snakemake

I want to loop snakemake above two different wildcards, which - I think - are somehow independent from each other. In case that there is already a solved threat for this case, I would be happy for a hint. But so far I'm not sure what the correct terms are to look for what I want to do. Let's assume my pipeline has three steps. I have a set of samples which I process in each of those three steps. Put in the second step I deploy an extra parameter to every sample. In the third step now I have to iterate through the samples and the associated parameter. Because of this structure, I think it's not

Is it possible to define default settings for memory and resources in cluster config file, and then override in rule specific manner, when needed? Is resources field in rules directly tied to cluster config file? Or is it just a fancy way for params field for readability purposes? In the example below, how do I use default cluster configs for rule a , but use custom changes ( memory=40000 and rusage=15000 ) in rule b ? cluster.json: { "__default__": { "memory": 20000, "resources": "\"rusage[mem=8000] span[hosts=1]\"", "output": "logs/cluster/{rule}.{wildcards}.out", "error": "logs/cluster/

Snakemake allows creation of a log for each rule with log parameter that specifies the name of the log file. It is relatively straightforward to pipe results from shell output to this log, but I am not able to figure out a way of logging output of run output (i.e. python script). One workaround is to save the python code in a script and then run it from the shell, but I wonder if there is another way? I have some rules that use both the log and run directives. In the run directive, I "manually" open and write the log file. For instance: rule compute_RPM: input: counts_table = source_small_RNA

I have cram(bam) files that I want to split by read group. This requires reading the header and extracting the read group ids. I have this function which does that in my Snakemake file: def identify_read_groups(cram_file): import subprocess command = 'samtools view -H ' + cram_file + ' | grep ^@RG | cut -f2 | cut -f2 -d":" ' read_groups = subprocess.check_output(command, shell=True) read_groups = read_groups.split('\n')[:-1] return(read_groups) I have this rule all: rule all: input: expand('cram/RG_bams/{sample}.RG{read_groups}.bam', read_groups=identify_read_groups('cram/{sample}.bam.cram'))

I would like to get snakemake running a Python script with a specific conda environment via a SGE cluster. On the cluster I have miniconda installed in my home directory. My home directory is mounted via NFS so accessible to all cluster nodes. Because miniconda is in my home directory, the conda command is not on the operating system path by default. I.e., to use conda I need to first explicitly add this to the path. I have a conda environment specification as a yaml file, which could be used with the --use-conda option. Will this work with the --cluster "qsub" option also? FWIW I also launch