Package: cmdbtools
cmdbtools: A command line tools for CMDB varaints browserIntroductionChina 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.TheChinese 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 ourCellpaper: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.2018.08.016cmdbtoolsis a command line tool for this CMDB variants browser.Quick startCMDB variant browser allows authorized access its data through an
Genomics API andcmdbtoolsis a convenient command line tools for
this purpose.InstallationInstall the released version bypip(Only support Python3 since v1.1.0):pipinstallcmdbtoolsSetupPlease enable your API access from Profile inCMDB
browserbefore usingcmdbtools.LoginLogin withcmdbtoolsby using CMDB API access key, which could be
found from Profile->Genomics API if you have apply for it.cmdbtoolslogin-kyour-genomics-api-keyIf everything goes smoothly,means you can use CMDB as one of your
varaints database in command line mode.LogoutLogoutcmdbtoolsby simply run the command below:cmdbtoollogoutQuery a single variantVariants could be retrieved from CMDB by usingquery-varaint.Runcmdbtools query-variant -hto see all available options. There're
two different ways to retrive variants.One is to use-cand-pparameters for single variant, the other way
uses-lfor multiple positions.Here are examples for quering single varaint by chromosome name and
position.cmdbtoolsquery-variant-cchr17-p41234470and 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  INFO1741234470rs1060915&CD086610&COSM4416375AG74.38PASSCMDB_AF=0.361763,CMDB_AC=4625,CMDB_AN=12757Quering multiple varants.A list of variants could be retrieved from CMDB by using the parameters
of-lwhen apply byquery-varaint.cmdbtoolsquery-variant-lpositions.list>result.vcfFormat forpositions.list, could be a mixture ofchrom   positionandchrom    start   end, even with or withoutchrin 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    17669357result.vcfis 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=10236You can even use-c-pand-lsimultaneously if you like.cmdbtoolsquery-variant-c22-p46616520-lpositions.list>result.vcfAnnotate your VCF filesAnnotate your VCF file with CMDB by usingcmdbtools annotatecommand.Download a list of example variants in VCF format frommultiple_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.cmdbtoolsannotate-imultiple_samples.vcf.gz>multiple_samples_CMDB.vcfIt'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-01CitationIf 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