Abstract
Completely sequenced genomes and other types of genomics data provide us with new information to predict protein function. While classical, homology-based function prediction provides information about a proteins’ molecular function (what does the protein do at a molecular scale?), the analysis of the sequence in the context of its genome or in other types of genomics data provides information about its functional context (what are the proteins’ interaction partners, and in which biological process does it play a role?) Genomic context data are however inherently noisy. Only by combining different types of genomic(s) context data (vertical comparative genomics) or by combining the same type of genomics data from different species (horizontal comparative genomics) do they become sufficiently reliable to be used for protein function prediction. Homology-based function prediction and context-based function prediction provide complementary information about a protein’s function and can becombined to make predictions that are specific enough for experimental testing. Here we discuss the genomic coverage and reliability of combining genomics data for protein function prediction and survey predictions that have actually led to experimental confirmation. Using a number of examples we illustrate how combining the information from various types of genomics data can lead to specific protein function predictions. These include the prediction that the Ribonuclease L inhibitor (RLI) is involved in the maturation of ribosomal RNA.
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Huynen, M.A., Snel, B., Gabaldón, T. (2006). Reliable and Specific Protein Function Prediction by Combining Homology with Genomic(s) Context. In: Discovering Biomolecular Mechanisms with Computational Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-36747-0_2
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