Input function for technical / biological replicates in snakemake - snakemake

Question

I´m currently trying to write a Snakemake workflow that can check automatically via a sample.tsv file if a given sample is a biological or technical replicate. And then use in this case at some point of my workflow a rule to merge technical/biological replicates.
My tsv file looks like this:
|sample | unit_bio | unit_tech | fq1 | fq2 |
|----------|----------|-----------|-----|-----|
| bCalAnn1 | 1 | 1 | /home/assembly_downstream/data/arima_HiC/bCalAnn1_1_1_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn1_1_1_R2.fastq.gz |
| bCalAnn1 | 1 | 2 | /home/assembly_downstream/data/arima_HiC/bCalAnn1_1_2_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn1_1_2_R2.fastq.gz |
| bCalAnn2 | 1 | 1 | /home/assembly_downstream/data/arima_HiC/bCalAnn2_1_1_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn2_1_1_R2.fastq.gz |
| bCalAnn2 | 1 | 2 | /home/assembly_downstream/data/arima_HiC/bCalAnn2_1_2_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn2_1_2_R2.fastq.gz |
| bCalAnn2 | 2 | 1 | /home/assembly_downstream/data/arima_HiC/bCalAnn2_2_1_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn2_2_1_R2.fastq.gz |
| bCalAnn2 | 3 | 1 | /home/assembly_downstream/data/arima_HiC/bCalAnn2_3_1_R1.fastq.gz | /home/assembly_downstream/data/arima_HiC/bCalAnn2_3_1_R2.fastq.gz |
My Pipeline looks like this:
import pandas as pd
import os
import yaml
configfile: "config.yaml"
samples = pd.read_table(config["samples"], dtype=str)
rule all:
input:
expand(config["arima_mapping"] + "final/{sample}_{unit_bio}_{unit_tech}.bam", zip,
sample=samples["sample"], unit_bio=samples["unit_bio"], unit_tech=samples["unit_tech"])
..
some rules
..
rule add_read_groups:
input:
config["arima_mapping"] + "paired/{sample}_{unit_bio}_{unit_tech}.bam"
output:
config["arima_mapping"] + "paired_read_groups/{sample}_{unit_bio}_{unit_tech}.bam"
params:
platform = "ILLUMINA",
sampleName = "{sample}",
library = "{sample}",
platform_unit ="None"
conda:
"../envs/arima_mapping.yaml"
log:
config["logs"] + "arima_mapping/paired_read_groups/{sample}_{unit_bio}_{unit_tech}.log"
shell:
"picard AddOrReplaceReadGroups I={input} O={output} SM={params.sampleName} LB={params.library} PU={params.platform_unit} PL={params.platform} 2> {log}"
rule merge_tech_repl:
input:
config["arima_mapping"] + "paired_read_groups/{sample}_{unit_bio}_{unit_tech}.bam"
output:
config["arima_mapping"] + "merge_tech_repl/{sample}_{unit_bio}_{unit_tech}.bam"
params:
val_string = "SILENT"
conda:
"../envs/arima_mapping.yaml"
log:
config["logs"] + "arima_mapping/merged_tech_repl/{sample}_{unit_bio}_{unit_tech}.log"
threads:
2 #verwendet nur maximal 2
shell:
"picard MergeSamFiles -I {input} -O {output} --ASSUME_SORTED true --USE_THREADING true --VALIDATION_STRINGENCY {params.val_string} 2> {log}"
rule mark_duplicates:
input:
config["arima_mapping"] + "merge_tech_repl/{sample}_{unit_bio}_{unit_tech}.bam" if config["tech_repl"] else config["arima_mapping"] + "paired_read_groups/{sample}_{unit_bio}_{unit_tech}.bam"
output:
bam = config["arima_mapping"] + "final/{sample}_{unit_bio}_{unit_tech}.bam",
metric = config["arima_mapping"] + "final/metric_{sample}_{unit_bio}_{unit_tech}.txt"
#params:
conda:
"../envs/arima_mapping.yaml"
log:
config["logs"] + "arima_mapping/mark_duplicates/{sample}_{unit_bio}_{unit_tech}.log"
shell:
"picard MarkDuplicates I={input} O={output.bam} M={output.metric} 2> {log}"
At the moment I have set a boolean in a config file that tells the mark_duplicates rule whether to take its input from the add_read_group or the merge_technical_replicates rule. This is of course not optimal since it could be that some samples may have duplicates (of any numbers) while others don´t. Therefore I want to create a syntax that checks the tsv table if a given sample name and unit_bio number are identical while the unit_tech number is different (and later analog to this for biological replicates), thus merging these specific samples while nonduplicate samples skip the merging rule.
EDIT
For clarification since I think I explained my goal confusingly.
My first attempt looks like this, I want "i" to be flexible, in case the duplicate number changes. I don't think that my input function returns all duplicates together that match each other but gives them one by one which is not what I want. I´m also unsure on how I would have to handle samples that do not have duplicates since they would have to skip this rule somehow.
input_function(wildcards):
return expand({sample}_{unit_bio}_{i}.bam", sample = wildcards.sample,
unit_bio = wildcards.unit_bio,
i = samples["sample"].str.count(wildcards.sample))
rule tech_duplicate_check:
input:
input_function #(that returns a list of 2-n duplicates, where n could be different for each sample)
output:
{sample}_{unit_bio}.bam
shell:
MergeTechDupl_tool {input} # input is a list

Answer 1

Therefore I want to create a syntax that checks the tsv table if a given sample name and unit_bio number are identical while the unit_tech number is different (and later analog to this for biological replicates), thus merging these specific samples while nonduplicate samples skip the merging rule.
rule gather_techdups_of_a_biodup:
output: "{sample}/{unit_bio}"
input: gather_techdups_of_a_biodup_input_fn
shell: "true" # Fill this in
rule gather_biodips_of_a_techdup:
output: "{sample}/{unit_tech}"
input: gather_biodips_of_a_techdup_input_fn
shell: "true" # Fill this in
After some attempts my main problem I struggle with is the table checking. As far as I know snakemake takes templates as input and checks for all samples that match this. But I would need to check the table for every sample that shares (e.g. for technical replicate) the sample name and the unit_bio number take all these samples and give them as input for the first rule run. Then I would have to take the next sample which was not already part of a previous run to prevent merging the same samples multiple times.
The logic you describe here can be implemented in the gather_techdups_of_a_biodup_input_fn and gather_biodips_of_a_techdup_input_fn functions above. For example, read your sample TSV file with pandas, filter for wildcards.sample and wildcards.unit_bio (or wildcards.unit_tech), then extract columns fq1 and fq2 from the filtered data frame.