In the study of molecular and phenotypic evolution, understanding the relative importance of random genetic drift and positive selection as the mechanisms for driving divergences between populations and maintaining polymorphisms within populations has been a central issue. A variety of statistical methods has been developed for detecting natural selection operating at the amino acid and nucleotide sequence levels. These methods may be largely classified into those aimed at detecting recurrent and/or recent/ongoing natural selection by utilizing the divergence and/or polymorphism data. Using these methods, pervasive positive selection has been identified for protein-coding and non-coding sequences in the genomic analysis of some organisms. However, many of these methods have been criticized by using computer simulation and real data analysis to produce excessive false-positives and to be sensitive to various disturbing factors. Importantly, some of these methods have been invalidated experimentally. These facts indicate that many of the statistical methods for detecting natural selection are unreliable. In addition, the signals that have been believed as the evidence for fixations of advantageous mutations due to positive selection may also be interpreted as the evidence for fixations of deleterious mutations due to random genetic drift. The genomic diversity data are rapidly accumulating in various organisms, and detection of natural selection may play a critical role for clarifying the relative role of random genetic drift and positive selection in molecular and phenotypic evolution. It is therefore important to develop reliable statistical methods that are unbiased as well as robust against various disturbing factors, for inferring natural selection.

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Onion, Allium cepa, is a model plant for experimental observation of somatic cell division, whose mitotic chromosome is extremely large, and contains the characteristic terminal heterochromatin. Epigenetic status of the onion chromosome is a matter of deep interest from a molecular cytogenetic point of view, because epigenetic marks regulate chromatin structure and gene expression. Here we examined chromosomal distribution of DNA methylation and histone modification in A. cepa in order to reveal the chromatin structure in detail. Immunodetection of 5-methylcytosine (5mC) and in situ nick-translation analysis showed that onion genomic DNA was highly methylated, and the methylated CG dinucleotides were distributed in entire chromosomes. In addition, distributions of histone methylation codes, which occur in close association with DNA methylation, were similar to those of other large genome species. From these results, a highly heterochromatic and less euchromatic state of large onion chromosomes were demonstrated at an epigenetic level.

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Alternative splicing is a main component of protein diversity, and aberrant splicing is known to be one of the main causes of genetic disorders such as cancer. Many statistical and computational approaches have identified several major factors that determine the splicing event, such as exon/intron length, splice site strength, and density of splicing enhancers or silencers. These factors may be correlated with one another and thus result in a specific type of splicing, but there has not been a systematic approach to extracting comprehensible association patterns. Here, we attempted to understand the decision making process of the learning machine on intron retention event. We adopted a hybrid learning machine approach using a random forest and association rule mining algorithm to determine the governing factors of intron retention events and their combined effect on decision-making processes. By quantifying all candidate features into five category values, we enhanced the understandability of generated rules. The interesting features found by the random forest algorithm are that only the adenine- and thymine-based triplets such as ATA, TTA, and ATT, but not the known intronic splicing enhancer GGG triplet is shown the significant features. The rules generated by the association rule mining algorithm also show that constitutive introns are generally characterized by high adenine- and thymine-based triplet frequency (level 3 and above), 3' and 5' splice site scores, exonic splicing silencer scores, and intron length, whereas retained introns are characterized by low-level counterpart scores.

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