This document discusses generating provenance mining rules from annotated workflow patterns and experiment report templates to refine provenance traces into linked experiment reports. It presents an approach that takes domain-specific annotations of workflow patterns and report templates as input to generate SPARQL-based provenance mining rules. An experiment applies a generated rule to provenance traces from a real-life RNA-seq workflow to produce RDF triples following the MicroPublication ontology, demonstrating how provenance can be leveraged to automate publishing experimental results as linked open data. Future work is needed to handle more complex workflow patterns and evaluate the approach at a larger scale.

1. From Scientific Workflow Patterns
to 5-star Linked Open Data
Alban Gaignard Hala Skaf-Molli Audrey Bihouée
Nantes Academic
Hospital (CHU de
Nantes), CNRS, France
LINA,
Nantes University,
CNRS,
France
Institut du Thorax,
Nantes University,
INSERM, CNRS,
France
8th USENIX workshop on Theory and Practice of Provenance (TaPP’16)
Washington DC

2. Needs for linked experiment reports
2

3. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing (massive) RNA-seq data
TopHat: algorithm to align multiple sequence reads to a reference
genome (known genes).
3

4. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing (massive) RNA-seq data
TopHat: algorithm to align multiple sequence reads to a reference
genome (known genes).
4
1 sample
Input data 2 x 17 Gb
1-core CPU 170 hours
32-cores CPU 32 hours
Output data 12 Gb

5. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing (massive) RNA-seq data
TopHat: algorithm to align multiple sequence reads to a reference
genome (known genes).
5
1 sample 300 samples
Input data 2 x 17 Gb 10.2 Tb
1-core CPU 170 hours 5.9 years
32-cores CPU 32 hours 14 months
Output data 12 Gb 3.6 Tb
1 sample 300 samples
Input data 2 x 17 Gb 10.2 Tb
1-core CPU 170 hours 5.9 years
32-cores CPU 32 hours 14 months
Output data 12 Gb 3.6 Tb
1 sample
Input data 2 x 17 Gb
1-core CPU 170 hours
32-cores CPU 32 hours
Output data 12 Gb

6. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing (massive) RNA-seq data
TopHat: algorithm to align multiple sequence reads to a reference
genome (known genes).
6
1 sample 300 samples
Input data 2 x 17 Gb 10.2 Tb
1-core CPU 170 hours 5.9 years
32-cores CPU 32 hours 14 months
Output data 12 Gb 3.6 Tb
Challenges
Algorithmic performance, storage, preservation,
reuse (limit recompute) & share.
1 sample 300 samples
Input data 2 x 17 Gb 10.2 Tb
1-core CPU 170 hours 5.9 years
32-cores CPU 32 hours 14 months
Output data 12 Gb 3.6 Tb
1 sample
Input data 2 x 17 Gb
1-core CPU 170 hours
32-cores CPU 32 hours
Output data 12 Gb

7. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing experiment results
Scientific experiment: RNA sequencing to quantify gene expression
levels under multiple biological conditions.
7

8. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Motivations: reusing experiment results
Scientific experiment: RNA sequencing to quantify gene expression
levels under multiple biological conditions.
8
Need for scientific context : metadata

9. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Expected result: human+machine tractable reports
9
Annotated “Material & Methods”
Links to some workflow artifacts (algorithms, data)

10. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
5-star Linked Open Data
10
W3C standards for machine and human
readable data on the web.
⭑⭑⭑⭑⭑ : time and expertise !

11. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
5-star Linked Open Data
11
How to ease this process ?
● Workflow engines → automation
● PROV → workflow runs as linked data
W3C standards for machine and human
readable data on the web.
⭑⭑⭑⭑⭑ : time and expertise !

12. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
PROV only
12

13. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
PROV only
13
too fine-grained
no domain concepts

14. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Provenance as a Linked Experiment Report
14
few + meaningful statements

15. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Problem statement & objectives
15
Problem statement
Scientific workflows produce massive raw results. Their
publication into curated query-able linked data repositories
requires lot of time and expertise.
Can we exploit provenance traces to ease the publication of
scientific results as Linked Data ?

16. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Problem statement & objectives
16
Problem statement
Scientific workflows produce massive raw results. Their
publication into curated query-able linked data repositories
requires lot of time and expertise.
Can we exploit provenance traces to ease the publication of
scientific results as Linked Data ?
Objectives
(1) Leverage annotated workflow patterns to generate
provenance mining rules.
(2) Refine provenance traces into linked experiment reports.

17. Rules generation
17

18. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Approach
18

19. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Input domain-specific annotations (❶,❷)
Workflow patterns ❶
19
Sequence patterns, with possibly intermediate steps
● P-PLAN ontology: Step, Variable, hasInputVar, hasOutputVar
● EDAM ontology: hasFunction, RNA sequence, Genome map

20. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Input domain-specific annotations (❶,❷)
Workflow patterns ❶
20
Sequence patterns, with possibly intermediate steps
● P-PLAN ontology: Step, Variable, hasInputVar, hasOutputVar
● EDAM ontology: hasFunction, RNA sequence, Genome map
Experiment report template ❷
Link scientific claims, statements, material and methods
● MicroPublication ontology: Material, Method, Claims
● Experimental factor ontology: Transcriptome, Gene expression
● NCBI taxonomy: Homo Sapiens
● Open Annotation model: hasBody, hasTarget

21. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
PoeM: generating PrOvEnance Mining rules ❸
21
(SPARQL Property path)
(SPARQL Basic graph pattern)
(SPARQL Construct query)

22. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
PoeM: sample generated rule ❸
22
<If> part
<Then> part

23. First experiments &
results
23

24. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Experiment context
24
SyMeTRIC: systems medicine project (2015-2017, call “Connect
Talent”), funded by the french region Pays de la Loire.

25. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Experiment
25
Material & methods
● Real-life RNA-seq workflow to study 3 mice populations
● WF implemented in Galaxy, run on 2 biological samples
● PROV traces exported from Galaxy Histories (API)

26. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Experiment
26
Material & methods
● Real-life RNA-seq workflow to study 3 mice populations
● WF implemented in Galaxy, run on 2 biological samples
● PROV traces exported from Galaxy Histories (API)
Results (for 1 biological sample)
● 60h CPU (12 cores for genome alignment), 21Gb storage
● 3s to export 81 PROV triples from the Galaxy history
● 2s to apply the rule and produce 35 Micropublication triples

27. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Conclusion & perspectives
27
Semi-automated approach
(1) PoeM generates semantic web rules
(2) PoeM rules applied on PROV traces to assemble
linked experiment reports (MicroPublication)
Limitations:
- Sequence workflow patterns only
- SPARQL property paths with complex WF patterns ?
- Syntactic matching between WF patterns and PROV labels
Usage scenarios:
→ Query workflow datasets with domain concepts
→ Populate RDF repositories with WF results

28. TaPP ‘16A. Gaignard - H. Skaf-Molli - A. Bihouée
Conclusion & perspectives
28
Future works
(1) WF patterns: split-merge, “common motifs”
(2) Genericity: other domains / other reports (RO, Nanopub.)
(3) PROV heterogeneity: multi-systems PROV
(4) Evaluation: involving biologists, at larger scale

29. Questions ?
Demo: http://poem.univ-nantes.fr
Contact: alban.gaignard@univ-nantes.fr
Acknowledgments
BiRD bioinformatics facility Connect Talent Call

30. TaPP’16A. Gaignard, H. Skaf-Molli, A. Bihouée 30
Larger scale experiments (PROV traces)
1232 edges

31. TaPP’16A. Gaignard, H. Skaf-Molli, A. Bihouée 31
Larger scale experiments (PROV traces)
49 edges