Metadata-Version: 2.1
Name: cmdbtools
Version: 1.1.2
Summary: cmdbtools: A command line tools for CMDB variant browser.
Home-page: https://github.com/ShujiaHuang/cmdbtools
Author: Shujia Huang
Author-email: huangshujia9@gmail.com
Maintainer: Shujia Huang
Maintainer-email: huangshujia9@gmail.com
License: BSD (3-clause)
Download-URL: https://github.com/ShujiaHuang/cmdbtools
Description: 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)](https://db.cngb.org/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](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):
        
        ```bash
        pip install cmdbtools
        ```
        
        Setup
        -----
        
        Please enable your API access from Profile in [CMDB
        browser](https://db.cngb.org/cmdb) 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.
        
        [![cmdb_genomics_api](assets/figures/cmdb_genomics_api.png)](assets/figures/cmdb_genomics_api.png)
        
        ```bash
        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:
        
        ```bash
        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.
        
        ```bash
        cmdbtools query-variant -c chr17 -p 41234470
        ```
        
        and you will get something looks like below:
        
        ```bash
        ##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`.
        
        ```bash
        cmdbtools query-variant -l positions.list > result.vcf
        ```
        
        Format for [positions.list](tests/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
        ```
        
        Actrually you can use `-c` `-p` and `-l` simultaneously if you like. And
        `positions.list` could just contain one single position.
        
        ```bash
        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](tests/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.
        
        ```bash
        cmdbtools annotate -i multiple_samples.vcf.gz > multiple_samples_CMDB.vcf
        ```
        
        It\'ll take about 2 or 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](https://doi.org/10.1016/j.cell.2018.08.016)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Description-Content-Type: text/markdown
