Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment

Abstract

BackgroundComparative 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 FindingsWe 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/SignificanceWe 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.

Crowdsourcing—Harnessing the Masses to Advance Health and Medicine, a Systematic Review

“…Although we identified 21 articles, most of which used crowdsourcing successfully, crowdsourcing clearly is not used pervasively in health research, and it is important to understand the quality of data it provides. Even though the use of crowdsourcing in health research is in its infancy, the "NP" indicates that data were collected but not published "NP" indicates that data were collected but not published papers we identified successfully used crowdsourcing to solve protein structure problems, 18 improve alignment of promoter sequences, 23 track H1N1 influenza outbreaks in near real time, 14 classify colonic polyps, 27,28 and identify RBCs infected with Plasmodium falciparum parasites. [24][25][26] Furthermore, as Mavandadi et al point out, one way around the problem of involving lay people in making a medical diagnosis is to use crowdsourcing to distill the data for a medical professional, who can then make the final decision.…”

“…12,13,[17][18][19]21 Also described was the online game Phylo, where users moved colored blocks representing different nucleotides of a gene promoter sequence around on screen in order to make the most parsimonious phylogenetic tree. 23 …”

Is Virtual Citizen Science A Game?

“…The smartphone age has given rise to the opportunity for "participatory sensing", allowing volunteers to record all types of phenomena using their mobile device [Restuccia et al 2016]. Relying on the ability of the human brain to complete tasks such as the analysis of images is quicker than even the most powerful of supercomputers [Kawrykow et al 2012]. Similar to SETI@home, VCS projects allow people to contribute without being a professional scientist.…”

Entomological citizen science in Canada

“…For many, the tasks appear to be simple busywork, motivated by the idea that there will ultimately be some benefit to science at a higher level. However, Kowrykow et al (2012) refer to their participants as "game players", and perhaps this phrase also characterises those who enjoy transcribing label data. Thus, it makes sense that citizen science participants do not necessarily learn much about science, or natural history, in the process (Druschke and Seltzer 2012), despite early evidence that eBird participants do engage in scientific habits of thought (Trumbull et al 2000).…”