From peer-reviewed to peer-reproduced: a role for research objects in scholarly publishing in the life sciences
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The document discusses how research objects and computational workflows can help capture experimental processes and reproduce findings in life sciences research. It describes a computational experiment evaluating three genome assembly algorithms on bacterial, insect, and human genomes. Key steps included identifying resources, designing the experimental workflow, running the experiment in Galaxy, and publishing results as nanopublications aggregated in a research object to enable verification and reuse. The goal is to improve reproducibility by making experimental descriptions and reviews more structured and transparent.
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From peer-reviewed to peer-reproduced: a role for research objects in scholarly publishing in the life sciences
- 1. From peer-reviewed to peer-reproduced: a role for research objects in scholarly publishing in the life sciences Alejandra González-Beltrán Oxford e-Research Centre, University of Oxford -ontology.org Bioinformatics Open Source Conference (BOSC), Dublin, Ireland July 10-11 2015
- 3. "AGBell Notebook" by Alexander Graham Bell. (d. 1922) - page 40-41 of Alexander Graham Bell Family Papers in the Library of Congress' Manuscript Division. Licensed under Public Domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:AGBell_Notebook.jpg#/media/File:AGBell_Notebook.jpg http://petcaretips.net/bonding-rabbit-to-pets.html Many things have been said about the challenges of science reproducibility and how it can go wrong… Difficulties when the description of the experimental steps is only available in lab notebooks and scientific articles; lack of data, lack of software tools required for analysis
- 4. Can data models and computational workflows help in capturing the experimental processes and reproduce findings? How? experimental description (design & steps) conclusions computational workflows aggregation & workflow preservation
- 5. Can data models and computational workflows help in capturing the experimental processes and reproduce findings? How?
- 6. Can data models and computational workflows help in capturing the experimental processes and reproduce findings? How?
- 7. Can data models and computational workflows help in capturing the experimental processes and reproduce findings? How?
- 8. • open peer-review • availability of • data • analysis scripts • documentation Evaluation of SOAPdenovo2 tool for the de novo assembly of genomes from small DNA segments reads by next generation sequencing, implementing improvements over SOAPdenovo1 assembler. pre-publication history https://github.com/aquaskyline/SOAPdenovo2 http://sourceforge.net/projects/soapdenovo2/
- 10. Experimental Description EXCELERATE interoperability component http://www.ncbi.nlm.nih.gov/books/NBK279831/ http://elixir-uk.org/interoperability-infrastructure
- 11. genome assembly algorithm genome size Predictor Variables (Factor Name, Factor Type) The experimental plan - computational case
- 12. genome assembly algorithm genome size SOAPdenovo2 SOAPdenovo1 ALL-PATHS-LG bacterial genome insect genome human genome Predictor Variables (Factor Name, Factor Type) The experimental plan - computational case
- 13. genome assembly algorithm genome size SOAPdenovo2 SOAPdenovo1 ALL-PATHS-LG bacterial genome insect genome human genome bacterial genome insect genome human genome bacterial genome insect genome human genome Predictor Variables (Factor Name, Factor Type) 3x3 factorial design 9 study groups The experimental plan - computational case
- 14. genome assembly algorithm genome size SOAPdenovo2 SOAPdenovo1 ALL-PATHS-LG bacterial genome insect genome human genome bacterial genome insect genome human genome bacterial genome insect genome human genome Predictor Variables (Factor Name, Factor Type) The experimental plan - computational case S. aureus R. sphaeroides B. impatiens Chinese Han genome (orYH genome)
- 15. genome assembly algorithm genome size SOAPdenovo2 SOAPdenovo1 ALL-PATHS-LG bacterial genome insect genome human genome bacterial genome insect genome human genome bacterial genome insect genome human genome Predictor Variables (Factor Name, Factor Type) The experimental plan - computational case Response Variables (with units) genome coverage (%) computation run time (h) peak memory consumption (Gb) contig N50 (kb or bp) scaffold N50 (kb or bp) number of errors
- 16. The experimental steps Unambiguous identification of resources (e.g. record from public repositories); persistent identifiers if available (ORCIDs, DOIs); we suggest a dedicated article section Experimental workflows - identification of processes, their inputs and outputs Experimental design: identify experimental goal, independent and response variables
- 17. The experimental steps Unambiguous identification of resources (e.g. record from public repositories); persistent identifiers if available (ORCIDs, DOIs); dedicated article section Experimental workflows - identification of processes, their inputs and outputs Experimental design: identify experimental goal, independent and response variables
- 18. Reproducing SOAPdenovo2 results with Galaxy workflows S. aureus pipeline
- 19. Reproducing SOAPdenovo2 results with Galaxy workflows S. aureus pipeline
- 20. 2241 400 30 119.0 11 106 24 68 0 Reproducing SOAPdenovo2 results with Galaxy workflows
- 21. Publishing findings as nanopublications assertion provenance publication info nanopublication A NP represents structured data along with its provenance in a single publishable and citable entity
- 22. Publishing findings as nanopublications assertion provenance publication info nanopublication A NP represents structured data along with its provenance in a single publishable and citable entity Abstract & Conclusions assertion provenance Generation of nanopublications for all the results of the response variables NanoMaton templates for nanopublications Prevent priming; report all findings corresponding to the identified response variables Remain neutral and report all findings of similar importance with the same weight
- 23. “genome coverage increased over the human data when comparing SOAPdenovo2 against SOAPdenovo1” Link conclusions to experimental description
- 24. http://www.researchobject.org/ Aggregation and workflow preservation as ResearchObject: enables the aggregation of the digital resources contributing to findings of computational research, including results, data and software, as citable compound digital objects
- 25. http://isa-tools.github.io/soapdenovo2 Aggregation and workflow preservation as http://www.researchobject.org/
- 26. From narrative to self-described structured data Model & workflow assisted experimental description and review process Depth and breadth of semantic resources, clear meaning of experimental elements
- 27. Ruibang Luo, University of Hong Kong Tin-Lap Lee, Chinese University of Hong Kong Tak-wah Lam, University of Hong Kong SOAPdenovo2 Scott Edmunds, GigaScience Peter Li, GigaScience Marco Roos, Leiden University Mark Thompson, Leiden University Rajaram Kaliyaperumal, Leiden University Eelke van der Horst, Leiden University Jun Zhao, Lancaster University María Susana Avila García, Oxford University Philippe Rocca-Serra, Oxford University Susanna-Assunta Sansone, Oxford University Alejandra Gonzalez-Beltran, Oxford University Team
- 28. Questions? You can email us... isatools@googlegroups.com View our blog http://isatools.wordpress.com Follow us onTwitter @isatools View our websites View our Git repo & contribute http://github.com/ISA-tools Thanks for your attention!