Background: Transposons are selfish genetic elements that self-reproduce in host DNA. They were active during evolutionary history and now occupy almost half of mammalian genomes. Close insertions of transposons reshaped structure and regulation of many genes considerably. Co-evolution of transposons and host DNA frequently results in the formation of new regulatory regions. Previously we published a concept that the proportion of functional features held by transposons positively correlates with the rate of regulatory evolution of the respective genes. Methods: We ranked human genes and molecular pathways according to their regulatory evolution rates based on high throughput genome-wide data on five histone modifications (H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K9me3) linked with transposons for five human cell lines. Results: Based on the total of approximately 1.5 million histone tags, we ranked regulatory evolution rates for 25075 human genes and 3121 molecular pathways and identified groups of molecular processes that showed signs of either fast or slow regulatory evolution. However, histone tags showed different regulatory patterns and formed two distinct clusters: promoter/active chromatin tags (H3K4me3, H3K9ac, H3K27ac) vs. heterochromatin tags (H3K27me3, H3K9me3). Conclusion: In humans, transposon-linked histone marks evolved in a coordinated way depending on their functional roles.

Parallel reporter assays provide rich data to decipher gene regulatory regions with deep learning. Here we introduce LegNet, a convolutional network architecture that secured the first place for our autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. To construct LegNet, we drew inspiration from EfficientNetV2 and reformulated the sequence-to-expression regression problem as a soft-classification task. Here, with published data, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of sequence alterations, such as single-nucleotide variants.

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.

Neural networks have proven to be an immensely powerful tool in predicting functional genomic regions, in particular with many recent successes in deciphering gene regulatory logic. However, how model architecture and training strategy choices affect model performance has not been systematically evaluated for genomics models. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding experimentally determined expression levels to best capture the relationship between regulatory DNA and gene expression in yeast. To robustly evaluate the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across all models, others differed substantially. For some sequence types, model performances exhibited correlation scores as high as 0.98, while for others, substantial improvement is still required. The top-performing models were all neural networks, which demonstrated substantial performance gains by customizing model architectures to the nature of the experiment and utilizing novel training strategies tailored to genomics sequence data. Overall, our DREAM Challenge highlights the need to benchmark genomics models across different scenarios to uncover their limitations.