Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment
Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment
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DOI：
10.1371/journal.pone.0031362
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发表时间：
2012-03-07
期刊：
影响因子：
3.7
通讯作者：
Waldispuehl, Jerome
中科院分区：
文献类型：
--
作者：
Kawrykow, Alexander;Roumanis, Gary;Waldispuehl, Jerome
背景：比较基因组学，即对不同物种基因组结构和功能关系的研究，为研究进化、注释基因组和了解各种遗传疾病的原因提供了强大的工具。然而，对齐多个 DNA 序列是大多数类型分析的重要中间步骤，也是一项艰巨的计算任务。与此同时，公民科学——一种利用人类大脑巧妙地解决特定类型问题这一事实的方法——正变得越来越受欢迎。在那里，困难计算问题的实例被分派给一群非专家的人类游戏玩家，解决方案被发送回中央服务器。 方法/主要发现：我们引入了 Phylo，一个基于人类的计算框架，应用“众包”技术来解决多序列比对（MSA）问题。 Phylo 的关键思想是将 MSA 问题转化为一款休闲游戏，普通网络用户只需具备最少的生物背景知识即可玩。我们应用这一策略来改善多达 44 种脊椎动物的疾病相关基因启动子的排列。自 2010 年 11 月推出以来，我们收到了 12,000 多名注册用户提交的超过 350,000 个解决方案。我们的结果表明，提交的解决方案有助于提高高达 70% 的所考虑的对齐块的准确性。结论/意义：我们证明，与经典算法相结合，群体计算技术可以成功地用于帮助提高 MSA 的准确性。更重要的是，我们证明了 NP 难计算问题可以嵌入休闲游戏中，无需经过大量科学训练的人就可以轻松玩游戏。这表明公民科学方法可用于利用每天玩游戏所花费的数十亿次“人脑千万亿次浮点运算”。 Phylo 的网址为：http://phylo.cs.mcgill.ca。
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.