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mapping - Snakemake：通过一个通配符组合输入

问题描述

我想知道您是否能够提供有关定义 Snakemake 规则以组合一个但不是所有通配符的建议？我的数据是有组织的，所以我有运行和样本；大多数（但不是所有）样品在每次运行中都重新测序。因此，我有每个样本运行的预处理步骤。然后，我有一个步骤为每个样本的每次运行组合 BAM 文件。但是，我遇到的问题是我有点困惑如何定义规则，以便我可以列出与样本相对应的所有个体 bam（来自不同的运行）的输入。

为了清楚起见，我将整个管道放在下面，但我真正的问题是关于规则 combine_bams。如何在输入中列出单个样本的所有 bam？

任何建议都会很棒！非常感谢您！

# Define samples and runs
RUNS, SAMPLES = glob_wildcards("/labs/jandr/walter/tb/data/Stanford/{run}/{samp}_L001_R1_001.fastq.gz")
print("runs are: ", RUNS)
print("samples are: ", SAMPLES)

rule all:
  input:
     #trim = ['process/trim/{run}_{samp}_trim_1.fq.gz'.format(samp=sample_id, run=run_id) for sample_id, run_id in zip(sample_ids, run_ids)],
     trim = expand(['process/trim/{run}_{samp}_trim_1.fq.gz'], zip, run = RUNS, samp = SAMPLES),
     kraken=expand('process/trim/{run}_{samp}_trim_kr_1.fq.gz', zip, run = RUNS, samp = SAMPLES),
     bams=expand('process/bams/{run}_{samp}_bwa_MTB_ancestor_reference_rg_sorted.bam', zip, run = RUNS, samp = SAMPLES), # add fixed ref/mapper (expand with zip doesn't allow these to repeate)
     combined_bams=expand('process/bams/{samp}_bwa_MTB_ancestor_reference.merged.rmdup.bam', samp = np.unique(SAMPLES))

# Trim reads for quality. 
rule trim_reads:  
  input: 
    p1='/labs/jandr/walter/tb/data/Stanford/{run}/{samp}_L001_R1_001.fastq.gz', # update inputs so they only include those that exist use zip.
    p2='/labs/jandr/walter/tb/data/Stanford/{run}/{samp}_L001_R2_001.fastq.gz'
  output: 
    trim1='process/trim/{run}_{samp}_trim_1.fq.gz',
    trim2='process/trim/{run}_{samp}_trim_2.fq.gz'
  log: 
    'process/trim/{run}_{samp}_trim_reads.log'
  shell:
    '/labs/jandr/walter/tb/scripts/trim_reads.sh {input.p1} {input.p2} {output.trim1} {output.trim2} &>> {log}'

# Filter reads taxonomically with Kraken.   
rule taxonomic_filter:
  input:
    trim1='process/trim/{run}_{samp}_trim_1.fq.gz',
    trim2='process/trim/{run}_{samp}_trim_2.fq.gz'
  output: 
    kr1='process/trim/{run}_{samp}_trim_kr_1.fq.gz',
    kr2='process/trim/{run}_{samp}_trim_kr_2.fq.gz',
    kraken_stats='process/trim/{run}_{samp}_kraken.report'
  log: 
    'process/trim/{run}_{samp}_run_kraken.log'
  threads: 8
  shell:
    '/labs/jandr/walter/tb/scripts/run_kraken.sh {input.trim1} {input.trim2} {output.kr1} {output.kr2} {output.kraken_stats} &>> {log}'

# Map reads.
rule map_reads:
  input:
    ref_path='/labs/jandr/walter/tb/data/refs/{ref}.fasta.gz',
    kr1='process/trim/{run}_{samp}_trim_kr_1.fq.gz',
    kr2='process/trim/{run}_{samp}_trim_kr_2.fq.gz'
  output:
    bam='process/bams/{run}_{samp}_{mapper}_{ref}_rg_sorted.bam'
  params:
    mapper='{mapper}'
  log:
    'process/bams/{run}_{samp}_{mapper}_{ref}_map.log'
  threads: 8
  shell:
    "/labs/jandr/walter/tb/scripts/map_reads.sh {input.ref_path} {params.mapper} {input.kr1} {input.kr2} {output.bam} &>> {log}"


# Combine reads and remove duplicates (per sample).
rule combine_bams:
  input:
    bams = 'process/bams/{run}_{samp}_bwa_MTB_ancestor_reference_rg_sorted.bam'
  output: 
    combined_bam = 'process/bams/{samp}_{mapper}_{ref}.merged.rmdup.bam'
  log: 
     'process/bams/{samp}_{mapper}_{ref}_merge_bams.log'
  threads: 8
  shell:
    "sambamba markdup -r -p -t {threads} {input.bams} {output.combined_bam}"

标签： mappingbioinformaticssnakemake

解决方案

创建一个字典以将每个样本与其运行列表相关联。

然后对于 combine_bams 规则，使用输入函数使用字典为该样本生成输入文件。

rule combine_bams:
  input:
    bams = lambda wildcards: expand('process/bams/{run}_{{samp}}_bwa_MTB_ancestor_reference_rg_sorted.bam', run=sample_dict[wildcards.sample])
  output: 
    combined_bam = 'process/bams/{samp}_{mapper}_{ref}.merged.rmdup.bam'
  log: 
     'process/bams/{samp}_{mapper}_{ref}_merge_bams.log'
  threads: 8
  shell:
    "sambamba markdup -r -p -t {threads} {input.bams} {output.combined_bam}"

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