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Accurate annotation of protein function is vital to understanding life at the molecular level, but automated annotation of functions is challenging. We here demonstrate the mix of a way for protein function annotation that uses network information to predict the biological processes a protein is involved in, with a sequence-based prediction method. The combined function prediction relies on co-expression networks and combines the network-based prediction method BMRF with the sequence-based prediction method Argot2. the mixture shows significantly improved performance compared to every of the methods separately, additionally as compared to BlastGO. The approach was applied to predict biological processes for the proteomes of rice, barrel clover, poplar, soybean, and tomato. The novel function predictions are available at www.ab.wur.nl/bmrf. Analysis of the relationships between sequence similarity and predicted function similarity identifies numerous cases of divergence of biological processes within which proteins are involved, despite sequence similarity. this means that the combination of network-based and sequence-based function prediction is useful towards the analysis of evolutionary relationships. samples of potential divergence are identified for various biological processes, notably for processes associated with cell development, regulation, and response to a chemical stimulus. Such divergence in organic process annotation for proteins with similar sequences should be taken into consideration when analyzing plant gene and genome evolution.
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