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Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Crowdsourcing for bioinformatics.

Benjamin M Good1, Andrew I Su

  • 1Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. bgood@scripps.edu

Bioinformatics (Oxford, England)
|June 21, 2013
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Summary
This summary is machine-generated.

Crowdsourcing harnesses distributed human intelligence for bioinformatics tasks, offering solutions for both large-volume microtasks and complex megatasks. This framework guides effective application of human-powered systems in scientific research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Human-Computer Interaction

Background:

  • Bioinformatics problems often require human intelligence for tasks like genome annotation and protein structure determination.
  • Crowdsourcing, leveraging distributed human intelligence, is an emerging approach in bioinformatics.
  • Existing resources for applying crowdsourcing in scientific domains are limited.

Purpose of the Study:

  • To provide a framework for understanding and applying crowdsourcing in bioinformatics.
  • To categorize crowdsourcing systems based on task volume and difficulty.
  • To guide the effective use of human-powered systems in scientific research.

Main Methods:

  • Categorization of crowdsourcing systems into microtask and megatask solutions.
  • Discussion of system types: volunteer labor, games with a purpose, microtask markets, and open innovation contests.
  • Illustration of system types with successful bioinformatics examples.

Main Results:

  • A framework is presented for understanding and applying crowdsourcing in bioinformatics.
  • Two broad classes of crowdsourcing systems are identified: microtasks and megatasks.
  • Successful examples demonstrate the application of various crowdsourcing types in bioinformatics.

Conclusions:

  • The study offers a guide for matching bioinformatics problems to appropriate crowdsourcing solutions.
  • The framework highlights the advantages and disadvantages of different crowdsourcing approaches.
  • Effective utilization of crowdsourcing can enhance human involvement in complex bioinformatics challenges.