Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method.

Faegheh Golabi,Abolfazl Barzegar,Mousa Shamsi,Mohammad Hosein Sedaaghi,Mohammad Saeid Hejazi

doi:10.15171/apb.2020.012

Abstract

Purpose: Riboswitches are special non-coding sequences usually located in mRNAs’ un-translated regions and regulate gene expression and consequently cellular function. Furthermore, their interaction with antibiotics has been recently implicated. This raises more interest in development of bioinformatics tools for riboswitch studies. Herein, we describe the development and employment of novel block location-based feature extraction (BLBFE) method for classification of riboswitches. Methods: We have already developed and reported a sequential block finding (SBF) algorithm which, without operating alignment methods, identifies family specific sequential blocks for riboswitch families. Herein, we employed this algorithm for 7 riboswitch families including lysine, cobalamin, glycine, SAM-alpha, SAM-IV, cyclic-di-GMP-I and SAH. Then the study was extended toward implementation of BLBFE method for feature extraction. The outcome features were applied in various classifiers including linear discriminant analysis (LDA), probabilistic neural network (PNN), decision tree and k-nearest neighbors (KNN) classifiers for classification of the riboswitch families. The performance of the classifiers was investigated according to performance measures such as correct classification rate (CCR), accuracy, sensitivity, specificity and f-score. Results: As a result, average CCR for classification of riboswitches was 87.87%. Furthermore, application of BLBFE method in 4 classifiers displayed average accuracies of 93.98% to 96.1%, average sensitivities of 76.76% to 83.61%, average specificities of 96.53% to 97.69% and average f-scores of 74.9% to 81.91%. Conclusion: Our results approved that the proposed method of feature extraction; i.e. BLBFE method; can be successfully used for classification and discrimination of the riboswitch families with high CCR, accuracy, sensitivity, specificity and f-score values.

Highlights

Regulation of cellular functions are achieved by effective collaboration of varying types of bio-molecules such as DNAs, RNAs and proteins
Following development of sequential block finding (SBF) algorithm for detection of frequently appearing family specific sequential blocks in riboswitch families, in this paper we first elucidated the performance of the designed algorithm
We proposed a new feature extraction strategy called block location-based feature extraction (BLBFE), which employs the locations of the specified blocks on riboswitch sequences as features

Summary

Introduction

Regulation of cellular functions are achieved by effective collaboration of varying types of bio-molecules such as DNAs, RNAs and proteins. Riboswitches[1,2,3,4] as an example of regulatory RNAs, are a part of mRNA molecules and regulate the expression of corresponding genes by directly binding to the target metabolites and undergoing consequent structural changes.[5,6,7] For instance, the riboswitch structural conformation alteration blocks the ribosome binding site and inhibits protein synthesis by the ribosome. Riboswitches are usually located in mRNAs’ 5’ un-translated regions.[3] Riboswitches with similar sequence and secondary and tertiary structures perform similar tasks.[8,9] riboswitches are categorized to families according to their function, sequence conservation and structural similarities.[10,11]

Methods

Results

Conclusion