LONG-TERM GENETIC AND EPIGENETIC DISORDERS IN PERSONS EXPOSED TO IONIZING RADIATION AND THEIR DESCENDANTS (review)

The review is devoted to long-term genetic and epigenetic disorders in exposed individuals and their descendants, namely to cytogenetic effects in the Chornobyl NPP accident clean-up workers and their children, DNA methylation as an epigenetic modification of human genome. Data presented in review expand the understanding of risk of the prolonged exposure for the present and future generations, which is one of key problems posed by fundamental radiation genetics and human radiobiology. The Scientific Council meeting of NAMS approved the NRCRM Annual Report. Key words: Chornobyl NPP accident, cytogenetic effects, DNA methylation.

Practical guide for managing large-scale human genome data in research

Studies in human genetics deal with a plethora of human genome sequencing data that are generated from specimens as well as available on public domains. With the development of various bioinformatics applications, maintaining the productivity of research, managing human genome data, and analyzing downstream data is essential. This review aims to guide struggling researchers to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses. Here, we discuss worldwide human genome projects that could be integrated into any data for improved analysis. Obtaining human whole-genome sequencing data from both data stores and processes is costly; therefore, we focus on the development of data format and software that manipulate whole-genome sequencing. Once the sequencing is complete and its format and data processing tools are selected, a computational platform is required. For the platform, we describe a multi-cloud strategy that balances between cost, performance, and customizability. A good quality published research relies on data reproducibility to ensure quality results, reusability for applications to other datasets, as well as scalability for the future increase of datasets. To solve these, we describe several key technologies developed in computer science, including workflow engine. We also discuss the ethical guidelines inevitable for human genomic data analysis that differ from model organisms. Finally, the future ideal perspective of data processing and analysis is summarized.

Evolutionary History of Endogenous Human Herpesvirus 6 Reflects Human Migration out of Africa

Human herpesvirus 6A and 6B (HHV-6) can integrate into the germline, and as a result, ∼70 million people harbor the genome of one of these viruses in every cell of their body. Until now, it has been largely unknown if 1) these integrations are ancient, 2) if they still occur, and 3) whether circulating virus strains differ from integrated ones. Here, we used next-generation sequencing and mining of public human genome data sets to generate the largest and most diverse collection of circulating and integrated HHV-6 genomes studied to date. In genomes of geographically dispersed, only distantly related people, we identified clades of integrated viruses that originated from a single ancestral event, confirming this with fluorescent in situ hybridization to directly observe the integration locus. In contrast to HHV-6B, circulating and integrated HHV-6A sequences form distinct clades, arguing against ongoing integration of circulating HHV-6A or “reactivation” of integrated HHV-6A. Taken together, our study provides the first comprehensive picture of the evolution of HHV-6, and reveals that integration of heritable HHV-6 has occurred since the time of, if not before, human migrations out of Africa.

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology has enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data, however there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN™ and DeepVariant) using Genome in a Bottle Consortium, “synthetic-diploid” and simulated WGS datasets. DRAGEN™ and DeepVariant show a better accuracy in SNPs and indels calling, with no significant differences in their F1-score. DRAGEN™ platform offers accuracy, flexibility and a highly-efficient running speed, and therefore superior advantage in the analysis of WGS data on a large scale. The combination of DRAGEN™ and DeepVariant also provides a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical application.

Hybrid Convolutional Neural Network with PSO Based Severe Dengue Prognosis Method in Human Genome Data

Dengue is one of the most significant diseases transmitted by arthropods in the world. Dengue phenotypes are focused on documented inaccuracies in the laboratory and clinical studies. In countries with a high incidence of this disease, early diagnosis of dengue is still a concern for public health. Deep learning has been developed as a highly versatile and accurate methodology for classification and regression, which requires small adjustment, interpretable results, and the prediction of risk for complex diseases. This work is motivated by the inclusion of the Particle Swarm Optimization (PSO) algorithm for the fine-tuning of the model's parameters in the convolutional neural network (CNN). The use of this PSO was used to forecast patients with extreme dengue, and to refine the input weight vector and CNN parameters to achieve anticipated precision, and to prevent premature convergence towards local optimum conditions.

Computational Intelligence applied to Human Genome Data for the Dengue Severity Prognosis

Dengue has become one of the most important worldwide arthropodborne diseases around the world. Here, one hundred and two Brazilian dengue virus (DENV) III patients and controls were genotyped for 322 innate immunity gene loci. All biological data (including age, sex and genome background) were analyzed using Machine Learning techniques to discriminate tendency to severe dengue phenotype development. Our current approach produces median values for accuracy greater than 86%, with sensitivity and specificity over 98% and 51%, respectively. Genome data information from 13 key immune polymorphic SNPs was used under different dominant or recessive models. Our approach is a valuable tool for early diagnosis of the severe form of dengue infection and can be used to identify individuals at high risk of developing this form of the disease even in uninfected individuals. The model also identifies various genes involved dengue severity.

GeneSyno : Simple tool to extract gene sequence from the human genome despite synonymous gene terms

Extracting gene data from the human genome is a tricky task. Gene name is the key information for harvesting its sequence, annotation, and other related data. Unfortunately, most human genes have different and multiple names, depending on the database and the resource in which they have been published. Such an issue is delaying the ability of researchers to gather the necessary knowledge and to build their opinion on the function of genes. Here we introduce GeneSyno, a simple, versatile, and reliable tool that can be used to extract gene information from human genome data even though it is synonymous gene names. GeneSyno was written using C and Python programming languages and could easily be integrated into another pipeline