Risks and rewards of big-data in epigenomics research: an interview with Melanie Ehrlich

The landscape of histone modifications in epigenomics since 2020

Histone proteins are a primary component of chromatin; therefore, any modifications to their structure are anticipated to affect the behavior of our genetic material, which is manifested in the form of phenotypic changes at a molecular, cellular or organic level. The majority of histone modifications are of either methylation or acetylation type that regulate gene expression. Though, not all of these modifications are concerned with the direct regulation of gene transcription. Throughout its 13-year run, Epigenomics has never ceased to cover these most gripping epigenetic stories, a significant proportion of which is in the matter of histones and their modifications. As such, the current perspective piece is intended to highlight original histone-oriented contributions published in Epigenomics since 2020.

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.