Name: ratatosk.ext.scilife
Owner: Science For Life Laboratory
Description: Ratatosk extension for use at Science for Life Laboratory
Created: 2013-03-25 13:39:06.0
Updated: 2013-03-25 14:36:20.0
Pushed: 2013-03-24 21:23:05.0
Homepage: null
Size: 79
Language: Python
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ratatosk.ext.scilife is a ratatosk library extension. It contains
functions, configuration and customized scripts for running ratatosk
tasks at Science for Life Laboratory. Among other things, the module
is designed to work out of the box with raw data delivered from the
Science for Life Laboratory Genomics Core facility in Stockholm.
Install ratatosk by following the instructions at
ratatosk.
To install the development version of ratatosk.ext.scilife, do
git clone https://github.com/percyfal/ratatosk.ext.scilife
python setup.py develop
By default, samples sequenced at Science for Life Laboratory Genomics
Core Facility, Stockholm, are organized by directories project name,
sample id, and flowcell. Each flowcell, finally, contains the
sequence files (in gzipped fastq format) labelled with lane and
barcode sequence information. For obvious reasons, the test data in
ngs.test.data has been
organized according to these principles. Running the command tree on
the project J.Doe_00_01 yields
|-- P001_101_index3
| |-- 120924_AC003CCCXX
| | |-- P001_101_index3_TGACCA_L001_R1_001.fastq.gz
| | |-- P001_101_index3_TGACCA_L001_R2_001.fastq.gz
| | `-- SampleSheet.csv
| |-- 121015_BB002BBBXX
| | |-- P001_101_index3_TGACCA_L001_R1_001.fastq.gz
| | |-- P001_101_index3_TGACCA_L001_R2_001.fastq.gz
| | `-- SampleSheet.csv
|-- P001_102_index6
| `-- 120924_AC003CCCXX
| |-- P001_102_index6_ACAGTG_L002_R1_001.fastq.gz
| |-- P001_102_index6_ACAGTG_L002_R2_001.fastq.gz
| `-- SampleSheet.csv
Here, sample P001_101_index3 has sequence data from flowcells
120924_AC003CCCXX and 121015_BB002BBBXX, both run in lane 1
(L001) with barcode sequence TGACCA.
Some ratatosk tasks require helper functions that collect
information about samples and raw data. The ratatosk.scilife target
generating functions have been tailored to work with data organized as
described in the previous section. For instance, the function
ratatosk.ext.scilife.sample.organize_sample_runs collects and
organizes information about sample runs for task MergeSamFiles which
merges aligned sequences for a sample and places the output directly
in the sample folder. Similarly,
ratatosk.ext.scilife.sample.target_generator collects information
about project samples for use with best practice pipelines.
The only difference between this script and the main ratatosk run
script is that ratatosk_run_scilife.py loads pipeline configuration
files located at ratatosk.config.scilife. These configuration files
use target generator functions specific to scilife data structure.
ratatosk_submit_job.py is a simple wrapper that uses the python
module drmaa for submitting ratatosk jobs to a cluster.