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Data Mining Techniques for the Life Sciences pp 153–176Cite as

Homology-Based Annotation of Large Protein Datasets

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

Abstract

Advances in DNA sequencing technologies have led to an increasing amount of protein sequence data being generated. Only a small fraction of this protein sequence data will have experimental annotation associated with them. Here, we describe a protocol for in silico homology-based annotation of large protein datasets that makes extensive use of manually curated collections of protein families. We focus on annotations provided by the Pfam database and suggest ways to identify family outliers and family variations. This protocol may be useful to people who are new to protein data analysis, or who are unfamiliar with the current computational tools that are available.

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Acknowledgements

The authors would like to thank Stijn van Dongen (European Bioinformatics Institute) for some important clarifications concerning the clustering method MCL.

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Authors and Affiliations

  1. Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de l’Ecole deMédecine, Paris, France

    Marco Punta

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

    Jaina Mistry

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  1. Marco Punta
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  2. Jaina Mistry
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Correspondence to Marco Punta .

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Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Punta, M., Mistry, J. (2016). Homology-Based Annotation of Large Protein Datasets. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_8

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3570-3

  • Online ISBN: 978-1-4939-3572-7

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