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The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment

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Data Mining Techniques for the Life Sciences

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

Abstract

As biomolecular sequencing is becoming the main technique in life sciences, functional interpretation of sequences in terms of biomolecular mechanisms with in silico approaches is getting increasingly significant. Function prediction tools are most powerful for protein-coding sequences; yet, the concepts and technologies used for this purpose are not well reflected in bioinformatics textbooks. Notably, protein sequences typically consist of globular domains and non-globular segments. The two types of regions require cardinally different approaches for function prediction. Whereas the former are classic targets for homology-inspired function transfer based on remnant, yet statistically significant sequence similarity to other, characterized sequences, the latter type of regions are characterized by compositional bias or simple, repetitive patterns and require lexical analysis and/or empirical sequence pattern–function correlations. The recipe for function prediction recommends first to find all types of non-globular segments and, then, to subject the remaining query sequence to sequence similarity searches. We provide an updated description of the ANNOTATOR software environment as an advanced example of a software platform that facilitates protein sequence-based function prediction.

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Authors and Affiliations

  1. Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*Star), 30 Biopolis Street #07-01, Matrix Building, Singapore, 138671, Singapore

    Birgit Eisenhaber, Durga Kuchibhatla, Westley Sherman, Fernanda L. Sirota, Igor N. Berezovsky, Wing-Cheong Wong & Frank Eisenhaber

  2. Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore, 117597, Singapore

    Igor N. Berezovsky & Frank Eisenhaber

  3. School of Computer Engineering (SCE), Nanyang Technological University (NTU), 50 Nanyang Drive, Singapore, 637553, Singapore

    Frank Eisenhaber

Authors
  1. Birgit Eisenhaber
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  2. Durga Kuchibhatla
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  3. Westley Sherman
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  4. Fernanda L. Sirota
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  5. Igor N. Berezovsky
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  6. Wing-Cheong Wong
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  7. Frank Eisenhaber
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Corresponding author

Correspondence to Frank Eisenhaber .

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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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Eisenhaber, B. et al. (2016). The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment. 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_25

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_25

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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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