BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The genomes of many animals, plants and fungi are tagged by methylation of DNA cytosine. To understand the biological significance of this epigenetic mark it is essential to know where in the genome it is located. New techniques are making it easier to map DNA methylation patterns on a large scale and the results have already provided surprises. In particular, the conventional view that DNA methylation functions predominantly to irreversibly silence transcription is being challenged. Not only is promoter methylation often highly dynamic during development, but many organisms also seem to target DNA methylation specifically to the bodies of active genes.

https://birdlab.bio.ed.ac.uk/bird/sites/sbsweb2.bio.ed.ac.uk.bird/files/20.pdf

DNA methylation landscapes: provocative insights from epigenomics

To gain insight into the function of DNA methylation at cis-regulatory regions and its impact on gene expression, we measured methylation, RNA polymerase occupancy and histone modifications at 16,000 promoters in primary human somatic and germline cells. We find CpG-poor promoters hypermethylated in somatic cells, which does not preclude their activity. This methylation is present in male gametes and results in evolutionary loss of CpG dinucleotides, as measured by divergence between humans and primates. In contrast, strong CpG island promoters are mostly unmethylated, even when inactive. Weak CpG island promoters are distinct, as they are preferential targets for de novo methylation in somatic cells. Notably, most germline-specific genes are methylated in somatic cells, suggesting additional functional selection. These results show that promoter sequence and gene function are major predictors of promoter methylation states. Moreover, we observe that inactive unmethylated CpG island promoters show elevated levels of dimethylation of Lys4 of histone H3, suggesting that this chromatin mark may protect DNA from methylation.

Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome.

http://www.uniroma2.it/didattica/Genomica_BTI/deposito/Mamm_Epigenome_rev_Cell.pdf

The mammalian epigenome.

Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis.

The mammalian genome depends on patterns of methylated cytosines for normal function, but the relationship between genomic methylation patterns and the underlying sequence is unclear. We have characterized the methylation landscape of the human genome by global analysis of patterns of CpG depletion and by direct sequencing of 3073 unmethylated domains and 2565 methylated domains from human brain DNA. The genome was found to consist of short (<4 kb) unmethylated domains embedded in a matrix of long methylated domains. Unmethylated domains were enriched in promoters, CpG islands, and first exons, while methylated domains comprised interspersed and tandem-repeated sequences, exons other than first exons, and non-annotated single-copy sequences that are depleted in the CpG dinucleotide. The enrichment of regulatory sequences in the relatively small unmethylated compartment suggests that cytosine methylation constrains the effective size of the genome through the selective exposure of regulatory sequences. This buffers regulatory networks against changes in total genome size and provides an explanation for the C value paradox, which concerns the wide variations in genome size that scale independently of gene number. This suggestion is compatible with the finding that cytosine methylation is universal among large-genome eukaryotes, while many eukaryotes with genome sizes <5 x 10(8) bp do not methylate their DNA.

/pdf/large-scale-structure-of-genomic-methylation-patterns-50iaicqnlf.pdf

Large-scale structure of genomic methylation patterns

Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called hdfinder) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using hdfinder, we have depicted the entire genomic methylation patterns for all 22 human autosomes.

/pdf/computational-prediction-of-methylation-status-in-human-2s1xtznyma.pdf

Rajdeep Das

Papers

Large-scale structure of genomic methylation patterns

Computational prediction of methylation status in human genomic sequences