SnakeLines: integrated set of computational pipelines for sequencing reads.

With the rapid growth of massively parallel sequencing technologies, still more laboratories are utilising sequenced DNA fragments for genomic analyses. Interpretation of sequencing data is, however, strongly dependent on bioinformatics processing, which is often too demanding for clinicians and researchers without a computational background. Another problem represents the reproducibility of computational analyses across separated computational centres with inconsistent versions of installed libraries and bioinformatics tools. We propose an easily extensible set of computational pipelines, called SnakeLines, for processing sequencing reads; including mapping, assembly, variant calling, viral identification, transcriptomics, and metagenomics analysis. Individual steps of an analysis, along with methods and their parameters can be readily modified in a single configuration file. Provided pipelines are embedded in virtual environments that ensure isolation of required resources from the host operating system, rapid deployment, and reproducibility of analysis across different Unix-based platforms. SnakeLines is a powerful framework for the automation of bioinformatics analyses, with emphasis on a simple set-up, modifications, extensibility, and reproducibility. The framework is already routinely used in various research projects and their applications, especially in the Slovak national surveillance of SARS-CoV-2.

APOC3 and ABCA1 variants in unusual combined hypolipidaemia showing premature peripheral vascular disease.

BACKGROUND: Familial combined hypolipidaemia is a condition characterised by very low concentrations of circulating very-low-density lipoprotein (VLDL), low-density lipoprotein cholesterol (LDL), and high-density lipoprotein cholesterol (HDL). It is thought that low LDL/combined hypolipidaemia can protect from cardiovascular disease (CVD), but this is not what we found in a case we present. OBJECTIVE: We report on a 57-years-old male patient with combined hypolipidaemia who presented with premature peripheral vascular disease. We investigated also his two sons, 32- and 27-years-old, who manifested a tendency to low lipid levels. METHODS AND RESULTS: We used Illumina exome analysis in all three individuals and in all of them we could exclude the major effect of the variants within the genes most frequently mutated in hypolipidaemia, including recently reported LIPC gene variant. Instead, in all three individuals we identified a novel ABCA1 variant, possibly responsible for the decreased HDL levels. The proband and one of his sons also share the splicing APOC3 variant rs138326449, known to be associated with decreased TG levels. CONCLUSION: The heterogeneous nature and the risk of atherosclerosis in combined hypolipidaemia seems to be variable, based on an interplay between low HDL and LDL levels, and it depends on the combination of variants that cause it (Tab. 2, Ref. 38).

Evaluation and limitations of different approaches among COVID-19 fatal cases using whole-exome sequencing data.

BACKGROUND: COVID-19 caused by the SARS-CoV-2 infection may result in various disease symptoms and severity, ranging from asymptomatic, through mildly symptomatic, up to very severe and even fatal cases. Although environmental, clinical, and social factors play important roles in both susceptibility to the SARS-CoV-2 infection and progress of COVID-19 disease, it is becoming evident that both pathogen and host genetic factors are important too. In this study, we report findings from whole-exome sequencing (WES) of 27 individuals who died due to COVID-19, especially focusing on frequencies of DNA variants in genes previously associated with the SARS-CoV-2 infection and the severity of COVID-19. RESULTS: We selected the risk DNA variants/alleles or target genes using four different approaches: 1) aggregated GWAS results from the GWAS Catalog; 2) selected publications from PubMed; 3) the aggregated results of the Host Genetics Initiative database; and 4) a commercial DNA variant annotation/interpretation tool providing its own knowledgebase. We divided these variants/genes into those reported to influence the susceptibility to the SARS-CoV-2 infection and those influencing the severity of COVID-19. Based on the above, we compared the frequencies of alleles found in the fatal COVID-19 cases to the frequencies identified in two population control datasets (non-Finnish European population from the gnomAD database and genomic frequencies specific for the Slovak population from our own database). When compared to both control population datasets, our analyses indicated a trend of higher frequencies of severe COVID-19 associated risk alleles among fatal COVID-19 cases. This trend reached statistical significance specifically when using the HGI-derived variant list. We also analysed other approaches to WES data evaluation, demonstrating its utility as well as limitations. CONCLUSIONS: Although our results proved the likely involvement of host genetic factors pointed out by previous studies looking into severity of COVID-19 disease, careful considerations of the molecular-testing strategies and the evaluated genomic positions may have a strong impact on the utility of genomic testing.

Mitochondrial DNA copy number changes, heteroplasmy, and mutations in plasma-derived exosomes and brain tissue of glioblastoma patients.

Glioblastoma is the most common malignant tumor of the central nervous system (CNS) in adults. Glioblastoma cells show increased glucose consumption associated with poor prognosis. Since mitochondria play a crucial role in energy metabolism, mutations and copy number changes of mitochondrial DNA may serve as biomarkers. As the brain is difficult to access, analysis of mitochondria directly from the brain tissue represents a challenge. Exosome analysis is an alternative (still poorly explored) approach to investigate molecular changes in CNS tumors. We analyzed brain tissue DNA and plasma-derived exosomal DNA (exoDNA) of 44 glioblastoma patients and 40 control individuals. Quantitative real-time PCR was performed to determine mtDNA copy numbers and the Kruskal-Wallis and Mann-Whitney U test were used for statistical analysis of data. Subsequently, sequencing libraries were prepared and sequenced on the MiSeq platform to identify mtDNA point mutations. Tissue mtDNA copy number was different among controls and patients in multiple comparisons. A similar tendency was detected in exosomes. Based on NGS analysis, several mtDNA point mutations showed slightly different frequencies between cases and controls, but the clinical relevance of these observations is difficult to assess and likely less than that of overall mtDNA copy number changes. Allele frequencies of variants were used to determine the level of heteroplasmy (found to be higher in exo-mtDNA of control individuals). Despite the suggested potential, the use of such biomarkers for the screening and/or diagnosis of glioblastomas is still limited, thus further studies are needed.

Privacy-preserving storage of sequenced genomic data.

BACKGROUND: The current and future applications of genomic data may raise ethical and privacy concerns. Processing and storing of this data introduce a risk of abuse by potential offenders since the human genome contains sensitive personal information. For this reason, we have developed a privacy-preserving method, named Varlock providing secure storage of sequenced genomic data. We used a public set of population allele frequencies to mask the personal alleles detected in genomic reads. Each personal allele described by the public set is masked by a randomly selected population allele with respect to its frequency. Masked alleles are preserved in an encrypted confidential file that can be shared in whole or in part using public-key cryptography. RESULTS: Our method masked the personal variants and introduced new variants detected in a personal masked genome. Alternative alleles with lower population frequency were masked and introduced more often. We performed a joint PCA analysis of personal and masked VCFs, showing that the VCFs between the two groups cannot be trivially mapped. Moreover, the method is reversible and personal alleles in specific genomic regions can be unmasked on demand. CONCLUSION: Our method masks personal alleles within genomic reads while preserving valuable non-sensitive properties of sequenced DNA fragments for further research. Personal alleles in the desired genomic regions may be restored and shared with patients, clinics, and researchers. We suggest that the method can provide an additional security layer for storing and sharing of the raw aligned reads.

Combination of Fetal Fraction Estimators Based on Fragment Lengths and Fragment Counts in Non-Invasive Prenatal Testing.

The reliability of non-invasive prenatal testing is highly dependent on accurate estimation of fetal fraction. Several methods have been proposed up to date, utilizing different attributes of analyzed genomic material, for example length and genomic location of sequenced DNA fragments. These two sources of information are relatively unrelated, but so far, there have been no published attempts to combine them to get an improved predictor. We collected 2454 single euploid male fetus samples from women undergoing NIPT testing. Fetal fractions were calculated using several proposed predictors and the state-of-the-art SeqFF method. Predictions were compared with the reference Y-based method. We demonstrate that prediction based on length of sequenced DNA fragments may achieve nearly the same precision as the state-of-the-art methods based on their genomic locations. We also show that combination of several sample attributes leads to a predictor that has superior prediction accuracy over any single approach. Finally, appropriate weighting of samples in the training process may achieve higher accuracy for samples with low fetal fraction and so allow more reliability for subsequent testing for genomic aberrations. We propose several improvements in fetal fraction estimation with a special focus on the samples most prone to wrong conclusion.

On the critical evaluation and confirmation of germline sequence variants identified using massively parallel sequencing.

Although massively parallel sequencing (MPS) is becoming common practice in both research and routine clinical care, confirmation requirements of identified DNA variants using alternative methods are still topics of debate. When evaluating variants directly from MPS data, different read depth statistics, together with specialized genotype quality scores are, therefore, of high relevance. Here we report results of our validation study performed in two different ways: 1) confirmation of MPS identified variants using Sanger sequencing; and 2) simultaneous Sanger and MPS analysis of exons of selected genes. Detailed examination of false-positive and false-negative findings revealed typical error sources connected to low read depth/coverage, incomplete reference genome, indel realignment problems, as well as microsatellite associated amplification errors leading to base miss-calling. However, all these error types were identifiable with thorough manual revision of aligned reads according to specific patterns of distributions of variants and their corresponding reads. Moreover, our results point to dependence of both basic quantitative metrics (such as total read counts, alternative allele read counts and allelic balance) together with specific genotype quality scores on the used bioinformatics pipeline, stressing thus the need for establishing of specific thresholds for these metrics in each laboratory and for each involved pipeline independently.