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 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 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, the 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.
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
dependencies:
- samtools =1.3
When Snakemake is executed with
snakemake --use-conda
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"
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 --use-conda
(see Automatic deployment of software dependencies),
the required software will be automatically deployed before execution.
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"
.
Constrains 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 isA.1.normal.txt
. It is not clear whetherdataset="A.1"
andgroup="normal"
ordataset="A"
andgroup="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.