Biomedical Mutation Analysis (BMA): A software tool for analyzing mutations associated with antiviral resistance.

Abstract

INTRODUCTION:

Hepatitis C virus (HCV) is considered a major public health problem, with 200 million people infected worldwide. The treatment for HCV chronic infection with pegylated interferon alpha plus ribavirin inhibitors is unspecific; consequently, the treatment is effective in only 50% of patients infected. This has prompted the development of direct-acting antivirals (DAA) that target virus proteins. These DAA have demonstrated a potent effect in vitro and in vivo; however, virus mutations associated with the development of resistance have been described.

OBJECTIVE:

To design and develop an online information system for detecting mutations in amino acids known to be implicated in resistance to DAA.

MATERIALS AND METHODS:

   We have used computer applications, technological tools, standard languages, infrastructure systems and algorithms, to analyze positions associated with resistance to DAA for the NS3, NS5A, and NS5B genes of HCV.

RESULTS:

We have designed and developed an online information system named Biomedical Mutation Analysis (BMA), which allows users to calculate changes in nucleotide and amino acid sequences for each selected sequence from conventional Sanger and cloning sequencing using a graphical interface.

CONCLUSION:

BMA quickly, easily and effectively analyzes mutations, including complete documentation and examples. Furthermore, the development of different visualization techniques allows proper interpretation and understanding of the results. The data obtained using BMA will be useful for the assessment and surveillance of HCV resistance to new antivirals, and for the treatment regimens by selecting those DAA to which the virus is not resistant, avoiding unnecessary treatment failures. The software is available at: http://bma.itiud.org.

• Authors: Karina Salvatierra, Hector Florez
• Journal: F1000Research (F1000Res) Vol. 5 Pg. 1141 ( 2016) ISSN: 2046-1402 [Print] England
• PMID: 27547378 (Publication Type: Journal Article)