Methods: Twenty-five infants were recruited and classified as good and non-/low-responders according to serological test results. Whole genome DNA methylation states were profiled by Illumina HumanMethylation 450 K beadchips. Data were processed through quality and dispersion filtering and with differential methylation analysis based on a combination of average methylation differences and non-parametric statistical tests. Results were finally associated to already published transcriptomics and post-transcriptomics to gain further insight.
Background: Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data. We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).
Exploring the molecular causes of hepatitis B virus vaccination response: an approach with epigenomic and transcriptomic data
Nardini Christine
2014
Abstract
Background: Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data. We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
