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A Survey of Computational Methods for Protein Function Prediction

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Big Data Analytics in Genomics

Abstract

Rapid advances in high-throughout genome sequencing technologies have resulted in millions of protein-encoding gene sequences with no functional characterization. Automated protein function annotation or prediction is a prime problem for computational methods to tackle in the post-genomic era of big molecular data. While recent community-driven experiments demonstrate that the accuracy of function prediction methods has significantly improved, challenges remain. The latter are related to the different sources of data exploited to predict function, as well as different choices in representing and integrating heterogeneous data. Current methods predict function from a protein’s sequence, often in the context of evolutionary relationships, from a protein’s three-dimensional structure or specific patterns in the structure, from neighbors in a protein–protein interaction network, from microarray data, or a combination of these different types of data. Here we review these methods and the state of protein function prediction, emphasizing recent algorithmic developments, remaining challenges, and prospects for future research.

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Acknowledgements

Funding for this work is provided in part by NSF-IIS1144106.

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Authors and Affiliations

  1. Department of Computer Science, Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA

    Amarda Shehu

  2. Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

    Daniel Barbará

  3. LAAS-CNRS, 7, avenue du Colonel Roche, 31077, Toulouse, France

    Kevin Molloy

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  1. Amarda Shehu
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  2. Daniel Barbará
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  3. Kevin Molloy
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Correspondence to Amarda Shehu .

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  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Shehu, A., Barbará, D., Molloy, K. (2016). A Survey of Computational Methods for Protein Function Prediction. In: Wong, KC. (eds) Big Data Analytics in Genomics. Springer, Cham. https://doi.org/10.1007/978-3-319-41279-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-41279-5_7

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