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Consortium biology in immunology: the perspective from the Immunological Genome Project

A Corrigendum to this article was published on 04 July 2014

This article has been updated

Abstract

Although the field has a long collaborative tradition, immunology has made less use than genetics of 'consortium biology', wherein groups of investigators together tackle large integrated questions or problems. However, immunology is naturally suited to large-scale integrative and systems-level approaches, owing to the multicellular and adaptive nature of the cells it encompasses. Here, we discuss the value and drawbacks of this organization of research, in the context of the long-running 'big science' debate, and consider the opportunities that may exist for the immunology community. We position this analysis in light of our own experience, both positive and negative, as participants of the Immunological Genome Project.

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  • 04 July 2014

    In the version of this Perspectives article that was originally published, the surname of an author listed as a member of the Immunological Genome Project was misspelt. The correct spelling is Cipolletta (and not Cipoletta, as in the original). The authors apologize for this error, which has been corrected in the online HTML and PDF versions of the article.

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Acknowledgements

We thank R. Germain, B. Malissen and the reviewers for comments and suggestions. The ImmGen programme is supported by grant R24-AI072073 from the US National Institute of Allergy and Infectious Diseases, National Institutes of Health, and is grateful to eBioscience, Affymetrix and Expression Analysis for sponsorship.

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Correspondence to Christophe Benoist or Diane Mathis.

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The Immunological Genome Project: Jeff Ericson, Michio Painter, Scott Davis, Catherine Laplace, Gordon Hyatt, Henry Paik, Katie Rothamel, Richard Cruse, Graeme Doran, Tracy Heng, Natasha Asinovski, Adriana Ortiz-Lopes, Ayla Ergun, Daniel Gray, Ei Wakamatsu, Jonathan Hill, Michael Mingueneau, Daniela Cipolletta, Hideyuki Yoshida, Christophe Benoist, Diane Mathis, Nadia Cohen, Edy Kim, Patrick Brennan, Lydia Lynch, Michael Brenner, James Costello, Jim J. Collins, David Blair, Michael Dustin, Jamie Knell, Edward Yang, Adam Best, Laura Shaw, Andrew Doedens, Ananda Goldrath, Susan Shinton, Yan Zhou, Randy Hardy, Vladimir Jojic, Sara Mostafavi, Daphne Koller, Radu Jianu, David Laidlaw, Natalie Bezman, Joseph Sun, Yanan Zhu, Deborah Hendricks, Yosuke Kamimura, Gundula Min-Oo, Deborah Hendricks, Maelig Morvan, Yosuke Kamimura, Tsukasa Nabekura, Viola Lam, Charles Kim, Lewis Lanier, Melanie Greter, Julie Helft, Andrew Chow, Milena Bogunovic, Arthur Mortha, Jeremy Price, Daigo Hashimoto, Jennifer Miller, Priyanka Sathe, Aleksey Chudnovskiy, Yonit Lavin, Juliana Idoyaga, Miriam Merad, Emmanuel Gautier, Claudia Jakubzick, June D'Angelo, Gwendolyn Randolph, Tal Shay, Aviv Regev, Roi Gazit, Derrick Rossi, Taras Kreslawsky, Harald von Bohmer, Angelique Bellemare-Pelletier, Kutlu Elpek, Lotte Spelv, Anne Fletcher, Deepali Malhotra, Viviana Cremasco, Shannon Turley, Francis Kim, Tata Nageswara Rao & Amy Wagers

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FURTHER INFORMATION

Biodefense and Emerging Infections Research Resources Repository

Centre for Modelling Human Disease

Euroflow

FOCIS Human ImmunoPhenotypong Consortium

Human Immunology Project Consortium

Human Leucocyte Differentiation Antigens (HLDA) Workshops

Immune Epitope Database

Immunological Genome Project

International Histocompatibility Working Group

International Histocompatibility Workshops

Mutagenetix

NIH Tetramer Core Facility

Glossary

Big science

Scientific research that involves larger instruments or groups of scientists than that more commonly practised in individual laboratories.

Crowdsourcing

A process in which a task is performed, typically in small subfractions, by a large group of people who are a priori undefined and not affiliated to the initiating entity.

Glycomes and lipidomes

The complete sets of polysaccharides (free or complexed) and lipids expressed in a cell or organism.

Proteome

By analogy to the genome (the complete set of genes), the proteome is the complete set of proteins expressed in a cell or organism, and their post-translational modifications.

Reverse engineering

The process of discovering the operational principles of a device or system of unknown structure through analyses of its function and operation. In the analysis of genetic regulatory networks, one starts from the end result of the regulatory network (a large number of measures of gene expression in different cells, with or without perturbation) and computationally infers which regulatory inputs can generate these results. It often involves taking a system apart and analysing its workings with the aim of making a new device or programme that does the same thing without using any physical part of the original.

Text mining

Deriving information from computational analyses of patterns in texts. In biology, text mining refers to discovering relationships between biological objects from the patterns of cooccurrence in abstracts or texts of published articles.

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Benoist, C., Lanier, L., Merad, M. et al. Consortium biology in immunology: the perspective from the Immunological Genome Project. Nat Rev Immunol 12, 734–740 (2012). https://doi.org/10.1038/nri3300

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