Additional features

In the following, we introduce some features that are beyond the scope of above example workflow. For details and even more features, see the Welcome to Snakemake’s documentation!, the Frequently Asked Questions and the command line help (snakemake --help).

Benchmarking

With the benchmark directive, Snakemake can be instructed to measure the wall clock time of a job. We activate benchmarking for the rule bwa_map:

rule bwa_map:
    input:
        "data/genome.fa",
        lambda wildcards: config["samples"][wildcards.sample]
    output:
        temp("mapped_reads/{sample}.bam")
    params:
        rg="@RG\tID:{sample}\tSM:{sample}"
    log:
        "logs/bwa_map/{sample}.log"
    benchmark:
        "benchmarks/{sample}.bwa.benchmark.txt"
    threads: 8
    shell:
        "(bwa mem -R '{params.rg}' -t {threads} {input} | "
        "samtools view -Sb - > {output}) 2> {log}"

The benchmark directive takes a string that points to the file where benchmarking results shall be stored. Similar to output files, the path can contain wildcards (it must be the same wildcards as in the output files). When a job derived from the rule is executed, Snakemake will measure the wall clock time and store it in the file in tab-delimited format. With the command line flag --benchmark-repeats, Snakemake can be instructed to perform repetitive measurements by executing benchmark jobs multiple times. The repeated measurements occur as subsequent lines in the tab-delimited benchmark file.

We can include the benchmark results into our report:

rule report:
    input:
        T1="calls/all.vcf",
        T2=expand("benchmarks/{sample}.bwa.benchmark.txt", sample=config["samples"])
    output:
        "report.html"
    run:
        from snakemake.utils import report
        with open(input.T1) as vcf:
            n_calls = sum(1 for l in vcf if not l.startswith("#"))

        report("""
        An example variant calling workflow
        ===================================

        Reads were mapped to the Yeast
        reference genome and variants were called jointly with
        SAMtools/BCFtools.

        This resulted in {n_calls} variants (see Table T1_).
        Benchmark results for BWA can be found in the tables T2_.
        """, output[0], **input)

We use the expand function to collect the benchmark files for all samples. Here, we directly provide names for the input files. In particular, we can also name the whole list of benchmark files returned by the expand function as T2. When invoking the report function, we just unpack input into keyword arguments (resulting in T1 and T2). In the text, we refer with T2_ to the list of benchmark files.

Exercise

• Re-execute the workflow and benchmark bwa_map with 3 repeats. Open the report and see how the list of benchmark files is presented in the HTML report.

Modularization

In order to re-use building blocks or simply to structure large workflows, it is sometimes reasonable to split a workflow into modules. For this, Snakemake provides the include directive to include another Snakefile into the current one, e.g.:

include: "path/to/other.snakefile"

Alternatively, Snakemake allows to define sub-workflows. A sub-workflow refers to a working directory with a complete Snakemake workflow. Output files of that sub-workflow can be used in the current Snakefile. When executing, Snakemake ensures that the output files of the sub-workflow are up-to-date before executing the current workflow. This mechanism is particularly useful when you want to extend a previous analysis without modifying it. For details about sub-workflows, see the Welcome to Snakemake’s documentation!.

Exercise

• Put the read mapping related rules into a separate Snakefile and use the include directive to make them available in our example workflow again.

Using custom scripts

Using the run directive as above is only reasonable for short Python scripts. As soon as your script becomes larger, it is reasonable to separate it from the workflow definition. For this purpose, Snakemake offers the script directive. Using this, report rule from above could instead look like this:

rule report:
    input:
        T1="calls/all.vcf",
        T2=expand("benchmarks/{sample}.bwa.benchmark.txt", sample=config["samples"])
    output:
        "report.html"
    script:
        "scripts/report.py"

The actual Python code to generate the report is now hidden in the script scripts/report.py. Script paths are always relative to the referring Snakefile. In the script, all properties of the rule like input, output, wildcards, params, threads etc. are available as attributes of a global snakemake object:

from snakemake.utils import report

with open(snakemake.input.T1) as vcf:
    n_calls = sum(1 for l in vcf if not l.startswith("#"))

report("""
An example variant calling workflow
===================================

Reads were mapped to the Yeast
reference genome and variants were called jointly with
SAMtools/BCFtools.

This resulted in {n_calls} variants (see Table T1_).
Benchmark results for BWA can be found in the tables T2_.
""", snakemake.output[0], **snakemake.input)

Although there are other strategies to invoke separate scripts from your workflow (e.g., invoking them via shell commands), the benefit of this is obvious: the script logic is separated from the workflow logic (and can be even shared between workflows), but boilerplate code like the parsing of command line arguments in unnecessary.

Apart from Python scripts, it is also possible to use R scripts. In R scripts, an S4 object named snakemake analog to the Python case above is available and allows access to input and output files and other parameters. Here the syntax follows that of S4 classes with attributes that are R lists, e.g. we can access the first input file with snakemake@input[[1]] (note that the first file does not have index 0 here, because R starts counting from 1). Named input and output files can be accessed in the same way, by just providing the name instead of an index, e.g. snakemake@input[["myfile"]].

For details and examples, see the External scripts section in the Documentation.

Cluster execution

By default, Snakemake executes jobs on the local machine it is invoked on. Alternatively, it can execute jobs in distributed environments, e.g., compute clusters or batch systems. If the nodes share a common file system, Snakemake supports three alternative execution modes.

In cluster environments, compute jobs are usually submitted as shell scripts via commands like qsub. Snakemake provides a generic mode to execute on such clusters. By invoking Snakemake with

$ snakemake --cluster qsub --jobs 100

each job will be compiled into a shell script that is submitted with the given command (here qsub). The --jobs flag limits the number of concurrently submitted jobs to 100. This basic mode assumes that the submission command returns immediately after submitting the job. Some clusters allow to run the submission command in synchronous mode, such that it waits until the job has been executed. In such cases, we can invoke e.g.

$ snakemake --cluster-sync "qsub -sync yes" --jobs 100

The specified submission command can also be decorated with additional parameters taken from the submitted job. For example, the number of used threads can be accessed in braces similarly to the formatting of shell commands, e.g.

$ snakemake --cluster "qsub -pe threaded {threads}" --jobs 100

Alternatively, Snakemake can use the Distributed Resource Management Application API (DRMAA). This API provides a common interface to control various resource management systems. The DRMAA support can be activated by invoking Snakemake as follows:

$ snakemake --drmaa --jobs 100

If available, DRMAA is preferable over the generic cluster modes because it provides better control and error handling. To support additional cluster specific parametrization, a Snakefile can be complemented by a Cluster Configuration file.

Constraining wildcards

Snakemake uses regular expressions to match output files to input files and determine dependencies between the jobs. Sometimes it is useful to constrain the values a wildcard can have. This can be achieved by adding a regular expression that describes the set of allowed wildcard values. For example, the wildcard sample in the output file "sorted_reads/{sample}.bam" can be constrained to only allow alphanumeric sample names as "sorted_reads/{sample,[A-Za-z0-9]+}.bam". This mechanism helps to solve two kinds of ambiguity.

• It can help to avoid ambiguous rules, i.e. two or more rules that can be applied to generate the same output file. Other ways of handling ambiguous rules are described in the Section Handling Ambiguous Rules.
• It can help to guide the regular expression based matching so that wildcards are assigned to the right parts of a file name. Consider the output file {sample}.{group}.txt and assume that the target file is A.1.normal.txt. It is not clear whether dataset="A.1" and group="normal" or dataset="A" and group="1.normal" is the right assignment. Here, constraining the dataset wildcard by {sample,[A-Z]+}.{group} solves the problem.

When dealing with ambiguous rules, it is best practice to first try to solve the ambiguity by using a proper file structure, for example, by separating the output files of different steps in different directories.