比對(duì)分析 bwa bwa mem -t 6 -k 32 -M -R "@RG\tID:saample\tLB:sample\tSM:sample" fa fq_R1.fq.gz fq_R2.fq.gz |samtools view -b -S ->sample.bam # bwa index進(jìn)行基因組建index 排序,因?yàn)楹罄m(xù)處理都需要按照基因組順序進(jìn)行排序 samtools sort bam > out.bam 進(jìn)行捕獲數(shù)據(jù)的提取和統(tǒng)計(jì) samtools view -@ 6 -L bed -b -o bam input.bam samtools flagstat bam > flagstat 去除重復(fù)區(qū)域 java -jar picard-1.119/MarkDuplicates.jar I=bam O=rmdup.bam M=.metrics \ VALIDATION_STRINGENCY=SILENT CREATE_INDEX=true REMOVE_DUPLICATES=true TMP_DIR=tmp MAX_FILE_HANDLES=1000 統(tǒng)計(jì)深度信息 samtools depth -q 20 -Q 20 -l 60 -d 5000 bam > depth 建立索引 samtools index bam # 當(dāng)基因組單條染色體長(zhǎng)度大于512M左右的時(shí)候,建立索引有問題,后續(xù)gatk檢測(cè)變異會(huì)受到影響,建議進(jìn)行染色體 N 區(qū)合適區(qū)域進(jìn)行拆分 變異檢測(cè) gatk進(jìn)行單個(gè)樣品gvcf檢測(cè) gatk-4.1.2.0/gatk HaplotypeCaller -R ref --emit-ref-confidence GVCF -I rmdup.bam -O g.vcf # 使用前將基因組建好索引 gatk CreateSequenceDictionary -R fa 合并gvcf gatk-4.1.2.0/gatk CombineGVCFs -R ref -O merge.g.vcf -V gvcf ... call 基因型 gatk GenotypeGVCFs -R ref -V merge.g.vcf -O raw.vcf 拆分snp,indel gatk SplitVcfs --INPUT=raw.vcf --INDEL_OUTPUT=raw.indel.vcf --SNP_OUTPUT=raw.SNP.vcf --STRICT=false 進(jìn)行基礎(chǔ)質(zhì)量過(guò)濾 gatk-4.1.2.0/gatk VariantFiltration -V raw.SNP.vcf \ -filter "QD < 2.0" --filter-name "QD2" \ -filter "QUAL < 30.0" \ --filter-name "QUAL30" \ -filter "SOR > 3.0" \ --filter-name "SOR3" \ -filter "FS > 60.0" \ --filter-name "FS60" \ -filter "MQ < 40.0" \ --filter-name "MQ40" \ -filter "MQRankSum < -12.5" \ --filter-name "MQRankSum-12.5" \ -filter "ReadPosRankSum < -8.0" \ --filter-name "ReadPosRankSum-8" -O raw.SNP.filter.QD2.QUAL30.SOR3.FS60.MQ40.MQRankSum-12.5.ReadPosRankSum-8.vcf gatk-4.1.2.0/gatk VariantFiltration \ -V raw.indel.vcf \ -filter "QD < 2.0" \ --filter-name "QD2" \ -filter "QUAL < 30.0" \ --filter-name "QUAL30" \ -filter "FS > 200.0" \ --filter-name "FS200" \ -filter "ReadPosRankSum < -20.0" \ --filter-name "ReadPosRankSum-20" \ -O raw.indel.QD2.QUAL30.FS200.ReadPosRankSum-20.vcf 進(jìn)行標(biāo)記群體質(zhì)量過(guò)濾 vcftools --minDP 4 --maxDP 100 --minGQ 10 --minQ 30 --min-meanDP 3 \ --out aw.SNP.filter.QD2.QUAL30.SOR3.FS60.MQ40.MQRankSum-12.5.ReadPosRankSum-8.minDP4.maxDP100.minGQ10.minQ30.min-meanDP3.miss0.2.maf0.01.vcf \ --vcf raw.SNP.filter.QD2.QUAL30.SOR3.FS60.MQ40.MQRankSum-12.5.ReadPosRankSum-8.vcf \ --recode --recode-INFO-all --max-missing 0.8 --maf 0.01 ## 建議有條件的測(cè)序 10X,個(gè)體深度>=7,miss 0.1;maf 0.05,不足的話,4X 群體結(jié)構(gòu)分析 數(shù)據(jù)格式轉(zhuǎn)化 vcftools --vcf vcf --plink --out plink --noweb --ped ped --map map --make-bed --out pro ## 注意plink識(shí)別的染色體編號(hào)是純數(shù)字,咱們這個(gè)是contig的,需要將染色體轉(zhuǎn)換下,后面的數(shù)據(jù)才能正常運(yùn)行 ## vcftools 在樣品數(shù)量高于 1008 左右的時(shí)候會(huì)有問題,可以1000 樣品一組生成 ped 和 map,然后直接 將 ped cat合并在一起即可,理論上,map 是一致的 PCA分析 gcta64 --bfile pro --make-grm --thread-num 4 --out pro gcta64 --grm pro --pca 10 --thread-num 4 --out pro # 根據(jù)結(jié)果R繪制散點(diǎn)圖 TREE 分析 # 將VCF|PED文件轉(zhuǎn)換為fa格式 treebest nj -b 1000 fa > nj.tree ggtree 或者其他在線軟件,離線軟件畫圖 STRUCTURE分析 admixture --cv 1 > 1.log ## 1-10按照上面循環(huán)下 # R畫圖 軟件文獻(xiàn) [1] Li, H. and R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009. 25(14): p. 1754-60. [2] Li, H., et al., The Sequence Alignment/Map format and SAMtools.Bioinformatics, 2009. 25(16): p. 2078-9. [3] Danecek, P., et al., The variant call format and VCFtools. Bioinformatics, 2011. 27(15): p. 2156-8. [4] Purcell, S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 559-75. [5] Yang, J., et al., GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics, 2011. 88(1): p. 76-82. [6] Alexander, D.H., J. Novembre, and K. Lange, Fast model-based estimation of ancestry in unrelated individuals. Genome Res, 2009. 19(9): p. 1655-64. [7] Vilella, A.J., et al., EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res, 2009. 19(2): p. 327-35. 作者:點(diǎn)滴生信 https://www.bilibili.com/read/cv6573709/ 出處:bilibili |
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