写的数据预处理snakemake流程其实包含在每个独自的剖析中比方种系遗传变异和肿瘤变异流程中，这里独自拿进去做演示用，因为数据预处理是通用的，在call变异之前须要解决好数据。
数据预处理过程包含，从fastq文件去接头、比对到基因组、去除反复、碱基品质校对，最初失去解决好的BAM或CRAM文件。

fastq去接头
fastq产生的报告json能够用multiqc汇总成一份报告
if config["fastq"].get("pe"):

rule fastp_pe:    input:        sample=get_fastq    output:        trimmed=[temp("results/trimmed/{s}{u}.1.fastq.gz"), temp("results/trimmed/{s}{u}.2.fastq.gz")],        html=temp("report/{s}{u}.fastp.html"),        json=temp("report/{s}{u}.fastp.json"),    log:        "logs/trim/{s}{u}.log"    threads: 32    wrapper:        config["warpper_mirror"]+"bio/fastp"

else:

rule fastp_se:    input:        sample=get_fastq    output:        trimmed=temp("results/trimmed/{s}{u}.fastq.gz"),        html=temp("report/{s}{u}.fastp.html"),        json=temp("report/{s}{u}.fastp.json"),    log:        "logs/trim/{s}{u}.log"    threads: 32    wrapper:        config["warpper_mirror"]+"bio/fastp"

BWA-mem2 比对+去重+排序
mem2的速度更快，所以采纳。
sambaster的去除反复速度比MarkDuplicat快，所以采纳。
最初用picard依照coordinate比照对后果排序。
输入的格局是CRAM，不是BAM，因为CRAM压缩效率更高，所以采纳。
rule bwa_mem2:

input:    reads=get_trimmed_fastq,    reference=gatk_dict["ref"],    idx=multiext(gatk_dict["ref"], ".0123", ".amb", ".bwt.2bit.64", ".ann",".pac"),output:    temp("results/prepared/{s}{u}.aligned.cram") # Output can be .cram, .bam, or .samlog:    "logs/prepare/bwa_mem2/{s}{u}.log"params:    bwa="bwa-mem2", # Can be 'bwa-mem, bwa-mem2 or bwa-meme.    extra=get_read_group,    sort="picard",    sort_order="coordinate",    dedup=config['fastq'].get('duplicates',"remove"), # Can be 'mark' or 'remove'.    dedup_extra=get_dedup_extra(),    exceed_thread_limit=True,    embed_ref=True,threads: 32wrapper:    config["warpper_mirror"]+"bio/bwa-memx/mem"

碱基品质校对
GATK说碱基的品质分数对call变异很重要，所以须要校对。
BaseRecalibrator计算怎么校对，ApplyBQSR更具BaseRecalibrator后果去校对。
rule BaseRecalibrator:

input:    bam="results/prepared/{s}{u}.aligned.cram",    ref=gatk_dict["ref"],    dict=gatk_dict["dict"],    known=gatk_dict["dbsnp"],  # optional known sites - single or a listoutput:    recal_table=temp("results/prepared/{s}{u}.grp")log:    "logs/prepare/BaseRecalibrator/{s}{u}.log"resources:    mem_mb=1024params:    # extra=get_intervals(),  # optionalwrapper:    config["warpper_mirror"]+"bio/gatk/baserecalibrator"

rule ApplyBQSR:

input:    bam="results/prepared/{s}{u}.aligned.cram",    ref=gatk_dict["ref"],    dict=gatk_dict["dict"],    recal_table="results/prepared/{s}{u}.grp",output:    bam="results/prepared/{s}{u}.cram"log:    "logs/prepare/ApplyBQSR/{s}{u}.log"params:    embed_ref=True  # embed the reference in cram outputresources:    mem_mb=2048wrapper:    config["warpper_mirror"]+"bio/gatk/applybqsr"

索引
最初对CRAM构建所以，之后可能用失去。
rule samtools_index:

input:    "results/prepared/{s}{u}.cram"output:    "results/prepared/{s}{u}.cram.crai"log:    "logs/prepare/samtools_index/{s}{u}.log"threads: 32params:    extra="-c"wrapper:    config["warpper_mirror"]+"bio/samtools/index"

Reference
https://gatk.broadinstitute.org/hc/en-us/articles/36003553591...