pyppl 3.0.0rc2

A Python PiPeLine framework

PyPPL - A Python PiPeLine framework

Documentation | API | Change log

Features

• Process caching.
• Process reusability.
• Process error handling.
• Runner customization.
• Easy running profile switching.
• Plugin system.

Plugin gallery

• pyppl_report: Generating reports for PyPPL pipelines
• pyppl_flowchart: Generating flowchart for PyPPL
• pyppl_export: Export outputs generated by PyPPL pipeline
• pyppl_echo: Echo script output to PyPPL logs"
• pyppl_rich: Richer information in logs for PyPPL
• pyppl_strict: More strict check of job success for PyPPL
• pyppl_lock: Preventing running processes from running again for PyPPL
• pyppl_runners: Common runners for PyPPL

Installation

pip install PyPPL

Writing pipelines with predefined processes

Let's say we are implementing the TCGA DNA-Seq Re-alignment Workflow (The very left part of following figure). For demonstration, we will skip the QC and the co-clean parts here.

demo.py:

from pyppl import PyPPL, Channel
# import predefined processes
from TCGAprocs import pBamToFastq, pAlignment, pBamSort, pBamMerge, pMarkDups

# Load the bam files
pBamToFastq.input = Channel.fromPattern('/path/to/*.bam')
# Align the reads to reference genome
pAlignment.depends = pBamToFastq
# Sort bam files
pBamSort.depends = pAlignment
# Merge bam files
pBamMerge.depends = pBamSort
# Mark duplicates
pMarkDups.depends = pBamMerge
# Export the results
pMarkDups.exdir = '/path/to/realigned_Bams'
# Specify the start process and run the pipeline
PyPPL().start(pBamToFastq).run()

Implementing individual processes

TCGAprocs.py:

from pyppl import Proc
pBamToFastq = Proc(desc = 'Convert bam files to fastq files.')
pBamToFastq.input = 'infile:file'
pBamToFastq.output = [
    'fq1:file:{{i.infile | stem}}_1.fq.gz',
    'fq2:file:{{i.infile | stem}}_2.fq.gz']
pBamToFastq.script = '''
bamtofastq collate=1 exclude=QCFAIL,SECONDARY,SUPPLEMENTARY \
    filename= {{i.infile}} gz=1 inputformat=bam level=5 \
    outputdir= {{job.outdir}} outputperreadgroup=1 tryoq=1 \
    outputperreadgroupsuffixF=_1.fq.gz \
    outputperreadgroupsuffixF2=_2.fq.gz \
    outputperreadgroupsuffixO=_o1.fq.gz \
    outputperreadgroupsuffixO2=_o2.fq.gz \
    outputperreadgroupsuffixS=_s.fq.gz
'''

pAlignment = Proc(desc = 'Align reads to reference genome.')
pAlignment.input = 'fq1:file, fq2:file'
#                             name_1.fq.gz => name.bam
pAlignment.output = 'bam:file:{{i.fq1 | stem | stem | [:-2]}}.bam'
pAlignment.script = '''
bwa mem -t 8 -T 0 -R <read_group> <reference> {{i.fq1}} {{i.fq2}} | \
    samtools view -Shb -o {{o.bam}} -
'''

pBamSort = Proc(desc = 'Sort bam files.')
pBamSort.input = 'inbam:file'
pBamSort.output = 'outbam:file:{{i.inbam | basename}}'
pBamSort.script = '''
java -jar picard.jar SortSam CREATE_INDEX=true INPUT={{i.inbam}} \
    OUTPUT={{o.outbam}} SORT_ORDER=coordinate VALIDATION_STRINGENCY=STRICT
'''

pBamMerge = Proc(desc = 'Merge bam files.')
pBamMerge.input = 'inbam:file'
pBamMerge.output = 'outbam:file:{{i.inbam | basename}}'
pBamMerge.script = '''
java -jar picard.jar MergeSamFiles ASSUME_SORTED=false CREATE_INDEX=true \
    INPUT={{i.inbam}} MERGE_SEQUENCE_DICTIONARIES=false OUTPUT={{o.outbam}} \
    SORT_ORDER=coordinate USE_THREADING=true VALIDATION_STRINGENCY=STRICT
'''

pMarkDups = Proc(desc = 'Mark duplicates.')
pMarkDups.input = 'inbam:file'
pMarkDups.output = 'outbam:file:{{i.inbam | basename}}'
pMarkDups.script = '''
java -jar picard.jar MarkDuplicates CREATE_INDEX=true INPUT={{i.inbam}} \
    OUTPUT={{o.outbam}} VALIDATION_STRINGENCY=STRICT
'''

Each process is indenpendent so that you may also reuse the processes in other pipelines.

Pipeline flowchart

# When try to run your pipline, instead of:
#   PyPPL().start(pBamToFastq).run()
# do:
PyPPL().start(pBamToFastq).flowchart().run()

Then an SVG file endswith .pyppl.svg will be generated under current directory. Note that this function requires Graphviz and graphviz for python.

See plugin details.

Pipeline report

See plugin details

pPyClone.report = """
## {{title}}

PyClone[1] is a tool using Probabilistic model for inferring clonal population structure from deep NGS sequencing.

![Similarity matrix]({{path.join(job.o.outdir, "plots/loci/similarity_matrix.svg")}})

```table
caption: Clusters
file: "{{path.join(job.o.outdir, "tables/cluster.tsv")}}"
rows: 10
```

[1]: Roth, Andrew, et al. "PyClone: statistical inference of clonal population structure in cancer." Nature methods 11.4 (2014): 396.
"""

# or use a template file

pPyClone.report = "file:/path/to/template.md"

PyPPL().start(pPyClone).run().report('/path/to/report', title = 'Clonality analysis using PyClone')

Full documentation

ReadTheDocs