ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses

Variability in Pattern of Mutational Signatures in Multiple Myeloma As a Function of Racial Origin

Neisseria gonorrhoeae, the bacterium responsible for the sexually transmitted disease gonorrhoea, has shown an extraordinary ability to develop antimicrobial resistance (AMR) to multiple classes of antimicrobials. With no available vaccine, managing N. gonorrhoeae infections demands effective preventive measures, antibiotic treatment and epidemiological surveillance. The latter two are progressively being supported by the generation of whole-genome sequencing (WGS) data on behalf of national and international surveillance programmes. In this context, this study aims to perform N. gonorrhoeae clustering into genogroups based on WGS data, for enhanced prospective laboratory surveillance. Particularly, it aims to identify the major circulating WGS-genogroups in Europe and to establish a relationship between these and AMR. Ultimately, it enriches public databases by contributing with WGS data from Portuguese isolates spanning 15 years of surveillance. A total of 3791 carefully inspected N. gonorrhoeae genomes from isolates collected across Europe were analysed using a gene-by-gene approach (i.e. using cgMLST). Analysis of cluster composition and stability allowed the classification of isolates into a two-step hierarchical genogroup level determined by two allelic distance thresholds revealing cluster stability. Genogroup clustering in general agreed with available N. gonorrhoeae typing methods [i.e. MLST (multilocus sequence typing), NG-MAST (N. gonorrhoeae multi-antigen sequence typing) and PubMLST core-genome groups], highlighting the predominant genogroups circulating in Europe, and revealed that the vast majority of the genogroups present a dominant AMR profile. Additionally, a non-static gene-by-gene approach combined with a more discriminatory threshold for potential epidemiological linkage enabled us to match data with previous reports on outbreaks or transmission chains. In conclusion, this genogroup assignment allows a comprehensive analysis of N. gonorrhoeae genetic diversity and the identification of the WGS-based genogroups circulating in Europe, while facilitating the assessment (and continuous monitoring) of their frequency, geographical dispersion and potential association with specific AMR signatures. This strategy may benefit public-health actions through the prioritization of genogroups to be controlled, the identification of emerging resistance carriage, and the potential facilitation of data sharing and communication.

We aim to provide insight and guidance on the utility of whole genome sequencing (WGS) data for investigating food-borne outbreaks of Shiga toxin-producing Escherichia coli (STEC) O157:H7 in England between 2013 and 2017. Analysis of WGS data delivered an unprecedented level of strain discrimination when compared with multilocus variable number tandem repeat analysis. The robustness of the WGS method ensured confidence in the microbiological identification of linked cases, even when epidemiological links were obscured. There was evidence that phylogeny derived from WGS data can be used to trace the geographical origin of an isolate. Further analysis of the phylogenetic data provided insight on the evolutionary context of emerging pathogenic strains. Publically available WGS data linked to the clinical, epidemiological and environmental context of the sequenced strain has improved trace back investigations during outbreaks. Expanding the use of WGS-based typing analysis globally will ensure the rapid implementation of interventions to protect public health, inform risk assessment and facilitate the management of national and international food-borne outbreaks of STEC O157:H7.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

BackgroundHaplotype reconstruction (phasing) is an essential step in many applications, including imputation and genomic selection. The best phasing methods rely on both familial and linkage disequilibrium (LD) information. With whole-genome sequence (WGS) data, relatively small samples of reference individuals are generally sequenced due to prohibitive sequencing costs, thus only a limited amount of familial information is available. However, reference individuals have many relatives that have been genotyped (at lower density). The goal of our study was to improve phasing of WGS data by integrating familial information from haplotypes that were obtained from a larger genotyped dataset and to quantify its impact on imputation accuracy.ResultsAligning a pre-phased WGS panel [~5 million single nucleotide polymorphisms (SNPs)], which is based on LD information only, to a 50k SNP array that is phased with both LD and familial information (called scaffold) resulted in correctly assigning parental origin for 99.62% of the WGS SNPs, their phase being determined unambiguously based on parental genotypes. Without using the 50k haplotypes as scaffold, that value dropped as expected to 50%. Correctly phased segments were on average longer after alignment to the genotype phase while the number of switches decreased slightly. Most of the incorrectly assigned segments, and subsequent switches, were due to singleton errors. Imputation from 50k SNP array to WGS data with improved phasing had a marginal impact on imputation accuracy (measured as r2), i.e. on average, 90.47% with traditional techniques versus 90.65% with pre-phasing integrating familial information. Differences were larger for SNPs located in chromosome ends and rare variants. Using a denser WGS panel (~13 millions SNPs) that was obtained with traditional variant filtering rules, we found similar results although performances of both phasing and imputation accuracy were lower.ConclusionsWe present a phasing strategy for WGS data, which indirectly integrates familial information by aligning WGS haplotypes that are pre-phased with LD information only on haplotypes obtained with genotyping data, with both LD and familial information and on a much larger population. This strategy results in very few mismatches with the phase obtained by Mendelian segregation rules. Finally, we propose a strategy to further improve phasing accuracy based on haplotype clusters obtained with genotyping data.

Sexual networks are difficult to construct because of incomplete sexual partner data. The proximity of people within a network may be inferred from genetically similar infections. We explored genomic data combined with partner services investigation (PSI) data to extend our understanding of sexual networks affected by Neisseria gonorrhoeae (NG). We used 2017-2019 PSI and whole-genome sequencing (WGS) data from 8 jurisdictions participating in Centers for Disease Control and Prevention's Strengthening the US Response to Resistant Gonorrhea (SURRG) project. Clusters were identified from sexual contacts and through genetically similar NG isolates. Sexual mixing patterns were characterized by describing the clusters by the individual's gender and gender of their sex partners. Our study included 4627 diagnoses of NG infection (81% sequenced), 2455 people received a PSI, 393 people were negative contacts of cases, and 495 were contacts with an unknown NG status. We identified 823 distinct clusters using PSI data combined with WGS data. Of cases that were not linked to any other case using PSI data, 37% were linked when using WGS data. Overall, 40% of PSI cases were allocated to a larger cluster when PSI and WGS data were combined compared with PSI data alone. Mixed clusters containing women, men who report sex with women, and men who report sex with men were common when using the WGS data either alone or in combination with the PSI data. Combining PSI and WGS data improves our understanding of sexual network connectivity.

Facing multidrug resistant (MDR) bacterial pathogens is one of the most important challenges for our society. The spread of highly virulent and resistant pathogens can be described using molecular typing technologies; in particular, whole genome sequencing (WGS) data can be usedf or molecular typing purposes with high resolution. WGS data analysis can explain the spatiotemporal patterns of pathogen transmission. However, the transmission between compartments (human, animal, food, environment) is very complex. Interoperable and curated metadata are a key requirement for fully understanding this complexity. In addition, high quality sequence data are a key element between centres using WGS data for diagnostic and epidemiological applications. We aim to describe steps to improve WGS data analysis and to implement a molecular surveillance platform allowing integration of high resolution WGS typing data and epidemiological data.

CefiderocolFinder: a tool for detecting genetic adaptations implicated in cefiderocol resistance.

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Accurate species identification from Whole Genome Sequencing (WGS) data remains challenging, particularly for closely related species such as sheep (Ovis aries) and goats (Capra hircus). Through analysis of mapping quality metrics and Kraken2 taxonomic classification of 40 WGS sheep and goat samples, we demonstrate that conventional approaches yield ambiguous results, with overlapping alignment rates and inconclusive taxonomic assignments. We present a robust comparative genomic approach that uses species-specific genomic regions to distinguish these species in WGS samples. We define species-specific regions as those exhibiting distinctive coverage patterns: average coverage when samples are aligned to their matching reference genome but absent/low coverage when aligned to non-matching references. By analyzing WGS data from both species aligned to both reference genomes, we identified 155,800 goat-specific and 1,714,126 sheep-specific regions. After curation, 10 high-confidence regions per species were selected, achieving 100% accuracy within the analyzed validation datasets comprising 14 independent samples. This approach provides reliable species verification for WGS data and establishes a framework that could be extended to other closely related species. To facilitate adoption, we provide analysis scripts and curated genomic regions available on our GitHub repository ( https://github.com/BTC-Lab/Goat_Sheep_specific_regions ).

Identification of Closely Related Listeria monocytogenes Isolates with No Apparent Evidence for a Common Source or Location: A Retrospective Whole Genome Sequencing Analysis

INTRODUCTION:Genome-wide association studies (GWAS) have identified loci associated with Alzheimer’s disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.METHODS:We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer’s Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.RESULTS:Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.DISCUSSION:This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Background: Moose ( Alces alces) colonized the North American continent from Asia less than 15,000 years ago, and spread across the boreal forest regions of Canada and the northern United States (US). Contemporary populations have low genetic diversity, due either to low number of individuals in the original migration (founder effect), and/or subsequent population bottlenecks in North America. Genetic tests based on informative single nucleotide polymorphism (SNP) markers are helpful in forensic and wildlife conservation activities, but have been difficult to develop for moose, due to the lack of a reference genome assembly and whole genome sequence (WGS) data. Methods: WGS data were generated for four individual moose from the US states of Alaska, Idaho, Wyoming, and Vermont with minimum and average genome coverage depths of 14- and 19-fold, respectively. Cattle and sheep reference genomes were used for aligning sequence reads and identifying moose SNPs. Results: Approximately 11% and 9% of moose WGS reads aligned to cattle and sheep genomes, respectively. The reads clustered at genomic segments, where sequence identity between these species was greater than 95%. In these segments, average mapped read depth was approximately 19-fold. Sets of 46,005 and 36,934 high-confidence SNPs were identified from cattle and sheep comparisons, respectively, with 773 and 552 of those having minor allele frequency of 0.5 and conserved flanking sequences in all three species. Among the four moose, heterozygosity and allele sharing of SNP genotypes were consistent with decreasing levels of moose genetic diversity from west to east. A minimum set of 317 SNPs, informative across all four moose, was selected as a resource for future SNP assay design. Conclusions: All SNPs and associated information are available, without restriction, to support development of SNP-based tests for animal identification, parentage determination, and estimating relatedness in North American moose.

Whole Genome Sequencing Identifies Single Nucleotide Variants, Structural and Copy Number Abnormalities with a High Sensitivity in Hematological Neoplasm: An All in One Diagnostic Tool Tested on 3241 Cases

BackgroundHigh oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference.ResultsIn the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells.ConclusionsATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp. Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.