Genome Informatics 2013 – P.Missier From scripted HPC-based NGS pipelines to workflows on the cloud Jacek Cała, Yaobo Xu, Eldarina Azfar Wijaya, Paolo Missier School of Computing Science and Institute of Genetic Medicine Newcastle University, Newcastle upon Tyne, UK C4Bio workshop @CCGrid 2014 Chicago, May 26 th , 2014
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Gen
ome
Info
rmat
ics
2013
– P
.Mis
sier
From scripted HPC-based NGS pipelines to workflows on the cloud
Jacek Cała, Yaobo Xu, Eldarina Azfar Wijaya, Paolo Missier
School of Computing Science and Institute of Genetic MedicineNewcastle University, Newcastle upon Tyne, UK
C4Bio workshop @CCGrid 2014
Chicago, May 26th, 2014
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erThe Cloud-e-Genome project
NGS data processing:
provide mechanisms to rapidly and flexibly create new exome sequence data processing pipelines, and to deploy them in a scalable way;
CostScalabilityFlexibility
Data to insightHuman variant interpretation for clinical diagnosis:provide clinicians with a tool for analysis and interpretation of human variants
• 2 year pilot project• Funded by UK’s National Institute for Health Research (NIHR)
through the Biomedical Research Council (BRC)• Nov. 2013: Cloud resources from Azure for Research Award
• 1 year’s worth of data/network/computing resources
Challenge:
to deliver the benefits of WES/WGS technology to clinical practice
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erKey technical goals
• Scalability• In the rate and number of patient sequence submissions
• In the density of sequence data (from whole exome to whole genome)
• Flexibility, Traceability, Comparability across versions• Simplify experimenting with alternative pipelines (choice of tools, configuration
parameters)
• Trace each version and its executions
• Ability to compare results obtained using different pipelines and reason about the differences
• Openness. Simplify the process of adding:• New variant analysis tools
• New statistical methods for variant filtering, selection, and ranking
• Integration with third party databases
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erApproach and testbed
Technical Approach:• double- porting
• Infrastructure: HPC cluster to cloud (IaaS)
• Implementation: NGS pipelines from scripts to workflow
• Implement user tools for clinical diagnosis as cloud apps (SaaS)
Testbed and scale:• Neurological patients from the North-East of England, focus on rare diseases
• Initial testing on about 300 sequences
• 2500-3000 sequences expected within 12 months
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erWhy port to workflow?
• Programming:• Workflows provide better abstraction in the specification of pipelines
• Workflows directly executable by enactment engine
• Easier to understand, share, and maintain over time
• Flexible – relatively easy to introduce variations
• Exploits available data parallelism (but not automagically)
• Reproducibility
• Execution monitoring, provenance collection• Persistence trace serves as evidence for data
• Amenable to automated analysis
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erScripted pipeline
RecalibrationCorrects for system bias on quality scores assigned by sequencer
Computes coverage of each read.
VCF Subsetting by filtering, eg non-exomic variants
Annovar functional annotations (eg MAF, synonimity, SNPs…)followed by in house annotations
Aligns sample sequence to HG19 reference genomeusing BWA aligner
Cleaning, duplicate elimination
Picard tools
Variant calling operates on multiple samples simultaneouslySplits samples into chunks.Haplotype caller detects both SNV as well as longer indels
Variant recalibration attempts to reduce false positive rate from caller
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erFrom scripts to workflows
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erWorkflow nesting
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erPipeline evolution
Pipeline:
set C = { c1 … cn } of components -- tool wrappers
Each ci has a configuration conf(ci) and a version v(ci)
…and why
• Technology / algorithm evolution• Traditional GATK variant caller
GATK haplotype caller• Does the interface change?• Do the operational assumptions
change?
Eg. GATK Variant Recalibrator requires large input data. Not suitable for targeted sequencing
What can change
1 – Tool version:v(ci) v’(ci)
2 - Tool replacement / add / remove:ci c’I
3 – Configuration parametersconf(ci) conf’(ci)
(*) S. Pabinger, A. Dander, M. Fischer, R. Snajder, M. Sperk, M. Efremova, B. Krabichler, M. R. Speicher, J. Zschocke, and Z. Trajanoski, “A survey of tools for variant analysis of next-generation genome sequencing data.” Briefings in bioinformatics, pp. bbs086–, Jan. 2013
Just for sequence alignment Pabinger et al. in their survey (*) list 17 aligners while for variant annotation they refer over 70 tools
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erRole of provenance
Provenance refers to the sources of information, including entities and processes, involving in producing or delivering an artifact (*)
Provenance is a description of how things came to be, and how they came to be in the state they are in today (*)
• Provenance is evidence in support of clinical diagnosis1. Why do these variants appear in the output list?
2. Why have you concluded they are disease-causing?
• Requires ability to trace variants through workflow execution• Simple scripting lacks this functionality
“Where do these variants come from?”
“Why do these results differ?”
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erComparing results across pipeline configurations
Missier, Paolo, Simon Woodman, Hugo Hiden, and Paul Watson. “Provenance and Data Differencing for Workflow Reproducibility Analysis.” Concurrency and Computation: Practice and Experience (2013): doi:10.1002/cpe.3035.
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erPDIFF - overview
WA
WB
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erThe corresponding provenance traces
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erDelta graph computed by PDIFF
PDIFF helps determine the impact of variations in the pipeline