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Nature Papers Present Tool for Topological Analysis of Chromosomes, Map of Genome-Lipid Associations

A method for genome-wide conformation analysis of replicated human chromosomes is reported in Nature this week. Chromosome conformation capture (Hi-C), which maps DNA contacts genome-wide, has enabled the analysis of vertebrate genome organization in three dimensions. However, it currently cannot be used to study topological interactions between the sister chromatids of replicated chromosomes since the identical DNA sequences in replicated chromosomes make it impossible to distinguish between intramolecular and intermolecular contacts. To address this problem, scientists from the Vienna BioCenter developed scsHi-C — short for sister-chromatid-sensitive Hi-C — which involves the labeling of nascent DNA with 4-thiothymidine and nucleoside conversion chemistry. "ScsHi-C provides a versatile tool for investigating this complex topological reorganization, as well as interactions between DNA molecules in other biological contexts, such as pairing and recombination of homologous chromosomes during meiosis," they write.

A large-scale map of genome-lipid associations, used to create a web-based tool for lipid identification, is described by a University of Wisconsin-Madison team in this week's Nature Metabolism. High-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology has enabled the rapid identification of lipids from complex mixtures, yet most of the features of these lipids remain unannotated. To discover the genetics of lipid features obtained through LC–MS/MS, the investigators analyzed liver and plasma from 384 diverse outbred mice and quantified 3,283 molecular features. They then mapped the features to 5,622 lipid quantitative trait loci, which were compiled into a public online resource — dubbed LipidGenie — that cross-references the data to the human genome. "We envision the genome-lipid associations contained within LipidGenie to be a valuable resource for researchers across multiple fields," the researchers write. "We anticipate it will be immediately useful for directed analysis of key unidentified features in exploratory lipidomics analyses and lead to recovery of more data for biological studies.