A model to predict the function of hypothetical proteins through a nine-point classification scoring schema

BMC Bioinformatics

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[1] 0000 0001 1456 3750, grid.412419.b, Department of Biotechnology, Osmania University, 500007, Hyderabad, India;0000 0001 1456 3750, grid.412419.b, Department of Biotechnology, Osmania University, 500007, Hyderabad, India;Bioclues.org, Kukatpally, 500072, Hyderabad, India;0000 0004 0610 6228, grid.469354.9, Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, 302001, Statue Circle, RJ, India;Bioclues.org, Kukatpally, 500072, Hyderabad, India;Bioclues.org, Kukatpally, 500072, Hyderabad, India;0000 0000 9152 1805, grid.412834.8, Department of Microbiology, Bioinformatics Infrastructure Facility, Vidyasagar University, Midnapore, India;Bioclues.org, Kukatpally, 500072, Hyderabad, India;0000 0004 0610 6228, grid.469354.9, Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, 302001, Statue Circle, RJ, India;Bioclues.org, Kukatpally, 500072, Hyderabad, India;Advanced Center for Computational and Applied Biotechnology, Uttarakhand Council for Biotechnology, 248007, Dehradun, India;Department of Pediatrics, The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital, The Ohio State University, Columbus, OH, USA;Bioclues.org, Kukatpally, 500072, Hyderabad, India;Labrynthe, New Delhi, India;

关键词: Hypothetical proteins;    Machine learning;    Classification features;    Functional genomics;   

DOI  :  10.1186/s12859-018-2554-y

来源: publisher

【 摘 要 】

BackgroundHypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.ResultsIn our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67).ConclusionWith the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.

【 授权许可】

CC BY