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BMC Bioinformatics. 2019 Jan 8;20(1):14. doi: 10.1186/s12859-018-2554-y.

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

Author information

1
Department of Biotechnology, Osmania University, Hyderabad, 500007, India.
2
Bioclues.org, Kukatpally, Hyderabad, 500072, India.
3
Department of Pediatrics, The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA.
4
Labrynthe, New Delhi, India.
5
Advanced Center for Computational and Applied Biotechnology, Uttarakhand Council for Biotechnology, Dehradun, 248007, India.
6
Department of Microbiology, Bioinformatics Infrastructure Facility, Vidyasagar University, Midnapore, India.
7
Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, RJ, 302001, India.
8
Bioclues.org, Kukatpally, Hyderabad, 500072, India. chanusuba@gmail.com.
9
Bioclues.org, Kukatpally, Hyderabad, 500072, India. prash@bisr.res.in.
10
Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, RJ, 302001, India. prash@bisr.res.in.

Abstract

BACKGROUND:

Hypothetical 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.

RESULTS:

In 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).

CONCLUSION:

With 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.

KEYWORDS:

Classification features; Functional genomics; Hypothetical proteins; Machine learning

PMID:
30621574
PMCID:
PMC6325861
DOI:
10.1186/s12859-018-2554-y
[Indexed for MEDLINE]
Free PMC Article

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