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 Writing Workflows, 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_mem/{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 memory usage (in MiB) and store it in the file in tab-delimited format. It is possible to repeat a benchmark multiple times in order to get a sense for the variability of the measurements. This can be done by annotating the benchmark file, e.g., with repeat("benchmarks/{sample}.bwa.benchmark.txt", 3) Snakemake can be told to run the job three times. The repeated measurements occur as subsequent lines in the tab-delimited benchmark file.

Modularization

In order to reuse 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.smk"

As can be seen, the default file extensions for snakefiles other than the main snakefile is .smk. 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 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.

Automatic deployment of software dependencies

In order to get a fully reproducible data analysis, it is not sufficient to be able to execute each step and document all used parameters. The used software tools and libraries have to be documented as well. In this tutorial, you have already seen how Conda can be used to specify an isolated software environment for a whole workflow. With Snakemake, you can go one step further and specify Conda environments per rule. This way, you can even make use of conflicting software versions (e.g. combine Python 2 with Python 3).

In our example, instead of using an external environment we can specify environments per rule, e.g.:

rule samtools_index:
  input:
      "sorted_reads/{sample}.bam"
  output:
      "sorted_reads/{sample}.bam.bai"
  conda:
      "envs/samtools.yaml"
  shell:
      "samtools index {input}"

with envs/samtools.yaml defined as

channels:
  - bioconda
  - conda-forge
dependencies:
  - samtools =1.9

Note

The conda directive does not work in combination with run blocks, because they have to share their Python environment with the surrounding snakefile.

When Snakemake is executed with

snakemake --software-deployment-method conda --cores 1
# or the short form
  snakemake --sdm conda -c 1

it will automatically create required environments and activate them before a job is executed. It is best practice to specify at least the major and minor version of any packages in the environment definition. Specifying environments per rule in this way has two advantages. First, the workflow definition also documents all used software versions. Second, a workflow can be re-executed (without admin rights) on a vanilla system, without installing any prerequisites apart from Snakemake and Miniconda.

Tool wrappers

In order to simplify the utilization of popular tools, Snakemake provides a repository of so-called wrappers (the Snakemake wrapper repository). A wrapper is a short script that wraps (typically) a command line application and makes it directly addressable from within Snakemake. For this, Snakemake provides the wrapper directive that can be used instead of shell, script, or run. For example, the rule bwa_map could alternatively look like this:

rule bwa_mem:
  input:
      ref="data/genome.fa",
      sample=lambda wildcards: config["samples"][wildcards.sample]
  output:
      temp("mapped_reads/{sample}.bam")
  log:
      "logs/bwa_mem/{sample}.log"
  params:
      "-R '@RG\tID:{sample}\tSM:{sample}'"
  threads: 8
  wrapper:
      "0.15.3/bio/bwa/mem"

Note

Updates to the Snakemake wrapper repository are automatically tested via continuous integration.

The wrapper directive expects a (partial) URL that points to a wrapper in the repository. These can be looked up in the corresponding database. The first part of the URL is a Git version tag. Upon invocation, Snakemake will automatically download the requested version of the wrapper. Furthermore, in combination with --software-deployment-method conda (see Automatic deployment of software dependencies), the required software will be automatically deployed before execution.

Cluster or cloud execution

Executing jobs on a cluster or in the cloud is supported by so-called executor plugins, which are distributed and documented via the [Snakemake plugin catalog](https://snakemake.github.io/snakemake-plugin-catalog/).

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". Constraints may be defined per rule or globally using the wildcard_constraints keyword, as demonstrated in Wildcards. 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.