C_N/C_S-calculator

C_N/C_S-calculator is a site-specific selection pressure analysis method designed to capture selective pressures acting across different gene sites. The rationale behind C_N/C_S-calculator lies in the recognition that not all variants within a gene contribute equally to oncogenic processes, with some variants driving cancer progression while others are constrained by functional requirements.

C_N/C_S-calculator consists of three modules. With the input cancer somatic mutation information, the first module of C_N/C_S-calculator computes the selection pressure at the gene level, where C_N/C_S > 1 denotes gene under positive selection while 1-H < C_N/C_S < 1 denotes that only several sites are under weak positive selection. Then, for each positively selected gene, the mutational landscape of different amino acid sites is inscribed using a two-component mixture module, which models the proportion of driver component ($\eta$) and passenger component ($1 - \eta$) in a gene. Finally, for the site components computed by the second module, the third module calculates site component-specific selection pressure Ω_driver and Ω_passenger, respectively.

Install

tar -zxvf CNCScalculator-2.0.tar.gz
cd CNCScalculator
python setup.py install

Analysis Steps

1. Choose specific version of Ensembl reference data (eg. TCGA(75_GRCh37.p13))
2. Data preprocessing for input (eg. collect mutation data from TCGA(.maf))
3. Site-specific selection pressure prediction steps as follows:
   1. choose calculation model (collect mutation profiles from TCGA data for mutation-profile based model)
   2. choose specific version of sequence data (.fas)
   3. load genetic code file
   4. perform CNCSPipe
   5. output files in your path

Usage

Files required (information of protein coding genes, mutation rate profile file, protein coding sequences, genetic code) is included in the package. You can change the files if necessary.

Example

from CNCScalculator import CNCSPipe,cncs,H_test,two_component_cncs
## Help information
print(help(CNCSPipe))
print(help(cncs))
print(help(H_test))
print(help(two_component_cncs))
## CNCSPipe(mut_file:str,alpha:float,out_cncs:str,out_gene:str,out_site:str,out_two_component_cncs:str,out_H_test:str)
CNCSPipe("Mutation_file.maf",1.51,"cncs.txt","gene.txt","site.txt","two_compo_cncs.txt","H_test.txt")

## cncs(mut_file:str,outputfile:str,arg:list = [])
cncs("Mutation_file.maf","cncs.txt")

## H_test(mut_file:str,alpha:float,out_cncs:str,out_H_test:str)
H_test("Mutation_file.maf",1.51,"cncs.txt","H_test.txt")

## two_component_cncs(mut_file:str,alpha:float,out_cncs:str,out_two_component_cncs:str)
two_component_cncs("Mutation_file.maf",1.51,"cncs.txt","two_compo_cncs.txt")

Input File

Mini Sample:

Hugo_Symbol	Consequence	Variant_Type	HGVSp_Short	Gene	Feature	HGVSc
TACC2	missense_variant	SNP	p.T38M	ENSG00000138162	ENST00000369005	c.113C>T
JAKMIP3	synonymous_variant	SNP	p.D723D	ENSG00000188385	ENST00000298622	c.2169C>T
PANX3	missense_variant	SNP	p.R296Q	ENSG00000154143	ENST00000284288	c.887G>A
SPI1	missense_variant	SNP	p.P127T	ENSG00000066336	ENST00000227163	c.379C>A
NAALAD2	missense_variant	SNP	p.R65C	ENSG00000077616	ENST00000534061	c.193C>T
FAT3	synonymous_variant	SNP	p.P3444P	ENSG00000165323	ENST00000298047	c.10332G>A
MTERFD3	missense_variant	SNP	p.L213S	ENSG00000120832	ENST00000552029	c.638T>C
BTBD11	missense_variant	SNP	p.E770K	ENSG00000151136	ENST00000280758	c.2308G>A
NOS1	upstream_gene_variant	SNP	.	ENSG00000089250	ENST00000338101	.

Output File

Result of cncs.txt

• Title

Gene_id	nonsynonymous_site	synonymous_site	nonsynonymous_count	synonymous_count	nonsynonymous_site_with_mutation	CN	CS	CN/CS	p_value

• Explanation
  • CN/CS: the ratio of nonsynonymous mutation to synonymous mutation
  • p_value: the p value of chi-continency assert whether null hypothesis of $C_{N}/C_{S} = 1$ can be statistically rejected

Result of gene.txt

• Title

Gene_id	Transcript_id	Gene_name	Chr	Max_hit	Count	Protein_length	Distribution	Mut_sites	f0	mean	variance	m0	m1	eta	LRT_p-value	Q(z)

• Explanation
  • f0: nohit in aa length
  • mean: mean of $z$
  • variance: variance of $z$
  • m0: the recurrent rate of somatic mutations at a passenger site
  • m1: the recurrent rate of somatic mutations at a driver site
  • eta: $\eta$, the probability of any site being a driver in a gene
  • LRT_p-value: the p value of approximate likelihood ratio test for the null hypothesis that all sites are passengers ($\eta = 0$)
  • Q(z): $Q(z) = P(driver|z)$, the posterior probability of an amino acid site being a cancer-driver when the number ($z$) of somatic mutations is observed at this site

Result of site.txt

• Title

Gene_id	Transcript_id	Gene_name	Chr	Protein_position	z	Q(z)	Protein_mutation	M(z)	Omega(z)

• Explanation
  • z: the count of somatic missense mutations at a site
  • M(z): the posterior mean of recurrent mutations at the site where $z$ mutations are observed
  • Omega(z): the site-specific selection pressure at the site where $z$ mutations are observed

Result of H_test.txt

• Title

Gene_id	Transcript_id	Gene_name	Chr	CN/CS	1-H	H_test	Htest_p_value

• Explanation
  • 1-H: $H$ is a relative measure of evolutionary rate variation among sites
  • H_test: the comparison of $1-H$ and $C_{N}/C_{S}$
  • Htest_p_value: the p value of chi-continency assert whether null hypothesis of $C_{N}/C_{S} = 1-H$ can be statistically rejected

Result of two_compo_cncs.txt

• Title

Gene_id	Transcript_id	Gene_name	Chr	Omega	Omega_p	Omegadri	Omegapass	Omegapass_p

• Explanation
  • Omega: $C_{N}/C_{S}$ ratio of this gene
  • Omega_p: the p value of chi-continency assert whether null hypothesis of $C_{N}/C_{S} = 1$ can be statistically rejected
  • Omegadri: $\Omega_{dri}$, $C_{N}/C_{S}$ ratio of driver sites of the gene
  • Omegapass: $\Omega_{pass}$, $C_{N}/C_{S}$ ratio of passenger sites of the gene
  • Omegapass_p: the p value of chi-continency assert whether null hypothesis of $\Omega_{pass} = 1$ can be statistically rejected

Build-in Data File

CNCScalculator-2.0
│	README.md
│	MANIFEST.in
│  setup.py    
│	...
└───CNCScalculator
│   │   __init__.py
│   │   CNCScalculator.py
│   └───data
│       │   genetic_code.txt
│       │   human_TCGA_match_GRCh37.fas
│       │   pancancer_mutation_rate.txt
│       │   TCGA_match_GRCh37.cds
└───CNCScalculator.egg-info
    │	....

genetic_code.txt symonymous or non-synonymous in codon mutation

human_TCGA_match_GRCh37.fas fasta file of human genes (GRCh37)

pancancer_mutation_rate.txt Pancancer rate of Ref mutate to Alt

TCGA_match_GRCh37.cds cds information of human genes

Contact

Xun Gu [email protected]

Zhou Zhan [email protected]