COLUMNS', 'DISTINCT_PATTERNS', 'PARSIMONY_INFORMATIVE_SITES', \
'SINGELTON_SITES', 'CONSTANT_SITES', 'FRAC_WILDCARDS_GAPS', 'FAILED_CHI2', 'IDENTICAL_SEQ', 'EXCLUDED_SEQ'])
model_para = pd.DataFrame(columns=['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'KEEP_IDENT', 'MODEL', 'BASE_MODEL', 'FREQ_TYPE', 'MODEL_RATE_HETEROGENEITY', 'NUM_RATE_CAT', \
'LOGL', 'AIC', 'WEIGHTED_AIC', 'CONFIDENCE_AIC', 'AICC', 'WEIGHTED_AICC', 'CONFIDENCE_AICC', 'BIC', 'WEIGHTED_BIC', 'CONFIDENCE_BIC', \
'CAIC', 'WEIGHTED_CAIC', 'CONFIDENCE_CAIC', 'ABIC', 'WEIGHTED_ABIC', 'CONFIDENCE_ABIC', 'NUM_FREE_PARAMETERS', 'NUM_MODEL_PARAMETERS', 'NUM_BRANCHES', 'TREE_LENGTH', \
'PROP_INVAR', 'ALPHA', 'FREQ_A', 'FREQ_R','FREQ_N','FREQ_D','FREQ_C','FREQ_Q','FREQ_E','FREQ_G','FREQ_H','FREQ_I','FREQ_L','FREQ_K','FREQ_M','FREQ_F', \
'FREQ_P','FREQ_S','FREQ_T','FREQ_W', 'FREQ_Y', 'FREQ_V', \
'PROP_CAT_1', 'REL_RATE_CAT_1', 'PROP_CAT_2', 'REL_RATE_CAT_2', \
'PROP_CAT_3', 'REL_RATE_CAT_3', 'PROP_CAT_4', 'REL_RATE_CAT_4', 'PROP_CAT_5', 'REL_RATE_CAT_5', \
'PROP_CAT_6', 'REL_RATE_CAT_6', 'PROP_CAT_7', 'REL_RATE_CAT_7', 'PROP_CAT_8', 'REL_RATE_CAT_8', \
'PROP_CAT_9', 'REL_RATE_CAT_19', 'PROP_CAT_10', 'REL_RATE_CAT_10'])
tree_para = pd.DataFrame(columns = ['ALI_ID', 'IQTREE_VERSION', 'RANDOM_SEED', 'TIME_STAMP', 'TREE_TYPE', 'CHOICE_CRITERIUM', 'KEEP_IDENT', \
'MODEL', 'BASE_MODEL', 'FREQ_TYPE', 'MODEL_RATE_HETEROGENEITY', 'NUM_RATE_CAT', \
'LOGL', 'UNCONSTRAINED_LOGL', 'AIC', 'AICC', 'BIC', 'CAIC', 'ABIC', 'NUM_FREE_PARAMETERS', 'NUM_MODEL_PARAMETERS', 'NUM_BRANCHES', \
'PROP_INVAR', 'ALPHA', 'FREQ_A', 'FREQ_R','FREQ_N','FREQ_D','FREQ_C','FREQ_Q','FREQ_E','FREQ_G','FREQ_H','FREQ_I','FREQ_L','FREQ_K','FREQ_M','FREQ_F', \
'FREQ_P','FREQ_S','FREQ_T','FREQ_W', 'FREQ_Y', 'FREQ_V', \
'PROP_CAT_1', 'REL_RATE_CAT_1', 'PROP_CAT_2', 'REL_RATE_CAT_2', \
'PROP_CAT_3', 'REL_RATE_CAT_3', 'PROP_CAT_4', 'REL_RATE_CAT_4', 'PROP_CAT_5', 'REL_RATE_CAT_5', \
'PROP_CAT_6', 'REL_RATE_CAT_6', 'PROP_CAT_7', 'REL_RATE_CAT_7', 'PROP_CAT_8', 'REL_RATE_CAT_8', \
'PROP_CAT_9', 'REL_RATE_CAT_19', 'PROP_CAT_10', 'REL_RATE_CAT_10', 'TREE_LENGTH', 'SUM_IBL', 'TREE_DIAMETER', 'DIST_MIN', 'DIST_MAX', \
'DIST_MEAN', 'DIST_MEDIAN', 'DIST_VAR', 'BL_MIN', 'BL_MAX', 'BL_MEAN', 'BL_MEDIAN', 'BL_VAR', \
'IBL_MIN', 'IBL_MAX', 'IBL_MEAN', 'IBL_MEDIAN', 'IBL_VAR', \
'EBL_MIN', 'EBL_MAX', 'EBL_MEAN', 'EBL_MEDIAN', 'EBL_VAR', \
'POT_FF_7', 'POT_FF_8', 'POT_FF_9', 'POT_FF_10', 'NEWICK_STRING'])
file_name_dic = {'seq_para': out_prefix+'_seq_parameters.tsv', 'ali_para': out_prefix+'_ali_parameters.tsv', 'tree_para': out_prefix+'_tree_parameters.tsv', \
'branch_para': out_prefix+'_branch_parameters.tsv', 'model_para': out_prefix+'_model_parameters.tsv'}
seq_para.to_csv(file_name_dic['seq_para'], index = False, sep = '\t')
ali_para.to_csv(file_name_dic['ali_para'], index = False, sep = '\t')
tree_para.to_csv(file_name_dic['tree_para'], index = False, sep = '\t')
branch_para.to_csv(file_name_dic['branch_para'], index = False, sep = '\t')
model_para.to_csv(file_name_dic['model_para'], index = False, sep = '\t')
def OpenUniqueFile(file: str) -> dict:
'''
Function that reads in the "phylib" file including only unique sequences created by IQ-Tree 2.
Input: The name of the file to be read in.
Returns: a dictionary with the name of the sequence as key and the sequence as entry
'''
seq = {}
with open (file) as t:
phy_file= t.readlines()
for i in range (1,len(phy_file)):
phy_file[i] = phy_file[i].strip('\n')
phy_file[i] = phy_file[i].split(' ')
while '' in phy_file[i]:
phy_file[i].remove('')
seq.setdefault(phy_file[i][0], phy_file[i][1])
return seq
def GetFreqPerSeq(line: str, states: list) -> dict:
'''
Function that calculates the state frequencies in a given sequence.
Input: a sequence (line), a list with the states to be calculated (with prefix "FREQ_") (states).
Returns: A dictionary containing the frequencies.
'''
count_states = Counter(line.upper())
freqs = {}
sum = 0
for state in states:
freqs.setdefault(state, count_states[state[len('FREQ_'):]])
sum += count_states[state[len('FREQ_'):]]
for key in freqs.keys():
freqs[key] = freqs[key]/sum
freqs['FRAC_WILDCARDS_GAPS'] = 1-sum/len(line)
return freqs
def CheckStateFreq():
'''
Function that calculates the state frequencies in the original alignment file or, should it exist, based on the unique.phy file created by IQ-Tree2.
Furthermore, it reads in all sequences from the original alignment and stores it in the seq_para DataFrame.
'''
# Declare global variables that will be introduced in this function
global freq_stats
global freq_stats_unique
global name_dic
global name_dic_unique
# Declare already existing global variables.
global ali_para
global seq_para
states = []
for key in seq_para.columns:
if key[:len('FREQ_')] == 'FREQ_' :
states.append(key)
# Create dictionaries to store infromation regarding the names and index of the sequences
name_dic = {}
name_dic_unique = {}
# Read in sequences with SeqIO from the Biopython library.
if '.phy' in ali_file:
parsed_seq = SeqIO.parse(ali_file, 'phylip')
elif '.fasta' in ali_file or '.fa' in ali_file or '.faa' in ali_file:
parsed_seq = SeqIO.parse(ali_file, 'fasta')
# For each sequence, calculate the frequencies. Update seq_para DataFrame with results.
# Set name_dic dctionary with the name of the sequence as key and the index as dictionary entry.
index = 1
for seq_record in parsed_seq:
freqs = GetFreqPerSeq(str(seq_record.seq).upper(), states)
freqs.update({'SEQ_INDEX': str(index), 'SEQ_NAME': str(seq_record.id).strip('\n'), 'SEQ': str(seq_record.seq)})
seq_para = seq_para.append(freqs, ignore_index = True)
name_dic.setdefault(str(seq_record.id).strip('\n'), str(index))
index += 1
# Should a unique sequence file exist, calculate state frequencies based on the sequences in the file.
# Otherwise, calculate state frequencies based on original alignment file.
# The results are stored in the (global) dictionary freq_stats.
all_seq = ''
for x in range (len(seq_para['SEQ'])):
all_seq += seq_para['SEQ'][x].upper()
freq_stats = GetFreqPerSeq(all_seq, states)
if unique is True:
columns = ['SEQ']+states
seq_para_unique = pd.DataFrame(columns=columns)
index = 1
phy_file = OpenUniqueFile(prefix+'.uniqueseq.phy')
for seq in phy_file.keys():
freqs = GetFreqPerSeq(phy_file[seq].upper(), states)
freqs.update({'SEQ': phy_file[seq].upper()})
name_dic_unique.setdefault(seq, str(index))
seq_para_unique = seq_para_unique.append(freqs, ignore_index = True)
index += 1
all_seq = ''
for x in range (len(seq_para_unique['SEQ'])):
all_seq += seq_para_unique['SEQ'][x].upper()
freq_stats_unique = GetFreqPerSeq(all_seq, states)
else:
name_dic_unique = name_dic
freq_stats_unique = freq_stats
seq_para['EXCLUDED'] = 0
freq_stats.pop('FRAC_WILDCARDS_GAPS', None)
freq_stats_unique.pop('FRAC_WILDCARDS_GAPS', None)
def CheckIfInConfidenceInterval(char):
if char == '+':
return 1
elif char == '-':
return 0
def TransDateTime(timeStamp: str) -> datetime:
'''Transforms the timestamp (input as string) into datetime format.'''
dateTime = datetime.strptime(timeStamp, '%c')
return dateTime
def ParseAliSeqParameters():
'''
Function that gathers all information to be stored in the alignment, sequence and model parameters files from the log, iqtree and model.gz files.
The results are written into tab seperated files (ali_parameters.tsv, model_parameters.tsv as well as seq_parameters.tsv).
'''
global ali_para
global seq_para
global number_columns
# Helper dictionary that will store "constants" such as the alignment ID (ALI_ID) or timestamp.
constant_stats = {}
# Parse IQ-Tree2 version from first line in iqtree file.
IQtreeVersion = iqtree[0]
constant_stats.setdefault('IQTREE_VERSION', IQtreeVersion.split(' ')[1])
# Read through iqtree file. Check each line and parse out relevent information.
for i in range (len(iqtree)):
if iqtree[i][:len('Input file name: ')] == 'Input file name: ':
constant_stats.setdefault('ALI_ID', iqtree[i][len('Input file name: '):-1].split('/')[-1])
if iqtree[i][:len('Random seed number: ')] == 'Random seed number: ':
constant_stats.setdefault('RANDOM_SEED', str(iqtree[i][len('Random seed number: '):-1]))
if iqtree[i][:len('Date and time: ')] == 'Date and time: ':
constant_stats.setdefault('TIME_STAMP', TransDateTime(iqtree[i][len('Date and time: '):-1]))
# Update ali_para DataFrame
ali_para = ali_para.append({'SEQ_TYPE': type, 'ALI_ID': constant_stats['ALI_ID'], 'RANDOM_SEED': constant_stats['RANDOM_SEED'], 'TIME_STAMP': constant_stats['TIME_STAMP'], \
'IQTREE_VERSION': constant_stats['IQTREE_VERSION']}, ignore_index = True)
# Read through log file. Check each line and parse out relevent information.
identical = 0
excluded = 0
for i in range (len(log)):
if log[i][:len('Alignment has')]=='Alignment has':
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", log[i])
ali_para['SEQUENCES'] = int(numbers[0])
ali_para['COLUMNS'] = int(numbers[1])
number_columns = int(numbers[1])
ali_para['DISTINCT_PATTERNS'] = int(numbers[2])
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", log[i+1])
ali_para['PARSIMONY_INFORMATIVE_SITES'] = int(numbers[0])
ali_para['SINGELTON_SITES'] = int(numbers[1])
ali_para['CONSTANT_SITES'] = int(numbers[2])
if log[i][:len('**** TOTAL')] == '**** TOTAL':
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", log[i])
ali_para['FAILED_CHI2'] = int(numbers[1])
ali_para['FRAC_WILDCARDS_GAPS'] = float(float(numbers[0])/100)
j = i+1
seq_para.IDENTICAL_TO = seq_para.IDENTICAL_TO.astype(str)
while log[j][:len('Checking for duplicate sequences: done in ')] != 'Checking for duplicate sequences: done in ':
if 'but kept for subsequent analysis' in log[j]:
seq1 = log[j].split('NOTE: ')[1].split(' ')[0]
seq2 = log[j].split('NOTE: ')[1].split(' is identical to ')[1].split(' ')[0]
indices1 = seq_para[seq_para['SEQ_NAME'] == seq1].index.values[0]
indices2 = seq_para[seq_para['SEQ_NAME'] == seq2].index.values[0]
ident_seq1 = seq_para.at[indices1, 'IDENTICAL_TO']
ident_seq2 = seq_para.at[indices2, 'IDENTICAL_TO']
if ident_seq2=='nan':
seq_para.at[indices2, 'IDENTICAL_TO'] = seq1
else:
seq_para.at[indices2, 'IDENTICAL_TO'] = seq1+','+ident_seq2
if ident_seq1=='nan':
seq_para.at[indices1, 'IDENTICAL_TO'] = seq2
else:
seq_para.at[indices1, 'IDENTICAL_TO'] = seq2+','+ident_seq1
identical+=1
j+=1
if log[i][:len('Identifying sites to remove: ')]=='Identifying sites to remove: ':
j=i+1
while log[j][:len('Alignment was printed to ')]!='Alignment was printed to ':
if 'is ignored but added at the' in log[j]:
seq1 = log[j].split('NOTE: ')[1].split(' ')[0]
seq2 = log[j].split('(identical to ')[1].split(')')[0]
indices1 = seq_para[seq_para['SEQ_NAME'] == seq1].index.values[0]
indices2 = seq_para[seq_para['SEQ_NAME'] == seq2].index.values[0]
ident_seq1 = seq_para.at[indices1, 'IDENTICAL_TO']
ident_seq2 = seq_para.at[indices2, 'IDENTICAL_TO']
seq_para.at[indices1, 'EXCLUDED'] = 1
if ident_seq2=='nan':
seq_para.at[indices2, 'IDENTICAL_TO'] = seq1
else:
seq_para.at[indices2, 'IDENTICAL_TO'] = seq1+','+ident_seq2
if ident_seq1=='nan':
seq_para.at[indices1, 'IDENTICAL_TO'] = seq2
else:
seq_para.at[indices1, 'IDENTICAL_TO'] = seq2+','+ident_seq1
identical+=1
excluded+=1
j+=1
# Update seq_para DataFrame with stats regarding each sequence.
if log[i][:len('Analyzing sequences: done in ')] == 'Analyzing sequences: done in ':
j = i+1
while log[j][:len('**** TOTAL')] != '**** TOTAL':
seq_line = log[j].split(' ')
while '' in seq_line:
seq_line.remove('')
indices = seq_para[seq_para['SEQ_NAME'] == seq_line[1]].index.values
if len(indices) == 1:
seq_para.at[indices[0], 'CHI2_P_VALUE'] = float(seq_line[4][:-2])
if seq_line[3] == 'passed':
seq_para.at[indices[0], 'CHI2_PASSED'] = 1
else:
seq_para.at[indices[0], 'CHI2_PASSED'] = 0
j+=1
ali_para['IDENTICAL_SEQ'] = identical
ali_para['EXCLUDED_SEQ'] = excluded
seq_para['ALI_ID'] = constant_stats['ALI_ID']
# Finally, write the results into the corresponding files.
ali_para.to_csv(file_name_dic['ali_para'], mode = 'a', sep ='\t', index = False, header = False)
print('...alignment parameters were written into file '+file_name_dic['ali_para'])
seq_para.to_csv(file_name_dic['seq_para'], mode = 'a', sep ='\t', index = False, header = False)
print('...sequences were written into file '+file_name_dic['seq_para'])
seq_para2 = pd.read_csv(file_name_dic['seq_para'], sep = '\t')
seq_para2['IDENTICAL_TO'].fillna('', inplace = True)
seq_para2.to_csv(file_name_dic['seq_para'], sep ='\t', index = False)
def CalculateNewSelectionCriteria(no_col, logL, k):
CAIC = -2*logL + (math.log(no_col)+1)*k
ABIC = -2*logL + (math.log((no_col+2)/24))*k
return CAIC, ABIC
def ParseModelParameters(iqtree, check, model_para, mode = 'out'):
global freq_stats_unique
global freq_stats
global type
global aa_models
global number_columns
global het_num_para
global dna_num_para
constant_stats = {}
# Parse IQ-Tree2 version from first line in iqtree file.
IQtreeVersion = iqtree[0]
constant_stats.setdefault('IQTREE_VERSION', IQtreeVersion.split(' ')[1])
# Read through iqtree file. Check each line and parse out relevent information.
for i in range (len(iqtree)):
if iqtree[i][:len('Input file name: ')] == 'Input file name: ':
constant_stats.setdefault('ALI_ID', iqtree[i][len('Input file name: '):-1].split('/')[-1])
if iqtree[i][:len('Random seed number: ')] == 'Random seed number: ':
constant_stats.setdefault('RANDOM_SEED', str(iqtree[i][len('Random seed number: '):-1]))
if iqtree[i][:len('Date and time: ')] == 'Date and time: ':
constant_stats.setdefault('TIME_STAMP', TransDateTime(iqtree[i][len('Date and time: '):-1]))
if iqtree[i][:len('List of models sorted by ')] == 'List of models sorted by ':
j=i+3
while iqtree[j][:len('AIC, ')] != 'AIC, ':
if iqtree[j][:len('WARNING: ')] != 'WARNING: ':
info_for_model = iqtree[j].strip('\n')
info_for_model = info_for_model.split(' ')
while '' in info_for_model:
info_for_model.remove('')
if len(info_for_model) == 0:
break
model = info_for_model[0]
rate_het = 'uniform'
if type == 'DNA':
freq = 'equal'
else:
freq = 'model'
if '+F+' in model:
rate_het = model[len(model.split('+')[0])+2:]
freq = 'empirical'
elif '+F' in model:
rate_het = 'uniform'
freq = 'empirical'
elif '+' in model:
rate_het = model[len(model.split('+')[0]):]
if '+Fo' in model or '+FO' in model:
freq = 'optimized'
base_model=model.split('+')[0]
if '+F' in model:
base_model=base_model+'+F'
model_num_para=0
if rate_het in het_num_para.keys():
model_num_para+=het_num_para[rate_het]
if type=='DNA':
if base_model in dna_num_para.keys():
model_num_para+=dna_num_para[base_model]
else:
model_num_para = None
else:
if '+F' in model:
model_num_para+=19
else:
model_num_para = None
number_rate = 0
if rate_het != 'uniform':
if '+G' in rate_het:
number_rate = 4
elif '+R' in rate_het:
number_rate = int(rate_het.split('+R')[-1])
temp_dic = {'MODEL': model, 'FREQ_TYPE': freq, 'BASE_MODEL': base_model, 'MODEL_RATE_HETEROGENEITY': rate_het, \
'LOGL': float(info_for_model[1]), 'AIC': float(info_for_model[2]), 'CONFIDENCE_AIC': CheckIfInConfidenceInterval(info_for_model[3]), 'WEIGHTED_AIC': float(info_for_model[4]), \
'AICC': float(info_for_model[5]), 'CONFIDENCE_AICC': CheckIfInConfidenceInterval(info_for_model[6]), 'WEIGHTED_AICC': float(info_for_model[7]), \
'BIC': float(info_for_model[8]), 'CONFIDENCE_BIC': CheckIfInConfidenceInterval(info_for_model[9]), 'WEIGHTED_BIC': float(info_for_model[10]), 'NUM_RATE_CAT': number_rate}
if model_num_para is not None:
temp_dic['NUM_MODEL_PARAMETERS'] = model_num_para
if freq == 'equal':
temp_dic.update({'FREQ_A': 0.25, 'FREQ_C': 0.25, 'FREQ_G': 0.25, 'FREQ_T': 0.25})
elif freq == 'model':
temp_dic.update(aa_models[model.split('+')[0]])
else:
if mode == 'out':
temp_dic.update(freq_stats_unique)
elif mode == 'keep':
temp_dic.update(freq_stats)
model_para = model_para.append(temp_dic, ignore_index=True)
j+=1
# Read through model.gz (checkpoint) file. Check each line and parse out relevent information to be stored in model_para DataFrame.
for i in range (len(check)):
pot_model = check[i].split(':')
pot_model = pot_model[0]
indices = model_para[model_para['MODEL'] == pot_model].index.values
freq = 'not_opt'
if '+Fo' in pot_model or '+FO' in pot_model:
freq = 'optimized'
if len(indices) == 1:
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", check[i][len(pot_model):])
free_parameters = numbers[1]
model_para.at[indices[0], 'NUM_FREE_PARAMETERS'] = free_parameters
if pot_model == 'JC' or pot_model == 'WAG':
branch_number = int(numbers[1])
model_para.at[indices[0], 'TREE_LENGTH'] = numbers[2]
model_para.at[indices[0], 'CAIC'], model_para.at[indices[0], 'ABIC'] = CalculateNewSelectionCriteria(float(number_columns), float(model_para.at[indices[0], 'LOGL']), float(free_parameters))
if 'Rate parameters:' in check[i]:
for rate in ['A-C: ', 'A-G: ', 'A-T: ', 'C-G: ', 'C-T: ', 'G-T: ']:
model_para.at[indices[0], 'RATE_'+rate[0]+rate[2]] = float(check[i].split(rate)[1].split(' ')[0].strip('\n'))
if 'Base frequencies:' in check[i] and freq == 'optimized':
for base in ['A: ', 'C: ', 'G: ', 'T: ']:
model_para.at[indices[0], 'FREQ_'+base[0]] = float(check[i].split('Base frequencies: ')[1].split(base)[1].split(' ').strip('\n'))
if 'Proportion of invariable sites: ' in check[i]:
model_para.at[indices[0], 'PROP_INVAR'] = float(check[i].split('Proportion of invariable sites: ')[1].split(' ')[0].strip('\n'))
if 'Site proportion and rates: ' in check[i]:
rate_cat = check[i].split('Site proportion and rates: ')[1].split(')')
if 'Gamma shape alpha:' in check[i]:
model_para.at[indices[0], 'ALPHA'] = float(check[i].split('Gamma shape alpha: ')[1].split(' ')[0].strip('\n'))
for k in range (len(rate_cat)):
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", rate_cat[k])
if len(numbers) > 1:
model_para.at[indices[0], 'PROP_CAT_'+str(k+1)] = numbers[1]
model_para.at[indices[0], 'REL_RATE_CAT_'+str(k+1)] = numbers[0]
else:
for k in range (len(rate_cat)):
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", rate_cat[k])
if len(numbers) > 1:
model_para.at[indices[0], 'PROP_CAT_'+str(k+1)] = numbers[0]
model_para.at[indices[0], 'REL_RATE_CAT_'+str(k+1)] = numbers[1]
if len(indices) > 1:
print('WARNING! Ambigious index for model '+str(pot_model))
#Calculate the wheighted CAIC and ABIC
min_CAIC = min(model_para['CAIC'])
min_ABIC = min(model_para['ABIC'])
for i in range (len(model_para['ALI_ID'])):
model_para.at[i, 'WEIGHTED_CAIC'] = np.exp(-0.5*(model_para['CAIC'][i]-min_CAIC))
model_para.at[i, 'WEIGHTED_ABIC'] = np.exp(-0.5*(model_para['ABIC'][i]-min_ABIC))
sum_w_CAIC = sum(model_para['WEIGHTED_CAIC'])
sum_w_ABIC = sum(model_para['WEIGHTED_ABIC'])
for i in range (len(model_para['ALI_ID'])):
model_para.at[i, 'WEIGHTED_CAIC'] = model_para['WEIGHTED_CAIC'][i]/sum_w_CAIC
if model_para['WEIGHTED_CAIC'][i] > 0.05:
model_para.at[i, 'CONFIDENCE_CAIC'] = 1
else:
model_para.at[i, 'CONFIDENCE_CAIC'] = 0
model_para.at[i, 'WEIGHTED_ABIC'] = model_para['WEIGHTED_ABIC'][i]/sum_w_ABIC
if model_para['WEIGHTED_ABIC'][i] > 0.05:
model_para.at[i, 'CONFIDENCE_ABIC'] = 1
else:
model_para.at[i, 'CONFIDENCE_ABIC'] = 0
# Update DataFrames with "constants" (such as ALI_ID).
model_para['ALI_ID'] = constant_stats['ALI_ID']
model_para['RANDOM_SEED'] = constant_stats['RANDOM_SEED']
model_para['TIME_STAMP'] = constant_stats['TIME_STAMP']
model_para['IQTREE_VERSION'] = constant_stats['IQTREE_VERSION']
if mode == 'out':
model_para['KEEP_IDENT'] = 0
elif mode == 'keep':
model_para['KEEP_IDENT'] = 1
model_para['NUM_BRANCHES'] = branch_number
model_para.to_csv(file_name_dic['model_para'], mode = 'a', sep = '\t', index = False, header = False)
print('...model parameters were written into file '+file_name_dic['model_para'])
return constant_stats, branch_number
def ParseTreeParameters(tree_type, file, mldist, branch_number, mode = 'out'):
'''
Function that gathers information to be stored in the tree parameters file from the iqtree file.
Input
--------
mode : str
Declares if the iqtree file to be parsed containes information regarding the ml or initial tree.
Can be "in" or "ml".
file : list
List of lines from the read in iqtree file.
Returns
--------
dict
Dictionary containing information to be stored in the tree parameters file.
bool
Returns True if tree is rooted and False if tree is unrooted.
'''
global type
global number_columns
root = False
tree_stats = {}
IQtreeVersion = file[0]
tree_stats['IQTREE_VERSION'] = IQtreeVersion.split(' ')[1]
tree_stats['TREE_TYPE'] = tree_type
tree_stats['NUM_BRANCHES'] = branch_number
for i in range (len(file)):
if file[i][:len('Input file name: ')] == 'Input file name: ':
tree_stats['ALI_ID'] = file[i][len('Input file name: '):-1].split('/')[-1]
if file[i][:len('Random seed number: ')] == 'Random seed number: ':
tree_stats['RANDOM_SEED'] = str(file[i][len('Random seed number: '):-1])
if file[i][:len('Date and time: ')] == 'Date and time: ':
tree_stats['TIME_STAMP'] = TransDateTime(file[i][len('Date and time: '):-1])
if file[i][:len('Best-fit model according to ')] == 'Best-fit model according to ':
tree_stats['CHOICE_CRITERIUM'] = file[i].split('according to ')[1].split(':')[0]
if file[i][:len('Model of substitution: ')] == 'Model of substitution: ':
best_model = file[i][len('Model of substitution: '):-1]
rate_het = 'uniform'
if '+F+' in best_model:
rate_het = best_model[len(best_model.split('+')[0])+2:]
elif '+F' in best_model:
rate_het = 'uniform'
elif '+' in best_model:
rate_het = best_model[len(best_model.split('+')[0]):]
if type == 'DNA':
freq = 'equal'
elif type == 'AA':
freq = 'model'
if '+FO' in best_model or '+Fo' in best_model:
freq = 'optimized'
elif '+F' in best_model:
freq = 'empirical'
if mode == 'keep':
tree_stats.update(freq_stats)
elif mode == 'out':
tree_stats.update(freq_stats_unique)
if freq == 'equal':
tree_stats.update({'FREQ_A':0.25, 'FREQ_C':0.25, 'FREQ_G':0.25, 'FREQ_T':0.25})
elif freq == 'model':
tree_stats.update(aa_models[best_model.split('+')[0]])
tree_stats['FREQ_TYPE'] = freq
if mode == 'out':
tree_stats['KEEP_IDENT'] = 0
elif mode == 'keep':
tree_stats['KEEP_IDENT'] = 1
number_rate = 0
if rate_het != 'uniform':
if '+G' in rate_het:
number_rate = 4
elif '+R' in rate_het:
number_rate = int(rate_het.split('+R')[-1])
tree_stats['MODEL'] = best_model
tree_stats['MODEL_RATE_HETEROGENEITY'] = rate_het
tree_stats['NUM_RATE_CAT'] = number_rate
base_model=best_model.split('+')[0]
if '+F' in best_model:
base_model=base_model+'+F'
model_num_para=0
if rate_het in het_num_para.keys():
model_num_para+=het_num_para[rate_het]
if type=='DNA':
if base_model in dna_num_para.keys():
model_num_para+=dna_num_para[base_model]
else:
model_num_para = None
else:
if '+F' in base_model:
model_num_para+=19
else:
model_num_para = None
tree_stats['BASE_MODEL'] = base_model
if model_num_para is not None:
tree_stats['NUM_MODEL_PARAMETERS'] = model_num_para
if file[i][:len('Rate parameter R:')] == 'Rate parameter R:':
j = i+2
while '-' in file[j]:
rate = [file[j].split('-')[0][-1], file[j].split('-')[1][0]]
tree_stats['RATE_'+rate[0]+rate[1]] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[j])[0])
j += 1
if file[i][:len('State frequencies: ')] == 'State frequencies: ' and freq == 'optimized':
j = i+2
while 'pi(' in file[j]:
tree_stats['FREQ_'+file[j].split('pi(')[1].split(')')[0]] = float(file[j].split(' = ')[1].strip('\n'))
if file[i][:len('Gamma shape alpha: ')] == 'Gamma shape alpha: ':
tree_stats['ALPHA'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i])[0])
if file[i][:len(' Category Relat')] == ' Category Relat':
for j in range (1, 11, 1):
if file[i+j][:len(' ')] == ' ':
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+j])
if int(numbers[0]) == 0:
tree_stats['PROP_INVAR'] = float(numbers[2])
else:
tree_stats['REL_RATE_CAT_'+str(numbers[0])+''] = float(numbers[1])
tree_stats['PROP_CAT_'+str(numbers[0])+''] = float(numbers[2])
else:
break
if file[i][:len('Total tree length (sum of branch lengths): ')] == 'Total tree length (sum of branch lengths): ':
tree_stats['TREE_LENGTH'] = float(file[i][len('Total tree length (sum of branch lengths): '):-1])
if file[i][:len('Log-likelihood of ')]=='Log-likelihood of ':
tree_stats['LOGL'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i])[0])
tree_stats['UNCONSTRAINED_LOGL'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+1])[0])
tree_stats['NUM_FREE_PARAMETERS'] = int(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+2])[0])
tree_stats['AIC'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+3])[0])
tree_stats['AICC'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+4])[0])
tree_stats['BIC'] = float(re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", file[i+5])[0])
tree_stats['CAIC'], tree_stats['ABIC'] = CalculateNewSelectionCriteria(float(number_columns), float(tree_stats['LOGL']), float(tree_stats['NUM_FREE_PARAMETERS']))
if file[i][:len('Tree in newick format:')] == 'Tree in newick format:':
newick_string = file[i+2].strip('\n')
if newick_string[-2:] == ');':
root = False
else:
root = True
tree_stats['NEWICK_STRING'] = newick_string
if file[i][:len('Sum of internal branch lengths: ')] == 'Sum of internal branch lengths: ':
tree_stats['SUM_IBL'] = float(file[i].split(': ')[1].split(' ')[0].strip('\n'))
if type == 'initial':
return tree_stats, root
else:
tree_stats['DIST_MAX'] = 0
tree_stats['DIST_MIN'] = float('+inf')
all_distances=[]
for i in range (1, len(mldist.columns)):
for j in range (i, len(mldist)):
all_distances.append(mldist[i][j])
if mldist[i][j] > tree_stats['DIST_MAX']:
tree_stats['DIST_MAX'] = mldist[i][j]
if mldist[i][j] < tree_stats['DIST_MIN']:
tree_stats['DIST_MIN'] = mldist[i][j]
tree_stats['DIST_MEAN'] = np.mean(all_distances)
tree_stats['DIST_MEDIAN'] = np.median(all_distances)
tree_stats['DIST_VAR'] = np.var(all_distances)
return tree_stats, root
def CalculateSelectionCriterium(no_col, logL, k):
'''
Function that calculates the selection criteria (BIC, AIC, AICC) for given input.
Input: Number of columns (no_col), log Likelihood (logL), number of parameters of model (no_model_para)
as well as the global variabel branch_number depicting the number of branches in the tree.
Returns: AIC, AICC, BIC
'''
AIC = -2*logL + 2*k
if (no_col - k - 1) == 0:
AICc = AIC + 2*k*(k + 1)
else:
AICc = AIC + 2*k*((k + 1) / (no_col - k - 1))
BIC = -2*logL + k*math.log(no_col)
return AIC, AICc, BIC
def ParseInitialTree(log, constant_stats, branch_number, name_dic_unique, mode = 'out'):
'''
Function that parses through log file to gather all relevent information regarding the initial tree.
Requires: variables log file, name_dic_unique, branch_number, constant_stats
Returns:
---------
tree_stats : dict
A dictionary containing information to be stored in the tree parameters file.
root : bool
Is True is tree is rooted and False if tree is unrooted.
Treefile_name : str
The name of the treefile the Newick sting of the initial tree was written into.
'''
global het_num_para
global dna_num_para
tree_stats = {}
no_col = 0
root = False
tree_stats['IQTREE_VERSION'] = constant_stats['IQTREE_VERSION']
tree_stats['TREE_TYPE'] = 'initial'
tree_stats['ALI_ID'] = constant_stats['ALI_ID']
tree_stats['RANDOM_SEED'] = constant_stats['RANDOM_SEED']
tree_stats['TIME_STAMP'] = constant_stats['TIME_STAMP']
for i in range (len(log)):
if log[i][:len('Alignment has')] == 'Alignment has':
numbers = re.findall(r"[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?", log[i])
no_col = int(numbers[1])
if log[i][:len('Perform fast likelihood tree search using ')] == 'Perform fast likelihood tree search using ':