Crowdsourcing for Bioinformatics

TL;DR

This paper reviews how crowdsourcing techniques are applied in bioinformatics, categorizing systems for various task types, and provides guidance on selecting appropriate crowdsourcing methods for different scientific problems.

Contribution

It introduces a comprehensive framework for understanding and applying different crowdsourcing approaches in bioinformatics research.

Findings

Different crowdsourcing systems are suited for various bioinformatics tasks.

Successful examples demonstrate the effectiveness of crowdsourcing in bioinformatics.

Guidelines help match bioinformatics problems with suitable crowdsourcing solutions.

Abstract

Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base construction and protein structure determination all benefit from human input. In some cases people are needed in vast quantities while in others we need just a few with very 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.