With the development of genome sequencing technologies, the amount of data produced has greatly increased in the last two decades. The abundance of digital sequence information (DSI) has provided research opportunities, improved our understanding of the genome, and led to the discovery of new solutions in industry and medicine. It has also posed certain challenges, i.e., how to store and handle such amounts of data. This, coupled with the need for convenience, international cooperation, and the possibility of independent validation, has led to the establishment of numerous databases. Spearheaded with the idea that data obtained with public funds should be available to the public, open access has become the predominant mode of accession. However, the increasing popularity of commercial genetic tests brings back the topic of data misuse, and patient’s privacy. At the previous United Nations Biodiversity Conference (COP15, 2022), an issue of the least-developed countries exploiting their natural resources while providing DSI and the most-developed countries benefitting from this was raised. It has been proposed that financial renumeration for the data could help protect biodiversity. With the goal of introducing the topic to those interested in utilizing biological databases, in this publication, we present the history behind the biological databases, their necessity in today’s scientific world, and the issues that concern them and their content, while providing scientific and policy context in relation to United Nations Biodiversity Conference (COP16, 21.10—1.11.24).

Paleogenetics has significantly changed since its inception almost forty years ago. Initially, molecular techniques available to the researchers offered minimal possibilities for ancient DNA analysis. The subsequent expansion of the scientific tool cabinet allowed for more remarkable achievements, combined has with the newfound popularity of this budding field of science. Finally, a breakthrough was made with the development of next-generation sequencing (NGS) technologies and the update of DNA isolation protocols, through which even very fragmented aDNA samples could be used to sequence whole genomes. In this paper, we review the achievements made thus far and compare the methodologies utilized in this field of science, discussing their benefits and challenges.

Background: DNA methylation mediates the gene–environment interactions, with implications for health and disease. Studies with sampling at more than one timepoint revealed the considerable variability of the blood methylome, but comprehensive resources on genome-wide methylation stability are still lacking. We aimed to identify methylation sites that remain the most stable across two timepoints in human whole blood. Methods: Publicly available blood DNA methylation data from three cohorts were analysed, which included methylation profiles at two timepoints >1 year apart. The cohorts included pre-/post-pubertal children (Illumina 450k array), the elderly (Illumina 450k array), and middle-aged adults with obesity (Illumina EPIC array). Two metrics were used for the stability assessment: the mean absolute difference (MAD) of beta values between two measurements and the intraclass correlation coefficient (ICC). We searched for probes demonstrating high stability (low MAD and high ICC) across the three cohorts. Data from 51 children, 86 elderly adults, and 120 middle-aged participants were re-analysed. Results: The median interquartile range (IQR) of the maximum (from three datasets) MAD was 2.1% (1.5–2.9%), and the median of the minimum ICC agreement coefficient was 0.053 (−0.077–0.304). The Pearson’s correlation coefficient for the ICC vs. maximum MAD was low (r = 0.34, p < 2.2 × 10⁻¹⁶). We found only 239 probes that were highly stable based on both the maximum MAD (<5th percentile, 95th percentile, >0.74). Conclusions: The whole-blood DNA methylation profile, as measured using microarrays, is dynamic over >1 year, but contains a fraction of stable probes, most of which are related to genomic variation. A resource describing probe stability is made publicly available, with the intention to support biomarker studies and the investigation of early epigenetic programming. The absolute error and correlation are two complementary facets of probe stability that may be considered in further research, especially to determine the stability of probes in health and disease across different tissues and populations.

Why should a genome be protected? Because it contains our most private data! A genome contains an organism’s set of genetic material (DNA and, in viruses, RNA), and it contains all genes and non-coding sequences. The structure of DNA was described by Watson and Crick in 1953, but the first studies were conducted a century earlier by Miescher, who described the structure and chemical composition of the nucleus. The first action aimed at securing the results of genetic research was the creation of databases for the results obtained using genetic fingerprinting technology. The discovery of the sequencing method and the introduction of the polymerase chain reaction laid the foundations for understanding the genome’s function. Automated DNA sequencing proved to be hundreds of times faster than traditional methods, thus reducing the cost and time of genome analyses. Thousands of genomic data points are stored in private and governmental databases. The security of patients’ genomic data must be ensured by protecting it from unauthorized use while, at the same time, enabling research for the sake of public health. The falling prices of genome sequencing and the increasing availability of commercial sequencing for the public could result in ethical problems and undermine the safety of personal information.

Background: Correlation of genes within tissues has attracted much attention. In contrast, genes that are INDependent In Expression (INDIE) remain poorly understood, even though they may represent tissue admixtures, reflect new regulatory mechanisms, either transcriptional or post-transcriptional, and contribute to biomarkers or machine learning algorithms. We hypothesised that INDIE genes can be found, may remain uncorrelated across tissues, and replicate within tissues in external datasets. Methods: Biweight midcorrelation was calculated for each gene against all other genes with sufficiently high expression in the given tissue from the GTEx dataset v8, along with the means of absolute values of obtained correlation coefficients. The threshold for gene designation as INDIE was both absolute (r) and relative (Z-score), while the threshold for external validation in the whole blood (four datasets) and the ileum (two datasets) was relative. Results: Only one gene, RPL13P12, was INDIE in all the analysed GTEx tissues, but it did not replicate in the external datasets. In contrast, HIST1H2AD and TMEM176B were not only INDIE in GTEx whole blood but also replicated in all four external datasets, despite their heterogeneity. Moreover, ACAT2 replicated in both external ileal datasets. The haemoglobin gene HBB belonged to most widespread INDIE genes in various GTEx tissues and was validated in an external ileal dataset, pointing towards the importance of tissue heterogeneity in bulk samples. Conclusions: A set of genes exhibiting independent expression patterns across various tissues of GTEx was described. Results for each tissue are made available. Even though many findings can be explained by tissue heterogeneity, some results point towards interesting mechanisms of gene expression regulation.