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Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment

Author

Listed:
  • Alexander Kawrykow
  • Gary Roumanis
  • Alfred Kam
  • Daniel Kwak
  • Clarence Leung
  • Chu Wu
  • Eleyine Zarour
  • Phylo players
  • Luis Sarmenta
  • Mathieu Blanchette
  • Jérôme Waldispühl

Abstract

Background: Comparative genomics, or the study of the relationships of genome structure and function across different species, offers a powerful tool for studying evolution, annotating genomes, and understanding the causes of various genetic disorders. However, aligning multiple sequences of DNA, an essential intermediate step for most types of analyses, is a difficult computational task. In parallel, citizen science, an approach that takes advantage of the fact that the human brain is exquisitely tuned to solving specific types of problems, is becoming increasingly popular. There, instances of hard computational problems are dispatched to a crowd of non-expert human game players and solutions are sent back to a central server. Methodology/Principal Findings: We introduce Phylo, a human-based computing framework applying “crowd sourcing” techniques to solve the Multiple Sequence Alignment (MSA) problem. The key idea of Phylo is to convert the MSA problem into a casual game that can be played by ordinary web users with a minimal prior knowledge of the biological context. We applied this strategy to improve the alignment of the promoters of disease-related genes from up to 44 vertebrate species. Since the launch in November 2010, we received more than 350,000 solutions submitted from more than 12,000 registered users. Our results show that solutions submitted contributed to improving the accuracy of up to 70% of the alignment blocks considered. Conclusions/Significance: We demonstrate that, combined with classical algorithms, crowd computing techniques can be successfully used to help improving the accuracy of MSA. More importantly, we show that an NP-hard computational problem can be embedded in casual game that can be easily played by people without significant scientific training. This suggests that citizen science approaches can be used to exploit the billions of “human-brain peta-flops” of computation that are spent every day playing games. Phylo is available at: http://phylo.cs.mcgill.ca.

Suggested Citation

  • Alexander Kawrykow & Gary Roumanis & Alfred Kam & Daniel Kwak & Clarence Leung & Chu Wu & Eleyine Zarour & Phylo players & Luis Sarmenta & Mathieu Blanchette & Jérôme Waldispühl, 2012. "Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    • Handle: RePEc:plo:pone00:0031362
    DOI: 10.1371/journal.pone.0031362
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    References listed on IDEAS

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    1. Cédric Notredame, 2007. "Recent Evolutions of Multiple Sequence Alignment Algorithms," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-4, August.
    2. Manolis Kellis & Nick Patterson & Matthew Endrizzi & Bruce Birren & Eric S. Lander, 2003. "Sequencing and comparison of yeast species to identify genes and regulatory elements," Nature, Nature, vol. 423(6937), pages 241-254, May.
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    1. Matthew Staffelbach & Peter Sempolinski & Tracy Kijewski-Correa & Douglas Thain & Daniel Wei & Ahsan Kareem & Gregory Madey, 2015. "Lessons Learned from Crowdsourcing Complex Engineering Tasks," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-19, September.
    2. Naihui Zhou & Zachary D Siegel & Scott Zarecor & Nigel Lee & Darwin A Campbell & Carson M Andorf & Dan Nettleton & Carolyn J Lawrence-Dill & Baskar Ganapathysubramanian & Jonathan W Kelly & Iddo Fried, 2018. "Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-16, July.
    3. Barbara Strobl & Simon Etter & Ilja van Meerveld & Jan Seibert, 2019. "The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-23, September.
    4. Andrei P. Kirilenko & Travis Desell & Hany Kim & Svetlana Stepchenkova, 2017. "Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. Volunteers," Sustainability, MDPI, vol. 9(11), pages 1-15, November.

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