cmdbtools: A command line tools for CMDB varaints browser

Introduction

China is the most populous country and the second largest economy in the world. However, the construction of Chinese genome database is in slow progress. At present, among the world's large-scale international and national genome sequencing projects, such as 1KGP, Genomics England, Genome of the Netherlands, ExAC are mostly biased towards the construction of a genomic baseline for European populations. In those projects, while the sample size goes up to hundreds of thousands for samples with european ancestry in those database, the sequen-cing Chinese samples is no more than a thousand.

Since a high-quality genomic baseline database serves as an important control for medical research and population-oriented clinical and drug applications, the Chinese millionome database (CMDB) is developed to fill the gap.

The Chinese Millionome Database(CMDB) is a unique large-scale Chinese genomics database produced by BGI and hosted in the National GeneBank. The CMDB delivers peridical and useful variation information and scientific insights derived from the analysis of millions of Chinese sequencing data. The results aim to promote genetic research and precision medicine actions in China.

The delivering information includes any of detected variants and the corresponding allele frequency, annotation, frequency comparison to the global populations from existing databases, etc.

Benchmarking detail and methods are described in our Cell paper:

Liu, S. et al.(2018) Genomic Analyses from Non-invasive Prenatal Testing Reveal Genetic Associations, Patterns of Viral Infections, and Chinese Population History. Cell, 2, 347-359. DOI:https://doi.org/10.1016/j.cell.2018.08.016

cmdbtools is a command line tool for this CMDB variants browser.

Quick start

CMDB variant browser allows authorized access its data through an Genomics API and cmdbtools is a convenient command line tools for this purpose.

Installation

Install the released version by pip (Only support Python3 since v1.1.0):

pip install cmdbtools

Setup

Please enable your API access from Profile in CMDB browser before using cmdbtools.

Login

Login with cmdbtools by using CMDB API access key, which could be found from Profile->Genomics API if you have apply for it.

cmdbtools login -k your-genomics-api-key

If everything goes smoothly, means you can use CMDB as one of your varaints database in command line mode.

Logout

Logout cmdbtools by simply run the command below:

cmdbtool logout

Query a single variant

Variants could be retrieved from CMDB by using query-varaint.

Run cmdbtools query-variant -h to see all available options. There're two different ways to retrive variants.

One is to use -c and -p parameters for single variant, the other way uses -l for multiple positions.

Here are examples for quering single varaint by chromosome name and position.

cmdbtools query-variant -c chr17 -p 41234470

and you will get something looks like below:

##fileformat=VCFv4.2
##FILTER=<ID=LowQual,Description="Low quality">
##INFO=<ID=CMDB_AN,Number=1,Type=Integer,Description="Number of Alleles in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AC,Number=A,Type=Integer,Description="Alternate Allele Counts in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AF,Number=A,Type=Float,Description="Alternate Allele Frequencies from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_FILTER,Number=A,Type=Float,Description="Filter from CMDB_hg19_v1.0">
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
17  41234470    rs1060915&CD086610&COSM4416375  A   G   74.38   PASS    CMDB_AF=0.361763,CMDB_AC=4625,CMDB_AN=12757

Quering multiple varants.

A list of variants could be retrieved from CMDB by using the parameters of -l when apply by query-varaint.

cmdbtools query-variant -l positions.list > result.vcf

Format for positions.list, could be a mixture of chrom position and chrom start end, even with or without chr in the chromosome ID column:

#CHROM  POS
chr22   17662378
chr22   17662408
22  17662442
22  17662444
22  17662699
22  17662729
22  17690496
22  17662353    17663671
22  17669209    17669357

result.vcf is VCF format and looks like below:

##fileformat=VCFv4.2
##FILTER=<ID=LowQual,Description="Low quality">
##INFO=<ID=CMDB_AN,Number=1,Type=Integer,Description="Number of Alleles in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AC,Number=A,Type=Integer,Description="Alternate Allele Counts in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AF,Number=A,Type=Float,Description="Alternate Allele Frequencies from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_FILTER,Number=A,Type=Float,Description="Filter from CMDB_hg19_v1.0">
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
chr22   17662699    rs58754958  A   G   59.86   PASS    CMDB_AF=0.031047,CMDB_AC=441,CMDB_AN=13553
chr22   17662793    rs7289170   A   G   64.23   PASS    CMDB_AF=0.050419,CMDB_AC=842,CMDB_AN=16135
chr22   17669245    rs116020027 G   T   30.3    PASS    CMDB_AF=0.003453,CMDB_AC=43,CMDB_AN=11280
chr22   17690409    rs362129    G   A   32.3    PASS    CMDB_AF=0.065438,CMDB_AC=686,CMDB_AN=10236

You can even use -c -p and -l simultaneously if you like.

cmdbtools query-variant -c 22 -p 46616520 -l positions.list > result.vcf

Annotate your VCF files

Annotate your VCF file with CMDB by using cmdbtools annotate command.

Download a list of example variants in VCF format from multiple_samples.vcf.gz. To annotate this list of variants with allele frequences from CMDB, you can just run the following command in Linux or Mac OS.

cmdbtools annotate -i multiple_samples.vcf.gz > multiple_samples_CMDB.vcf

It'll take about 2 ~ 3 minutes to complete 3,000+ variants' annotation. Then you will get 4 new fields with the information of CMDB in VCF INFO:

• CMDB_AF: Allele frequece in CMDB;
• CMDB_AN: Coverage in CMDB in population level;
• CMDB_AC: Allele count in population level in CMDB;
• CMDB_FILTER: Filter status in CMDB.

##fileformat=VCFv4.2
##ALT=<ID=NON_REF,Description="Represents any possible alternative allele at this location">
##FILTER=<ID=LowQual,Description="Low quality">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Allele count in genotypes, for each ALT allele, in the same order as listed">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency, for each ALT allele, in the same order as listed">
##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes">
##INFO=<ID=BaseQRankSum,Number=1,Type=Float,Description="Z-score from Wilcoxon rank sum test of Alt Vs. Ref base qualities">
##reference=file:///home/tools/hg19_reference/ucsc.hg19.fasta
##INFO=<ID=CMDB_AN,Number=1,Type=Integer,Description="Number of Alleles in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AC,Number=A,Type=Integer,Description="Alternate Allele Counts in Samples with Coverage from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_AF,Number=A,Type=Float,Description="Alternate Allele Frequencies from CMDB_hg19_v1.0">
##INFO=<ID=CMDB_FILTER,Number=A,Type=Float,Description="Filter from CMDB_hg19_v1.0">
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO
chr21   9413612 .       C       T       6906.62 .       AC=25;AF=0.313;AN=80;BaseQRankSum=0.425;CMDB_AC=2459;CMDB_AF=0.207525;CMDB_AN=11834;CMDB_FILTER=PASS
chr21   9413629 .       C       T       8028.88 .       AC=30;AF=0.375;AN=80;BaseQRankSum=-1.200e+00;CMDB_AC=6906;CMDB_AF=0.305445;CMDB_AN=22406;CMDB_FILTER=PASS
chr21   9413700 .       G       A       7723.82 .       AC=30;AF=0.375;AN=80;BaseQRankSum=-9.000e-02
chr21   9413735 .       C       A       10121.72        .       AC=35;AF=0.438;AN=80;BaseQRankSum=0.977;CMDB_AC=2385;CMDB_AF=0.283965;CMDB_AN=8382;CMDB_FILTER=PASS
chr21   9413839 .       C       T       8192.08 .       AC=28;AF=0.350;AN=80;BaseQRankSum=-5.200e-02
chr21   9413840 .       C       A       11514.35        .       AC=38;AF=0.475;AN=80;BaseQRankSum=0.253
chr21   9413870 .       T       C       7390.60 .       AC=26;AF=0.325;AN=80;BaseQRankSum=-4.270e-01
chr21   9413880 .       T       A       146.96  .       AC=1;AF=0.013;AN=80;BaseQRankSum=2.12;ClippingRankSum=0.00
chr21   9413909 .       G       A       1131.78 .       AC=10;AF=0.125;AN=80;BaseQRankSum=0.549;CMDB_AC=209;CMDB_AF=0.01507;CMDB_AN=13683;CMDB_FILTER=PASS
chr21   9413913 .       C       T       8120.65 .       AC=28;AF=0.350;AN=80;BaseQRankSum=-4.390e-01;CMDB_AC=2870;CMDB_AF=0.205597;CMDB_AN=13955;CMDB_FILTER=PASS
chr21   9413945 .       T       C       43787.68        .       AC=71;AF=0.888;AN=80;BaseQRankSum=0.089
chr21   9413995 .       C       T       9632.44 .       AC=29;AF=0.363;AN=80;BaseQRankSum=0.747
chr21   9413996 .       A       G       41996.48        .       AC=71;AF=0.888;AN=80;BaseQRankSum=-1.242e+00;CMDB_AC=3308;CMDB_AF=0.688533;CMDB_AN=4790;CMDB_FILTER=PASS
chr21   9414003 .       T       C       4256.54 .       AC=19;AF=0.238;AN=80;BaseQRankSum=-6.030e-01

Citation

If you use CMDB in your scientific publication, we would appreciate citation this paper:

Siyang Liu, Shujia Huang. et al.(2018) Genomic Analyses from Non-invasive Prenatal Testing Reveal Genetic Associations, Patterns of Viral Infections, and Chinese Population History. Cell, 2, 347-359. DOI:https://doi.org/10.1016/j.cell.2018.08.016