Welcome to grape.recipe.pipeline’s documentation!

Grape (Grape RNA-Seq Analysis Pipeline Environment) is a pipeline for processing and analyzing RNA-Seq data developed at the Bioinformatics and Genomics unit of the Centre for Genomic Regulation (CRG) in Barcelona.

The grape.buildout package makes use of the grape.recipe.pipeline recipe to configure Grape pipelines. You get preconfigured start and execute scripts, and don’t have to worry about command line options any more. This makes configuring multiple Grape pipelines more convenient.

To learn more about Grape, and to download and install it, go to the Bioinformatics and Genomics website at:

Grape Homepage


The grape.recipe.pipeline package is a Buildout recipe used by grape.buildout, and is not a standalone Python package. It is only going to be useful as installed by the grape.buildout package.


Here at the CRG, we configure all our RNASeq pipeline runs in a central place before running them. Once all the accessions and pipeline profiles have been defined and the buildout parts have been created, we start and execute them on an SGE cluster.

When we receive FASTQ or BAM files for a project, we typically have to:

  1. Define the accessions and profiles:

  2. Create a pipeline project folder:

  3. Configure the buildout:

  4. Run the buildout in:

  5. Run the pipelines in:


The grape.recipe.pipeline recipe plays a major role in step number 4. The buildout uses the recipe to produce the individual pipelines and preconfigure the start and execute scripts with all the necessary command line options.

Accession Configuration Parameters

The accession parameters are mostly derived from UCSC’s ENCODE controlled vocabulary.

The following parameters are used when configuring accessions:


The full path to the file

If there is more than one file, put one file per line

For each line in file_location, a corresponding line needs to be specified for the following parameters:

mate_id Using this label, files can be marked as belonging to one read
pair_id Using this label, files can be associated with pairs
label Using this label, files can be associated with the accession itself

The pairing information is required:

paired Set this to 0 for unpaired reads, an 1 for paired.

Instead of risking to wrongly deduce the file type from the file extensions contained in the file_location parameter, it should be given explicitly here.

type Set the file type. This can be set to fastq or bam

The qualities and the read type are important, because depending on them Grape may produce different results:

qualities The base quality can be se to phred and solexa, or if you don’t know the quality, you can set it to ignore

Paired/Single reads lengths: Specific information about cDNA sequence reads including length, directionality and single versus paired read.

See UCSC’s ENCODE controlled vocabulary for readType.

The replicate and species parameters don’t change anything in the behaviour of Grape, but are considered essential meta data.

replicate The biological replicate of a particular experiment, if the experiment is a bioreplicate
species The species, for example Homo sapiens, Mus musculus

The following parameters have been used in the ENCODE project. It makes sense to fill in the cell parameters and the rnaExtract as well. For most projects, the localization is probably going to be ‘cell’.


Cell, tissue or DNA sample: Cell line or tissue used as the source of experimental material.

See UCSC’s ENCODE controlled vocabulary for cell.


RNA Extract: Fraction of total cellular RNA selected for by an experiment. This includes size fractionation (long versus short) and feature fractionation (PolyA-, PolyA+, rRNA-).

See UCSC’s ENCODE controlled vocabulary for rnaExtract.


Cellular compartment: The cellular compartment from which RNA is extracted. Primarily used by the Transcriptome Project.

See UCSC’s ENCODE controlled vocabulary for localization.

Example Accession Configuration

Here’s a complete example of how the pipelines are configured, taken from the Test project in grape.buildout.

First we define an accession in:


This is the content of the db.cfg file:

species = Homo sapiens
readType = 2x76
file_location = ${buildout:directory}/src/testdata/testA.r2.fastq.gz
mate_id = testA.2
pair_id = testA
label = Test
type = fastq
paired = 1

Profiles Configuration Parameters

The following parameters are configured in the profiles folder, and specify the general parameters of the Grape pipeline.

The project id should be as short as possible.

PROJECTID Name of the project

There are two predefined pipeline templates, one for fastq files as input and one for bam files.


Path to the template defining the pipeline steps

For FASTQ files as input: ${buildout:directory}/src/pipeline/template3.0.txt

For BAM files as input: ${buildout:directory}/src/pipeline/template.bam.txt

There are some technical settings that need to be made so that the results are written to the right databases.

DB Statistic results database name
COMMONDB Meta data Database name
HOST MySQL database host name
CLUSTER Name of the cluster node to use

You can fine-tune the number of threads to be used for any program that can make use of threads, like for example GEM. There is also a setting for the amount of memory to use for the Flux.

THREADS Number of threads to use
FLUXMEM Configures the memory used by the Flux. The default value is 16G

The mapper and the number of mismatches can be set.

MAPPER This currently has to be set to the value GEM
MISMATCHES Number of mismatches for the mapper

The genome and annotation files need to be specified.

GENOMESEQ Genome file
ANNOTATION Annotation file

Preprocessing the reads should be done on the fly. The most common preprocessing step is trimming, so there is one setting for the trim length. You can also specify your own preprocessing script.

PREPROCESS_TRIM_LENGTH A preprocessing step that trims the reads by the given nucleotide length
PREPROCESS Path to a custom script used for preprocessing each of the read files before anything else

You can customize the way the recursive mapping is done, as well as how the postprocessing is done on some files.

MIN_RECURSIVE_MAPPING_TRIM_LENGTH Tunes the minimum length to which a read will be trimmed during the recursive mapping.

Sets the maximum length of splits allowed during the postprocessing of the files generated by gem-2-sam removing the noise.

The default is set to 50k, which is reasonable in mammals, however different species may require different settings. Setting it to 0 will remove this filter.

Example Profile Configuration

Then we need to define the pipeline runs in:


This is the content of the db.cfg file:

parts = TestRun

TEMPLATE   = ${buildout:directory}/src/pipeline/template3.0.txt
DB         = Test_RNAseqPipeline
COMMONDB   = Test_RNAseqPipelineCommon
THREADS    = 8
CLUSTER    = mem_6
ANNOTATION = ${buildout:directory}/src/testdata/H.sapiens.EnsEMBL.55.test.gtf
GENOMESEQ  = ${buildout:directory}/src/testdata/H.sapiens.genome.hg19.test.fa

accession = TestRun

Now that we have the accessions and profiles defined, we can go to our project folder and define the buildout.cfg that will produce our Grape pipelines:


The buildout.cfg should look like this:

extends = ../dependencies.cfg

There are pointers to the accession and profile. The dependencies file takes care of installing all the dependencies, like overlap, flux, gem, and the Grape pipeline.


Indices and tables