Single nucleotide polymorphism-based genome-wide chromosome copy change, loss of heterozygosity, and aneuploidy in Barrett's esophagus neoplastic progression.

Chromosome copy gain, loss, and loss of heterozygosity (LOH) involving most chromosomes have been reported in many cancers; however, less is known about chromosome instability in premalignant conditions. 17p LOH and DNA content abnormalities have been previously reported to predict progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA). Here, we evaluated genome-wide chromosomal instability in multiple stages of BE and EA in whole biopsies. Forty-two patients were selected to represent different stages of progression from BE to EA. Whole BE or EA biopsies were minced, and aliquots were processed for flow cytometry and genotyped with a paired constitutive control for each patient using 33,423 single nucleotide polymorphisms (SNP). Copy gains, losses, and LOH increased in frequency and size between early- and late-stage BE (P 30% in early and late stages, respectively. A set of statistically significant events was unique to either early or late, or both, stages, including previously reported and novel abnormalities. The total number of SNP alterations was highly correlated with DNA content aneuploidy and was sensitive and specific to identify patients with concurrent EA (empirical receiver operating characteristic area under the curve = 0.91). With the exception of 9p LOH, most copy gains, losses, and LOH detected in early stages of BE were smaller than those detected in later stages, and few chromosomal events were common in all stages of progression. Measures of chromosomal instability can be quantified in whole biopsies using SNP-based genotyping and have potential to be an integrated platform for cancer risk stratification in BE

Genome-wide Map of Nucleosome Acetylation and Methylation in Yeast

SummaryEukaryotic genomes are packaged into nucleosomes whose position and chemical modification state can profoundly influence regulation of gene expression. We profiled nucleosome modifications across the yeast genome using chromatin immunoprecipitation coupled with DNA microarrays to produce high-resolution genome-wide maps of histone acetylation and methylation. These maps take into account changes in nucleosome occupancy at actively transcribed genes and, in doing so, revise previous assessments of the modifications associated with gene expression. Both acetylation and methylation of histones are associated with transcriptional activity, but the former occurs predominantly at the beginning of genes, whereas the latter can occur throughout transcribed regions. Most notably, specific methylation events are associated with the beginning, middle, and end of actively transcribed genes. These maps provide the foundation for further understanding the roles of chromatin in gene expression and genome maintenance

Computational Analysis of Whole-Genome Differential Allelic Expression Data in Human

Allelic imbalance (AI) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions. Recently, Bing et al. have described the use of genotyping arrays to assay AI at a high resolution (∼750,000 SNPs across the autosomes). In this paper, we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner. We propose two families of approaches: (i) a statistical approach based on z-score computations, and (ii) a family of machine learning approaches based on Hidden Markov Models. Each method is evaluated using previously published experimental data sets as well as with permutation testing. When applied to whole genome data from 53 HapMap samples, our approaches reveal that allelic imbalance is widespread (most expressed genes show evidence of AI in at least one of our 53 samples) and that most AI regions in a given individual are also found in at least a few other individuals. While many AI regions identified in the genome correspond to known protein-coding transcripts, others overlap with recently discovered long non-coding RNAs. We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels, but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end. The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases