Detecting Hepatitis B Viral Amino Acid Sequence Mutations in Occult Hepatitis B Infections via Bayesian Partition Model
- *Corresponding Author:
- Jing Zhang
Program of Computational Biology and Bioinformatics
Yale University, New Haven, CT06511, USA
E-mail: [email protected]
Received date: April 29, 2013; Accepted date: June 07, 2013; Published date: June 12, 2013
Citation: Lian Z, Tian QN, Liu Y, Cento V, Salpini R, et al. (2013) Detecting Hepatitis B Viral Amino Acid Sequence Mutations in Occult Hepatitis B Infections via Bayesian Partition Model. J Proteomics Bioinform S6:005. doi:10.4172/jpb. S6-005
Copyright: © 2013 Lian Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: With advancements in technology, a number of Hepatitis B virus (HBV) infections, where viral DNA is present in the liver or plasma, without the concomitant detection of HBsAg in plasma have been reported, and have been termed occult Hepatitis B infections (OBI). Unfortunately, the etiology and pathogenesis of OBI remain elusive to date, and the genetic characteristics of HBV sequence that lead to the development of OBI are still poorly understood.
Methods: 358 genotype-C (330 chronically infected patients and 28 occult infected patients) and 107 genotype-D (83 chronically infected patients and 24 occult infected patients) HBV Reverse Transcriptase (RT) amino acid sequences were collected. In addition to greedy search, a novel statistical approach, Bayesian Variable Partition Model is applied to pinpoint those positions, where amino acid mutations collaboratively discriminate OBI samples from chronically infected samples, in genotype-C and genotype-D, respectively.
Results: Several discriminate and correlated positions were found in genotype-C (high-order position combinations listed in tables) and genotype-D (positions 126+138, 129+131 and 138+139) respectively. By comparing amino acid distributions in these positions between genotype-C and genotype-D, six position combinations were reported to have obvious different amino acid distributions in these two HBV genotypes.
Conclusions: This paper furthers the understanding of the correlation between HBV sequence mutations and the differences of OBI in two HBV genotypes, by studying mutations in HBV RT amino acid sequences. Different from other traditional methods, the Bayesian-based method is able to analyze high-order combinations of positions.