title: Crowdsourcing for bioinformatics.
abstract: BACKGROUND
  Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains.
  
  
  RESULTS
  Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches.
authors.affiliation: Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. bgood@scripps.edu
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Good
authors.forename: Andrew I
authors.initials: AI
authors.lastname: Su
mesh_terms: D019295:Computational Biology; D063045:Crowdsourcing; D005717:Games, Experimental; D016415:Sequence Alignment; D014838:Volunteers