Cleaning up

Last updated on 2024-10-07 | Edit this page

Overview

Questions

  • How do I save disk space by removing temporary files?
  • How do I isolate the interim files created by jobs from each other?

Objectives

  • Understand how Snakemake manages temporary outputs
  • Learn about running in --touch mode
  • Learn about shadow mode rules

For reference, this is the final Snakefile from episodes 1 to 8 you may use to start this episode.

Temporary files in Snakemake

Analyses in bioinformatics inevitably generate a lot of files. Not all of these need to be kept, if they are intermediate files produced during the workflow. Even if the files might be needed again, knowing that you can regenerate them again with a single Snakemake command means you may well want to clean them up. This way you save disk space and reduce clutter.

Remember that Snakemake only re-generates intermediate files if it needs them to make a target, so simply deleting intermediate files manually is fine. Assuming you already made the multiqc report, and have not edited the rules in the Snakefile since:

OUTPUT 

$ rm trimmed/ref*.fq
$ snakemake -j1 -p multiqc_out
...
Nothing to be done.

To get Snakemake to clean up files for you, mark them with the temporary() function. Much like the directory() function, this is applied only on the outputs of rules. Any file marked as temporary will be removed by Snakemake as soon as it is no longer needed.

To provide a plausible example, lets say we decide to compress the trimmed reads with gzip. It’s very common to store FASTQ files in this compressed format, and most software will read the Gzipped files directly, so there is no need to keep the uncompressed files. This includes Salmon and Kallisto. Add a new rule like so:

rule gzip_fastq:
    output: "trimmed/{afile}.fq.gz"
    input:  "trimmed/{afile}.fq"
    shell:
        "gzip -nc {input} > {output}"

Now we can tell Snakemake not to keep the uncompressed files, but remember we can only add the temporary() marker to outputs, not inputs, so we need to modify the existing trimreads rule.

rule trimreads:
    output: temporary("trimmed/{myfile}.fq")
    input:  "reads/{myfile}.fq"
    shell:
        ...

Snakemake will only run our gzip_fastq rule if we have a consumer for the Gzipped files, so modify both the salmon_quant and kallisto_quant rules to work on the compressed files. In both cases, you just need to add .gz to the fq1 and fq2 input filenames.

rule salmon_quant:
    output: directory("salmon.{sample}")
    input:
        index = "Saccharomyces_cerevisiae.R64-1-1.salmon_index",
        fq1   = "trimmed/{sample}_1.fq.gz",
        fq2   = "trimmed/{sample}_2.fq.gz",
    shell:
        ...

rule kallisto_quant:
    output: directory("kallisto.{sample}")
    input:
        index = "Saccharomyces_cerevisiae.R64-1-1.kallisto_index",
        fq1   = "trimmed/{sample}_1.fq.gz",
        fq2   = "trimmed/{sample}_2.fq.gz",
    shell:
        ...

And now re-run the workflow. Since we modified the trimreads rule, Snakemake should see that it needs to be re-run, and will also re-run any downstream rules, which now includes gzip_fastq. Snakemake will remove the uncompressed trimmed reads once no jobs in the DAG require them, and so at the end we are left with just the Gzipped versions.

OUTPUT 

$ snakemake -j1 -p multiqc_out
...
$ ls trimmed/
etoh60_1_1.fq.gz  ref_1_1.fq.gz  temp33_1_1.fq.gz
etoh60_1_2.fq.gz  ref_1_2.fq.gz  temp33_1_2.fq.gz
etoh60_2_1.fq.gz  ref_2_1.fq.gz  temp33_2_1.fq.gz
etoh60_2_2.fq.gz  ref_2_2.fq.gz  temp33_2_2.fq.gz
etoh60_3_1.fq.gz  ref_3_1.fq.gz  temp33_3_1.fq.gz
etoh60_3_2.fq.gz  ref_3_2.fq.gz  temp33_3_2.fq.gz

Removing the HTML reports from FastQC

We have no use for the HTML reports produced by FastQC. Modify the Snakefile so that Snakemake will automatically remove them.

Amend the html output of the fastqc rule to mark it as temporary():

html = temporary("{indir}.{sample}_fastqc.html"),

Since the files are already there, Snakemake will not normally remove them unless the jobs are re-run, so you could re-run the workflow as was done for trimreads above. However, there is also a --delete-temp-output option which forces all temporary files in the DAG to be removed, and this provides the cleanest way to remove the files after modifying the Snakefile.

BASH 

$ snakemake -p -j1 --delete-temp-output multiqc

Running in --touch mode

One annoying thing about Snakemake is that, in moderately complex workflows, it may seem determined to re-run a job for reasons that are unclear to you. For example, after adding the gzip_fastq rule above we re-ran all of the kallisto and salmon quantifications, but with real data this could take hours or even days.

Let’s pretend we just decided to gzip all the trimmed reads manually, ouside of Snakemake. This results in files with new timestamps, so to simulate this we can just touch all the existing files.

BASH 

$ touch trimmed/*
$ snakemake -n -p multiqc_out
...
Job counts:
    count   jobs
    1       multiqc
    9       salmon_quant
    10

Snakemake wants to re-run all the salmon_quant jobs (and the kallisto_quant jobs, if you completed the exercise above), which makes sense. However, we know the results are good, and don’t want to waste time re-making them, so we can fudge the timestamps using the --touch option. In the words of the Snakemake manual, “This is used to pretend that the rules were executed, in order to fool future invocations of snakemake.”

BASH 

$ snakemake -j 1 --touch -p multiqc_out

Protecting specific files

A related feature is the protected() function. This is rather like the opposite of the temporary() function and says that once an output has been produced it must not be overwritten. In practise, Snakemake does this by revoking write permissions on the files (as in chmod -w {output}).

We can do this for the Salmon and Kallisto indexes, for example, as these should only ever need to be generated once.

This works, but can be annoying because Snakemake will refuse to run if it believes it needs to re-create a file which is protected. An alternative suggestion is, once you have generated an important output file, move or copy the file away from your working directory. That way it will be harder to accidentally clobber it (which is remarkably easy to do!).

Shadow mode rules

Setting your rules to run in shadow mode helps clean up temporary files that are only used within the job. The Snakemake manual describes the shadow directive very nicely, so refer to that for more details.

Briefly, in shadow mode, Snakemake links the input files into a temporary working directory, then runs the shell command and finally copies the outputs back. If you have ever used NextFlow this idea will be familiar as NextFlow runs all workflow steps this way.

This test rule serves to demonstrate the operation of rules using the shadow directive. The file temp_file.txt will not be there after the job has run, but the shadow_out.txt file will be there because Snakemake sees that it is an output file and moves it back to the real working directory,

rule shallow_rule:
    output: "shadow_out.txt"
    shadow: "minimal"
    shell:
        """echo minimal shadow mode
           touch shadow_out.txt
           touch temp_file.txt
           tree `pwd`
        """

Advantages of using shadow rules are:

  • Any temporary files created by applications do not require explicit removal (as with temp_file.txt in the above example).
  • When running jobs in parallel (eg. -j2) certain conflicts related to temporary files will be avoided by not ever running multiple jobs in the same directory at once - we’ve already seen a case back in Episode 07 where the final version of the fastqc rule has this problem.

Disadvantages are:

  • It can be confusing when error messages reference the shadow directory.
  • Symlinks to subdirectories do not always work the way you expect.
  • Shadow directories are not always removed cleanly if Snakemake exits with an error.

You may want to test your rules in normal mode first, then add shadow: "minimal" before you run the workflow for real.

Removing the HTML reports (again)

Amend the fastqc rule once more so that the HTML reports are not even mentioned in the rule, and will not appear in the working directory.

The shadow: "minimal" directive will do the job nicely. You also need to remove mention of the .html file from the list of outputs and the shell commands.

rule fastqc:
    shadow: "minimal"
    output:
        zip  = "{indir}.{myfile}_fastqc.zip"
    input:  "{indir}/{myfile}.fq"
    shell:
        """fastqc -o . {input}
           mv {wildcards.myfile}_fastqc.zip {output.zip}
        """

In this case, marking the html output as temporary() or simply removing the file within the shell part does work fine, but the good thing about the shadow approach is you do not need to deal with or mention the unwanted file at all.

For reference, this is a Snakefile incorporating the changes made in this episode.

Key Points

  • Cleaning up working files is good practise
  • Make use of the temporary() function on outputs you don’t need to keep
  • Protect outputs which are expensive to reproduce
  • Shadow rules can solve issues with commands that produce unwanted files