[Done] Observed number of occurrences of codon (Obs) analysis
[Done] Stat frequency of cusp software. cusp website: https://www.bioinformatics.nl/cgi-bin/emboss/cusp
[Done] Relative synonymous codon usage (RSCU) analysis
[Done] Draw codon barplot
[Done] Parity rule 2 (PR2) analysis
[Done] Draw Neutrality curve
[Done] Draw ENC plot
[Done] Cups sortware anslysis
[Done part] codonW sortware anslysis
[Plan] CAI analysis
[Plan] Codon Bias Index (CBI)
[Plan] Frequency of OPtimal codons
[Plan] Corresponding analysis (COA)
scipy >= v1.11.4
numpy >= v1.26.3
seaborn >= v0.13.1
matplotlib >= v3.8.2
python >= v3.11.5
Min.mt.fasta data from https://plantscience.cn/cn/article/doi/10.11913/PSJ.2095-0837.2022.20229
import sys
sys.path.append('/mnt/nfs1/jupyter/pyCUBs/pyCUBs/') # import library path
import pycubcore
# Switch the test work path and import test data
import os
os.chdir('/mnt/nfs1/jupyter/pyCUBs/')
inputfile = "./test_data/Min.mt.fasta"
# Module Introduction
"""
├── codontables.py # Codon table module
├── fastaio.py # Fasta IO module
└── pycubcore.py # CUB Core computing module
"""
import codontables
print("\nTable of available genetic codons:\n",codontables.CodonTables())
import fastaio
help(fastaio.FastaIO)
# Some information about the library
print(pycubcore.__author__)
print("pycubcore function: ", pycubcore.__all__)
print("version: ",pycubcore.__version__)Table of available genetic codons:
Reference website: https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes
Translate Tables/Genetic Codes:
1: Standard
2: Vertebrate Mitochondrial
3: YeastMitochondrial
4: Mold Mitochondrial, Protozoan Mitochondrial, Coelenterate Mitochondrial, Mycoplasma, Spiroplasma
5: Invertebrate Mitochondrial
6: Ciliate Nuclear, Dasycladacean Nuclear, Hexamita Nuclear
9: Echinoderm Mitochondrial, Flatworm Mitochondrial
10: Euplotid Nuclear
11: Bacterial, Archaeal, Plant Plastid
12: Alternative Yeast Nuclear
13: Ascidian Mitochondrial
14: Alternative Flatworm Mitochondrial
16: Chlorophycean Mitochondrial
21: Trematode Mitochondrial
22: Scenedesmus obliquus Mitochondrial
23: Thraustochytrium Mitochondrial
24: Rhabdopleuridae Mitochondrial
25: Candidate Division SR1, Gracilibacteria
26: Pachysolen tannophilus Nuclear
27: Karyorelict Nuclear
28: Condylostoma Nuclear
29: Mesodinium Nuclear
30: Peritrich Nuclear
31: Blastocrithidia Nuclear
33: Cephalodiscidae Mitochondrial UAA-Tyr
Help on function FastaIO in module fastaio:
FastaIO(file: str)
Read a fasta file path and support compressed files ending in ".gz",
or accept a handle of "_io.TextIOWrapper" class.
Author: Guisen Chen; Email: [email protected]; Date: 2024/05/17
pycubcore function: ['GetObs', 'GetFranction', 'GetFrequency', 'GetRSCU', 'DrawCodonBarplot', 'GetCusp', 'GetcodonW', 'NPA', 'DrawNPA', 'GetNC', 'GetGC3s', 'ENC', 'DrawENC', 'Find4Dtv', 'GetPR2', 'PR2', 'DrawPR2']
version: v0.01
## example1: Effective number of codons (ENC) analysis
#help(pycubcore.ENC)
ENCResult = pycubcore.ENC(inputfile, genetic_codes=16) #Genetic_Codes=16, Selected genetic codon table 16 Plant chloroplasts
#help(pycubcore.DrawENC)
pycubcore.DrawENC(ENCResult, show_gene_name=False)
# example2: Parity rule 2 (PR2) analysis.
# help(pycubcore.PR2)
PR2Result = pycubcore.PR2(inputfile, genetic_codes=16)
# help(pycubcore.DrawPR2)
pycubcore.DrawPR2(PR2Result, show_gene_name=True) #Show gene name
# example3: Neutral plot analysis.
NPAResult = pycubcore.NPA(inputfile, genetic_codes=16)
pycubcore.DrawNPA(NPAResult, show_gene_name=True)
# example4: compute Obs, Franction, Frequency, RSCU
GeneName, Seqence = next(fastaio.FastaIO(inputfile))
print("input seqence:\n")
print(">"+GeneName+"\n", Seqence, sep="")
# Calculate
Obs = pycubcore.GetObs(seqences=Seqence, genetic_codes=1) #The seqences parameter can be a string of gene sequence or a list. For example, a list of all CDS sequences of a chloroplast, so as to calculate the Obs in the whole organism
Franction=pycubcore.GetFranction(Obs)
Frequency=pycubcore.GetFrequency(Obs)
RSCU=pycubcore.GetRSCU(Obs)
# Visualization
print("\nVisualization result：\n")
pycubcore.DrawCodonBarplot(Obs, ylabel="Number")
pycubcore.DrawCodonBarplot(Franction, ylabel="Franction")
pycubcore.DrawCodonBarplot(Frequency, ylabel="Frequency")
pycubcore.DrawCodonBarplot(RSCU, ylabel="RSCU")input seqence:
>psbA
ATGACTGCAATTTTAGAGAGACGCGAAAGCGAAAGCCTATGGGGTCGCTTCTGTAACTGGATAACCAGCACTGAGAACCGTCTTTACATTGGATGGTTTGGTGTTTTGATGATCCCTACCTTATTGACCGCAACTTCTGTATTTATTATCGCCTTCATTGCTGCTCCTCCAGTAGATATTGATGGTATTCGTGAACCTGTTTCTGGGTCTCTACTTTACGGAAACAATATTATCTCTGGTGCCATTATTCCTACTTCTGCAGCTATAGGATTGCACTTTTACCCGATATGGGAAGCGGCATCCGTTGATGAATGGTTATACAATGGTGGTCCTTATGAATTGATTGTTCTACACTTCTTACTTGGTGTAGCTTCTTACATGGGTCGTGAGTGGGAACTAAGTTTCCGTCTGGGTATGCGCCCTTGGATTGCTGTTGCATATTCAGCTCCTGTTGCAGCTGCAACTGCTGTTTTCTTGATCTACCCAATCGGTCAAGGAAGCTTCTCTGATGGTATGCCCCTAGGAATCTCTGGTACTTTCAACTTCATGATTGTATTCCAGGCTGAGCACAACATTCTTATGCACCCATTTCACATGTTAGGTGTGGCTGGTGTATTCGGCGGCTCCCTATTCAGTGCTATGCATGGTTCCTTGGTAACTTCAAGTTTGATCAGGGAAACCACTGAAAATGAATCTGCTAATGAAGGTTACAGATTCGGTCAAGAGGAAGAAACTTATAATATCGTAGCTGCTCATGGTTATTTTGGCCGATTGATCTTCCAATATGCTAGTTTCAACAATTCTCGTTCTTTACATTTCTTCCTAGCTGCTTGGCCTGTAGTAGGTATCTGGTTCACTGCTTTAGGTATTAGTACCATGGCTTTCAACCTAAATGGTTTCAATTTCAACCAATCCGTAGTTGACAGTCAAGGTCGTGTAATTAACACTTGGGCTGATATCATCAACCGTGCTAACCTTGGTATGGAAGTTATGCATGAACGTAATGCTCACAACTTCCCTCTAGACCTAGCTGCTGTTGAAGCTCCATCCATAAATGGATAA
Visualization result：
# example5: Get similar results to Cusp software
# Cusp website: https://www.bioinformatics.nl/cgi-bin/emboss/cusp
GeneName, Seqence = next(fastaio.FastaIO(inputfile))
print("Input seqence: \n")
print(">"+GeneName+"\n", Seqence, sep="")
print("\n")
Obs = pycubcore.GetObs(seqences=Seqence, genetic_codes=1)
print("Cusp result: \n")
print(pycubcore.GetCusp(Obs, human_format=True)) #human_format=True, Human-readable output, otherwise machine-readable.Input seqence:
>psbA
ATGACTGCAATTTTAGAGAGACGCGAAAGCGAAAGCCTATGGGGTCGCTTCTGTAACTGGATAACCAGCACTGAGAACCGTCTTTACATTGGATGGTTTGGTGTTTTGATGATCCCTACCTTATTGACCGCAACTTCTGTATTTATTATCGCCTTCATTGCTGCTCCTCCAGTAGATATTGATGGTATTCGTGAACCTGTTTCTGGGTCTCTACTTTACGGAAACAATATTATCTCTGGTGCCATTATTCCTACTTCTGCAGCTATAGGATTGCACTTTTACCCGATATGGGAAGCGGCATCCGTTGATGAATGGTTATACAATGGTGGTCCTTATGAATTGATTGTTCTACACTTCTTACTTGGTGTAGCTTCTTACATGGGTCGTGAGTGGGAACTAAGTTTCCGTCTGGGTATGCGCCCTTGGATTGCTGTTGCATATTCAGCTCCTGTTGCAGCTGCAACTGCTGTTTTCTTGATCTACCCAATCGGTCAAGGAAGCTTCTCTGATGGTATGCCCCTAGGAATCTCTGGTACTTTCAACTTCATGATTGTATTCCAGGCTGAGCACAACATTCTTATGCACCCATTTCACATGTTAGGTGTGGCTGGTGTATTCGGCGGCTCCCTATTCAGTGCTATGCATGGTTCCTTGGTAACTTCAAGTTTGATCAGGGAAACCACTGAAAATGAATCTGCTAATGAAGGTTACAGATTCGGTCAAGAGGAAGAAACTTATAATATCGTAGCTGCTCATGGTTATTTTGGCCGATTGATCTTCCAATATGCTAGTTTCAACAATTCTCGTTCTTTACATTTCTTCCTAGCTGCTTGGCCTGTAGTAGGTATCTGGTTCACTGCTTTAGGTATTAGTACCATGGCTTTCAACCTAAATGGTTTCAATTTCAACCAATCCGTAGTTGACAGTCAAGGTCGTGTAATTAACACTTGGGCTGATATCATCAACCGTGCTAACCTTGGTATGGAAGTTATGCATGAACGTAATGCTCACAACTTCCCTCTAGACCTAGCTGCTGTTGAAGCTCCATCCATAAATGGATAA
Cusp result:
#Coding GC 42.84%
#1st letter GC 50.0%
#2nd letter GC 43.22%
#3rd letter GC 35.31%
#Codon AA Fraction Frequency Number
TTT Phe 0.192 14.124 5
TTC Phe 0.808 59.322 21
TCT Ser 0.393 31.073 11
TCC Ser 0.179 14.124 5
TCA Ser 0.071 5.65 2
TCG Ser 0.0 0.0 0
AGT Ser 0.214 16.949 6
AGC Ser 0.143 11.299 4
TAT Tyr 0.417 14.124 5
TAC Tyr 0.583 19.774 7
TGT Cys 1.0 2.825 1
TGC Cys 0.0 0.0 0
TTA Leu 0.226 19.774 7
TTG Leu 0.258 22.599 8
CTT Leu 0.161 14.124 5
CTC Leu 0.0 0.0 0
CTA Leu 0.323 28.249 10
CTG Leu 0.032 2.825 1
TAA * 1.0 2.825 1
TGA * 0.0 0.0 0
TAG * 0.0 0.0 0
TGG Trp 1.0 28.249 10
CCT Pro 0.6 25.424 9
CCC Pro 0.067 2.825 1
CCA Pro 0.267 11.299 4
CCG Pro 0.067 2.825 1
CAT His 0.4 11.299 4
CAC His 0.6 16.949 6
CGT Arg 0.533 22.599 8
CGC Arg 0.2 8.475 3
CGA Arg 0.067 2.825 1
CGG Arg 0.0 0.0 0
AGA Arg 0.133 5.65 2
AGG Arg 0.067 2.825 1
CAA Gln 0.833 14.124 5
CAG Gln 0.167 2.825 1
ATT Ile 0.484 42.373 15
ATC Ile 0.387 33.898 12
ATA Ile 0.129 11.299 4
ACT Thr 0.688 31.073 11
ACC Thr 0.312 14.124 5
ACA Thr 0.0 0.0 0
ACG Thr 0.0 0.0 0
AAT Asn 0.455 28.249 10
AAC Asn 0.545 33.898 12
ATG Met 1.0 33.898 12
GTT Val 0.455 28.249 10
GTC Val 0.0 0.0 0
GTA Val 0.5 31.073 11
GTG Val 0.045 2.825 1
GCT Ala 0.714 70.621 25
GCC Ala 0.057 5.65 2
GCA Ala 0.2 19.774 7
GCG Ala 0.029 2.825 1
GAT Asp 0.714 14.124 5
GAC Asp 0.286 5.65 2
GGT Gly 0.697 64.972 23
GGC Gly 0.091 8.475 3
GGA Gly 0.182 16.949 6
GGG Gly 0.03 2.825 1
GAA Glu 0.762 45.198 16
GAG Glu 0.238 14.124 5
# example6: Get similar results to codonW software.
GeneName, Seqence = next(fastaio.FastaIO(inputfile))
print("Input seqence: \n")
print(">"+GeneName+"\n", Seqence, sep="")
print("\n")
Obs = pycubcore.GetObs(seqences=Seqence, genetic_codes=16)
print("codonW result: \n")
print(pycubcore.GetcodonW(Obs, human_format=True)) #human_format=True, Human-readable output, otherwise machine-readable.Input seqence:
>psbA
ATGACTGCAATTTTAGAGAGACGCGAAAGCGAAAGCCTATGGGGTCGCTTCTGTAACTGGATAACCAGCACTGAGAACCGTCTTTACATTGGATGGTTTGGTGTTTTGATGATCCCTACCTTATTGACCGCAACTTCTGTATTTATTATCGCCTTCATTGCTGCTCCTCCAGTAGATATTGATGGTATTCGTGAACCTGTTTCTGGGTCTCTACTTTACGGAAACAATATTATCTCTGGTGCCATTATTCCTACTTCTGCAGCTATAGGATTGCACTTTTACCCGATATGGGAAGCGGCATCCGTTGATGAATGGTTATACAATGGTGGTCCTTATGAATTGATTGTTCTACACTTCTTACTTGGTGTAGCTTCTTACATGGGTCGTGAGTGGGAACTAAGTTTCCGTCTGGGTATGCGCCCTTGGATTGCTGTTGCATATTCAGCTCCTGTTGCAGCTGCAACTGCTGTTTTCTTGATCTACCCAATCGGTCAAGGAAGCTTCTCTGATGGTATGCCCCTAGGAATCTCTGGTACTTTCAACTTCATGATTGTATTCCAGGCTGAGCACAACATTCTTATGCACCCATTTCACATGTTAGGTGTGGCTGGTGTATTCGGCGGCTCCCTATTCAGTGCTATGCATGGTTCCTTGGTAACTTCAAGTTTGATCAGGGAAACCACTGAAAATGAATCTGCTAATGAAGGTTACAGATTCGGTCAAGAGGAAGAAACTTATAATATCGTAGCTGCTCATGGTTATTTTGGCCGATTGATCTTCCAATATGCTAGTTTCAACAATTCTCGTTCTTTACATTTCTTCCTAGCTGCTTGGCCTGTAGTAGGTATCTGGTTCACTGCTTTAGGTATTAGTACCATGGCTTTCAACCTAAATGGTTTCAATTTCAACCAATCCGTAGTTGACAGTCAAGGTCGTGTAATTAACACTTGGGCTGATATCATCAACCGTGCTAACCTTGGTATGGAAGTTATGCATGAACGTAATGCTCACAACTTCCCTCTAGACCTAGCTGCTGTTGAAGCTCCATCCATAAATGGATAA
codonW result:
T3s C3s A3s G3s Nc GC3s GC L_sym L_aa
0.5033 0.2730 0.2964 0.0901 43.01 0.311 0.430 331 353