Comparative epigenomics by machine learning approach for neuroblastoma.

BACKGROUND: Neuroblastoma (NB) is the second most common pediatric solid tumor. Because the number of genetic mutations found in tumors are small, even in some patients with unfavorable NB, epigenetic variation is expected to play an important role in NB progression. DNA methylation is a major epigenetic mechanism, and its relationship with NB prognosis has been a concern. One limitation with the analysis of variation in DNA methylation is the lack of a suitable analytical model. Therefore, in this study, we performed a random forest (RF) analysis of the DNA methylome data of NB from multiple databases. RESULTS: RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature selection analysis based on probe annotation revealed that enhancer-annotated regions had strong predictive power, particularly for MYCN-amplified NBs. We developed a gene-based analytical model to identify candidate genes related to disease progression, such as PRDM8 and FAM13A-AS1. RF analysis revealed sufficient predictive power compared to other machine learning models. CONCLUSIONS: RF is a useful tool for DNA methylome analysis in cancer epigenetic studies, and has potential to identify a novel cancer-related genes.

The Rate and Tract Length of Gene Conversion between Duplicated Genes.

Interlocus gene conversion occurs such that a certain length of DNA fragment is non-reciprocally transferred (copied and pasted) between paralogous regions. To understand the rate and tract length of gene conversion, there are two major approaches. One is based on mutation-accumulation experiments, and the other uses natural DNA sequence variation. In this review, we overview the two major approaches and discuss their advantages and disadvantages. In addition, to demonstrate the importance of statistical analysis of empirical and evolutionary data for estimating tract length, we apply a maximum likelihood method to several data sets.

The power of the methods for detecting interlocus gene conversion.

Interlocus gene conversion can homogenize DNA sequences of duplicated regions with high homology. Such nonvertical events sometimes cause a misleading evolutionary interpretation of data when the effect of gene conversion is ignored. To avoid this problem, it is crucial to test the data for the presence of gene conversion. Here, we performed extensive simulations to compare four major methods to detect gene conversion. One might expect that the power increases with increase of the gene conversion rate. However, we found this is true for only two methods. For the other two, limited power is expected when gene conversion is too frequent. We suggest using multiple methods to minimize the chance of missing the footprint of gene conversion.