https://www.illumina.com/platinumgenomes.html

https://console.cloud.google.com/storage/browser/genomics-public-data/platinum-genomes/vcf?pageState=("StorageObjectListTable":("f":"%255B%255D"))&prefix=&forceOnObjectsSortingFiltering=false

https://gatk.broadinstitute.org/hc/en-us/articles/360035531452-After-gCNV-calling-considerations
https://gatk.broadinstitute.org/hc/en-us/articles/360035531152

https://www.biorxiv.org/content/10.1101/2021.04.30.442110v1.full
A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data

cnvpytor 验证记录
小服务器 /ceph_disk3/var_demo_data

sudo apt-get install python-tk
pip3 install  cnvpytor -i https://pypi.tuna.tsinghua.edu.cn/simple
用conda的话  需要把源码中的这些文件复制下
cp CNVpytor/cnvpytor/data/*.pytor /opt/miniconda3/lib/python3.7/site-packages/cnvpytor/data/
samtools index NA12877_S1.bam
cnvpytor -root 12877.pytor -rd NA12877_S1.bam
cnvpytor -root 12877.pytor -his 1000 10000 100000
cnvpytor -root 12877.pytor -partition 1000 10000 100000
cnvpytor -root 12877.pytor -call 1000 10000 100000

如下步骤为可选步骤 在使用snp时运行（CWL、WDL加开关控制）
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -snp NA12877_S1.genome.vcf -sample NA12877
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -pileup NA12877_S1.bam
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -mask_snps
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -baf 1000 10000 100000
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -call baf 1000 10000 100000
注意下面这个命令需要一个文件来描述范围
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -genotype 1000 10000 100000 <regions
/opt/miniconda3/bin/cnvpytor -root 12877.pytor -call combined 1000 10000 100000
后续还有几个命令来输出图片  等报告一起做

错误：

Traceback (most recent call last):
  File "/home/anneng/.local/bin/cnvpytor", line 11, in <module>
    sys.exit(main())
  File "/home/anneng/.local/lib/python2.7/site-packages/cnvpytor/__main__.py", line 437, in main
    use_gc_corr=not args.no_gc_corr, use_mask=args.use_mask_with_rd)
  File "/home/anneng/.local/lib/python2.7/site-packages/cnvpytor/root.py", line 1502, in call
    distN = np.zeros_like(NN, dtype="long") - 1
  File "/home/anneng/.local/lib/python2.7/site-packages/numpy/core/numeric.py", line 168, in zeros_like
    res = empty_like(a, dtype=dtype, order=order, subok=subok)
TypeError: data type "long" not understood
需要使用python3 运行

freebayes验证 对于多个样本 每个样本都要加上RG头

bwa mem ecoli.fasta SRR10000374_1.fastq.gz SRR10000374_2.fastq.gz -R '@RG\tID:SRR10000374\tSM:SRR10000374' | samtools sort -o SRR10000374.bam -
bwa mem ecoli.fasta SRR10000377_1.fastq.gz SRR10000377_2.fastq.gz -R '@RG\tID:SRR10000377\tSM:SRR10000377' | samtools sort -o SRR10000377.bam -
../freebayes-1.3.6-linux-amd64-static -L list -f ecoli.fasta -v demo.vcf

https://bioinformaticsworkbook.org/dataAnalysis/VariantCalling/freebayes-dnaseq-workflow.html#gsc.tab=0

GATK and Picard variant manipulation tools are currently able to recognize the following types of alleles:

SNP (single nucleotide polymorphism)
INDEL (insertion/deletion)
MIXED (combination of SNPs and indels at a single position)
MNP (multi-nucleotide polymorphism, e.g. a dinucleotide substitution)
SYMBOLIC (such as the <NON-REF> allele used in GVCFs produced by HaplotypeCaller, the * allele used to signify the presence of a spanning deletion, or undefined events like a very large allele or one that's fuzzy and not fully modeled; i.e. there's some event going on here but we don't know what exactly)

To investigate whether using alternative tools results in correct annotation of MNVs, we re-processed the VCF file of simulated MNVs using GATK 3.6.0 ReadBackedPhasing 10 (default parameters plus “-maxDistMNP 2 -enableMergeToMNP”) or MAC 1.2 9 then annotated the resulting VCF files using Alamut batch version 1.5.2 (Interactive Biosoftware, Rouen, France). We also tested re-calling the variants using VarDict 1.4 7 and Platypus 0.8.1 12.

GATK新版本已经没有了 ReadBackedPhasing 工具
However, they do not emit MNPs. If you would like to combine contiguous SNPs into MNPs, you will need to use the legacy ReadBackedPhasing tool in GATK3 with the MNP merging function activated. See the GATK3 tool documentation for details.
https://gatk.broadinstitute.org/hc/en-us/articles/360035530752-What-types-of-variants-can-GATK-tools-detect-or-handle-

Identification of MNVs requires the constituent variants to be properly phased—that is, to be identified accurately as either both occurring on the same haplotype (in cis) or on two different haplotypes (in trans). Phasing can be performed following three broad strategies: read-based phasing18, which assesses whether nearby variants co-segregate on the same reads in DNA sequencing data; family-based phasing19, which assesses whether pairs of variants are co-inherited within families; and population-based phasing20, which leverages haplotype sharing between members of a large genotyped population to make a statistical inference of phase. Read-based phasing is particularly effective for pairs of nearby variants, making it suitable for the analysis of MNVs.