Genomic Data Research Articles

Equitable community-based participatory research engagement with communities of color drives All of Us Wisconsin genomic research priorities.

The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities. We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert community engagement scientist facilitated 6 SIG meetings over the year, explicitly fostering openness and flexibility during conversations. We qualitatively analyzed discussions using a social movement framework tailored for community-based participatory research (CBPR) mobilization. The SIG evolved through CBPR stages of emergence, coalescence, momentum, and maintenance/integration. Researchers prioritized community needs above personal academic interests while community members kept discussions focused on tangible return of value to communities. One key outcome includes SIG-driven shifts in programmatic and research priorities of the All of Us Research Program in Southeastern Wisconsin. One major challenge was building equitable conversations that balanced scientific rigor and community understanding. Our approach allowed for a rich dialogue to emerge. Points of connection and disconnection between community members and scientists offered important guidance for emerging areas of genomic inquiry. Our study presents a robust foundation for future efforts to engage diverse communities in CBPR, particularly on healthcare concerns affecting UBR communities.

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Streptomyces yaizuensis sp. nov., a berninamycin C-producing actinomycete isolated from sponge.

While screening for antibiotics in a marine sample, we discovered a berninamycin C-producing actinomycete, designated YSPA8T, isolated from a sponge. A polyphasic approach was used to determine the taxonomic position of the strain. Strain YSPA8T formed sympodially branched aerial mycelia that ultimately segment into chains of spores. Comparative and phylogenetic analyses of the 16S rRNA gene sequence showed that strain YSPA8T were closely related to Streptomyces clavuligerus ATCC 27064T (99.66%), Streptomyces amakusaensis NRRL B-3351T (98.69%), Streptomyces inusitatus NBRC 13601T (98.48%), and 'Streptomyces jumonjinensis' JCM 4947 (98.41%). The phylogenetic tree using the 16S rRNA gene sequences, and both phylogenomic trees suggested that the closest relative of strain YSPA8T was S. clavuligerus ATCC 27064T. The average nucleotide identity, digital DNA-DNA hybridization, and average amino acid identity values between strain YSPA8T and S. clavuligerus ATCC 27064T were 84.1%, 28.9%, and 82.5%, respectively, which were below the thresholds of 95%, 70%, and 95% for a prokaryotic conspecific assignment. The G + C of the strain YSPA8T was 72.6%. Whole-cell hydrolysates of strain YSPA8T contained LL-diaminopimelic acid. The predominant menaquinones were MK-9(H6) (49%) and MK-9(H8) (48%), and the major fatty acids were C16:0 (26.8%), C16:1 ω7c/ω6c (17.2%), iso-C16:0 (16.0%), and iso-C15:0 (12.5%). The major phospholipids were diphosphatidylglycerol, phosphatidylethanolamine, and other unidentified phospholipids. Based on the phenotypic, phylogenetic, genomic, and chemotaxonomic data, strain YSPA8T represents a novel species of the genus Streptomyces, and the proposed name for this species is Streptomyces yaizuensis sp. nov. The type strain is YSPA8T (=NBRC 115866T = TBRC 17196T).

Analysis of Powassan Virus Genome Sequences from Human Cases Reveals Substantial Genetic Diversity with Implications for Molecular Assay Development

Powassan virus (POWV) is an emerging tick-borne virus that causes severe meningoencephalitis in the United States, Canada, and Russia. Serology is generally the preferred diagnostic modality, but PCR on cerebrospinal fluid, blood, or urine has an important role, particularly in immunocompromised patients who are unable to mount a serologic response. Although the perceived poor sensitivity of PCR in the general population may be due to the biology of infection and health-seeking behavior (with short viremic periods that end before hospital presentation), limitations in assay design may also contribute. Genome sequences from clinical POWV cases are extremely scarce; PCR assay design has been informed by those available, but the numbers are limited. Larger numbers of genome sequences from tick-derived POWV are available, but it is not known if POWV genomes from human infections broadly mirror genomes from tick hosts, or if human infections are caused by a subset of more virulent strains. We obtained viral genomic data from 10 previously unpublished POWV human infections and showed that they broadly mirror the diversity of genome sequences seen in ticks, including all three major clades (lineage I, lineage II Northeast, and lineage II Midwest). These newly published clinical POWV genome sequences include the first confirmed lineage I infection in the United States, highlighting the relevance of all clades in human disease. An in silico analysis of published POWV PCR assays shows that many assays were optimized against a single clade and have mismatches that may affect their sensitivity when applied across clades. This analysis serves as a launching point for improved PCR design for clinical diagnostics and environmental surveillance.

Identifying factors that influence the use of pathogen genomics in Australia and New Zealand: a protocol.

Pathogen genomics, where whole genome sequencing technologies are used to produce complete genomic sequences of pathogens, is being increasingly used for infectious disease surveillance and outbreak response. Although proof-of-concept studies have highlighted the viability of using pathogen genomics in public health, few studies have investigated how end-users utilize pathogen genomics in public health. We describe a protocol for a study that aims to identify key factors that influence the use of pathogen genomics to inform public health responses against infectious diseases in Australia and New Zealand. We will use qualitative comparative analysis (QCA), a case-oriented methodology that systematically compares and analyses multiple cases (or 'units of analysis'), to identify multiple pathways leading to the use of pathogen genomics results in public health actions. As part of the process, we will develop a rubric to identify and define the use of pathogen genomics and individual factors affecting this process. Simultaneously, we will identify cases where pathogen genomics has been used in public health across Australia and New Zealand. Data for these cases will be collected from document review of publicly available and confidential documents and semi-structured interviews with technicians and end-users and summarized in a case report. These case reports will form the basis for scoring each case on the extent of the use of pathogen genomics data and the presence or absence of specific factors such as the ease of extracting essential information from pathogen genomics reports and perceptions toward pathogen genomics. Using the scores, cases will be analyzed using QCA techniques to identify pathways leading to the use of pathogen genomics data. These pathways will be interpreted alongside the cases to provide rich explanations of the use of pathogen genomics in public health. This study will improve our understanding of the key factors that facilitate or hinder the use of pathogen genomics to inform public health authorities and end-users. These findings may inform ways to enhance the use of pathogen genomics data in public health.

CascAGS: Comparative Analysis of SNP Calling Methods for Human Genome Data in the Absence of Gold Standard.

The development of third-generation sequencing has accelerated the boom of single nucleotide polymorphism (SNP) calling methods, but evaluating accuracy remains challenging owing to the absence of the SNP gold standard. The definitions for without-gold-standard and performance metrics and their estimation are urgently needed. Additionally, the possible correlations between different SNP loci should also be further explored. To address these challenges, we first introduced the concept of a gold standard and imperfect gold standard under the consistency framework and gave the corresponding definitions of sensitivity and specificity. A latent class model (LCM) was established to estimate the sensitivity and specificity of callers. Furthermore, we incorporated different dependency structures into LCM to investigate their impact on sensitivity and specificity. The performance of LCM was illustrated by comparing the accuracy of BCFtools, DeepVariant, FreeBayes, and GATK on various datasets. Through estimations across multiple datasets, the results indicate that LCM is well-suitable for evaluating callers without the SNP gold standard, and accurate inclusion of the dependency between variations is crucial for better performance ranking. DeepVariant has a higher sum of sensitivity and specificity than other callers, followed by GATK and BCFtools. FreeBayes has low sensitivity but high specificity. Notably, appropriate sequencing coverage is another important factor for precise callers' evaluation. Most importantly, a web interface for assessing and comparing different callers was developed to simplify the evaluation process.

The role of artificial intelligence in the management of liver diseases.

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

Whole-genome sequencing of clinical isolates of Klebsiella michiganensi in China carrying blaIPM-4 and blaNDM-1

AimThis study aimed to analyze the prevalence, phenotypes, and carriage of resistance genes of carbapenem-resistant Klebsiella michiganensis strains isolated in our hospital. Methodology: Four K. michiganensis strains were collected from January 2015 to December 2023. Antimicrobial susceptibility was tested using 21 antibiotics with the BD Phoenix™ M50 System. Whole-genome sequencing of the four strains was performed on an Illumina NovaSeq 6000 platform. Species identification was performed using Kleborate software, sequence type (ST) was performed using MLST, and prediction of antibiotic resistance genes and virulence genes were performed using ABRicate. PlasmidFinder was used to search for plasmids. Whole genome data from 211 strains of carbapenem-resistant K. michiganensis were downloaded from the NCBI database; together with the strains in this study, these data were used to construct an phylogenetic tree using genome single nucleotide polymorphisms (SNP). ResultsAntimicrobial susceptibility showed that K. michiganensis was highly resistant to β-lactams. For the three carbapenems, strain WF0046 was only resistant to ertapenem, while WF0047, WF0052, and WF0053 were resistant to all three carbapenems tested. The four strains of K. michiganensis only showed complete sensitivity to polymyxin E and tigecycline. The four K. michiganensis strains were found to have 43 resistance genes, and all carried carbapenem resistance genes; WF0046 carried the carbapenem resistance gene blaIMP-4, while the other three strains carried blaNDM-1. Among the other resistance genes, β-lactam resistance genes were predicted most (11 types), followed by eight types of aminoglycoside resistance genes. Of the strains characterized in this study, WF0046 belonged to ST158, WF0047 belonged to ST40, WF0052 belonged to ST533, and WF0053 belonged to ST13. These four strains, along with 211 carbapenem-resistant K. michiganensis strains downloaded from NCBI, were divided into five clades; WF0052 belonged to clade A, WF0046 belonged to clade C, and WF0047 and WF0053 both belonged to clade D. The four strains had relatively distant genetic relationships; WF0046, WF0047, and WF0053 were closely related to strains of human origin from other regions of China, while WF0052 was genetically close with a strain of K. michiganensis isolated in the United States. ConclusionThe strains of K. michiganensis from our hospital were resistant to multiple antibiotics, and all strains carried carbapenem resistance genes along with multiple other antibiotic resistance genes. The phylogenetic results showed that these four strains had distant genetic relationships and different ST types, indicating that they came from different sources and the possibility of polyclonal transmission.

A Mendelian Randomization Study of the Connection Between Exogenous Hormones and Perianal Abscess in Pediatric Patients.

Background: Recent years have witnessed the hypothesis that bioavailable testosterone (BT) might be closely related to the development of inflammatory diseases, especially anal abscess (AA), a common inflammatory ailment with unclear pathogenesis. Given that AA is more prevalent among males, this study investigates the causal relationship between BT and AA. Objective: To explore the causal link between BT and AA, a Mendelian randomization (MR) study was conducted using large-scale genomic data. Materials and Methods: Utilizing genomic data from the UK Biobank and IEU OpenGWAS databases, a two-sample MR analysis was executed. Twenty-six genetic variants strongly associated with BT were selected as instrumental variables (IVs) to assess their link with AA risk. Various MR methods were employed for consistency checks, including sensitivity analyses for heterogeneity and horizontal pleiotropy. Results: Using a combination of MR methods, we identified a significant causal relationship between BT and the risk of AA. Specifically, the MR analysis revealed that higher levels of BT were associated with an increased risk of AA. Sensitivity analyses, including heterogeneity tests and assessments for horizontal pleiotropy, confirmed the robustness of these findings. The IVs used in the analysis demonstrated a strong association with BT and showed no evidence of significant heterogeneity or horizontal pleiotropy, indicating the validity of the causal inference. Conclusion: This study, employing two-sample MR for the first time, confirms a causal relationship between BT levels and the risk of AA. These findings provide preliminary evidence of the causal relationship between BT and AA and may offer new insights into the pathophysiological mechanism of AA and future therapeutic strategies.

Annotation-free prediction of microbial dioxygen utilization.

Aerobes require dioxygen (O2) to grow; anaerobes do not. However, nearly all microbes-aerobes, anaerobes, and facultative organisms alike-express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O2-utilizing enzymes, for example. These effects permit high-quality prediction of O2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content-e.g., triplets of amino acids-perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or "sense," pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology-an organism's capacity to engage metabolically with its environment-may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

Deep mining reveals the diversity of endogenous viral elements in vertebrate genomes

Integration of viruses into host genomes can give rise to endogenous viral elements (EVEs), which provide insights into viral diversity, host range and evolution. A systematic search for EVEs is becoming computationally challenging given the available genomic data. We used a cloud-computing approach to perform a comprehensive search for EVEs in the kingdoms Shotokuvirae and Orthornavirae across vertebrates. We identified 2,040 EVEs in 295 vertebrate genomes and provide evidence for EVEs belonging to the families Chuviridae, Paramyxoviridae, Nairoviridae and Benyviridae. We also find an EVE from the Hepacivirus genus of flaviviruses with orthology across murine rodents. In addition, our analyses revealed that reptarenaviruses and filoviruses probably acquired their glycoprotein ectodomains three times independently from retroviral elements. Taken together, these findings encourage the addition of 4 virus families and the Hepacivirus genus to the growing virus fossil record of vertebrates, providing key insights into their natural history and evolution.

Artificial Intelligence and Omics in Malignant Gliomas.

The availability of large scale multi-omics data requires development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large genomic, transcriptomic, proteomic, and metabolomic data. Machine learning (ML) is the most significant AI algorithm in health sciences have exploded, specifically due to the recent progress made by deep learning. Although the use of AI/ML tools in GBM-omics is still at an early stage, a comprehensive discussion of how AI can be used to unravel various aspects of GBM (intratumor heterogeneity, biomarker discovery, survival prediction, and treatment optimization) would be highly relevant to both researchers and clinicians. Here, we aim to review the different AI-based techniques that have been used to study GBM pathogenesis using multi-omics data over the last decade. We first summarize different types of GBM related omics resources that can be used to develop AI models. We then discuss various AI applications for multi-omics data in order to enhance GBM precision medicine. Finally, we discuss the technical and ethical challenges that limit its application and ways to improve its implementation in clinics.

Recent and Recurrent Autopolyploidization Fueled Diversification of Snow Carp on the Tibetan Plateau.

Whole-genome duplication (WGD), or polyploidization, is a major contributor to biodiversity. However, the establishment and survival of WGDs are often considered to be stochastic, since elucidating the processes of WGD establishment remains challenging. In the current study, we explored the processes leading to polyploidy establishment in snow carp (Cyprinidae: Schizothoracinae), a predominant component of the ichthyofauna of the Tibetan Plateau and its surrounding areas. Using large-scale genomic data from isoform sequencing, we analyzed ohnolog genealogies and divergence in hundreds to thousands of gene families across major snow carp lineages. Our findings demonstrated that independent autopolyploidization subsequent to speciation was prevalent, while autopolyploidization followed by speciation also occurred in the diversification of snow carp. This was further supported by matrilineal divergence and drainage evolution evidence. Contrary to the long-standing hypothesis that ancient polyploidization preceded the diversification of snow carp, we determined that polyploidy in extant snow carp was established by recurrent autopolyploidization events during the Pleistocene. These findings indicate that the diversification of extant snow carp resembles a coordinated duet: first, the uplift of the Tibetan Plateau orchestrated the biogeography and diversification of their diploid progenitors; then, the extensive Pliocene-Pleistocene climate changes acted as relay runners, further fueling diversification through recurrent autopolyploidization. Overall, this study not only reveals a hitherto unrecognized recent WGD lineage in vertebrates but also advances current understanding of WGD processes, emphasizing that WGD establishment is a nonstochastic event, emerging from numerous adaptations to environmental challenges and recurring throughout evolutionary history rather than merely in plants.

ParthenoGenius: A User-Friendly Heuristic for Inferring Presence and Mechanism of Facultative Parthenogenesis from Genetic and Genomic Data Sets.

Facultative parthenogenesis (FP), or asexual reproduction by sexually-reproducing female animals, has been reported across several clades of vertebrates and is increasingly being recognized as a reproductive mechanism with significant implications for the genetic variation of captive and wild populations. The definitive identification of parthenogens requires molecular confirmation, with large genomic data sets necessary to accurately parse the parthenogenetic mechanism (i.e., endoduplication, gametic duplication, terminal fusion automixis, or central fusion automixis). Current methods for inferring FP from large genomic data sets are statistically intensive, require competency in R scripting for their execution, and are not designed for detection of facultative parthenogenesis or screening of large numbers of mother/offspring pairs, whereas small data sets (i.e., microsatellites) that can be evaluated visually lack the power to discriminate among FP mechanisms. Here, we present the user-friendly software program, ParthenoGenius, that uses intuitive logic to infer presence and mechanism of FP from even large genomic data sets comprising many mothers and offspring. ParthenoGenius runs relatively quickly and does not require the researcher to have knowledge of R scripting or statistics. ParthenoGenius was tested on eight empirical data sets, and in each case identified parthenogens (and parthenogenic mechanism when present) consistent with results of previous studies or corroborating evidence. ParthenoGenius will facilitate the rapid screening of large genomic data sets comprising many mothers and offspring for the presence and mechanism of parthenogenesis, improving our understanding of the frequency and phylogenetic distribution of FP across the animal kingdom.

Genetic diversity loss in the Anthropocene will continue long after habitat destruction ends.

Genetic diversity within species is the basis for evolutionary adaptive capacity and has recently been included as a target for protection in the United Nations' Global Biodiversity Framework (GBF). However, there is a lack of reliable large-scale predictive frameworks to quantify how much genetic diversity has already been lost, let alone to quantitatively predict future losses under different conservation scenarios in the 21st century. Combining spatio-temporal population genetic theory with population genomic data of 18 plant and animal species, we studied the dynamics of genetic diversity after habitat area losses. We show genetic diversity reacts slowly to habitat area and population declines, but lagged losses will continue for many decades even after habitats are fully protected. To understand the magnitude of this problem, we combined our predictive method with species' habitat area and population monitoring reported in the Living Planet Index, the Red List, and new GBF indicators. We then project genetic diversity loss in 13,808 species with a short-term genetic diversity loss of 13 - 22% and long-term loss of 42 - 48% with substantial deviations depending on the level of habitat fragmentation. These results highlight that protection of only current habitats is insufficient to ensure the genetic health of species and that continuous genetic monitoring alone likely underestimates long term impacts. We provide an area-based spatio-temporal predictive framework to develop quantitative scenarios of global genetic biodiversity.

Demographic History and Adaptive Evolution of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) in Western Australia.

Demographic processes can substantially affect a species' response to changing ecological conditions, necessitating the combined consideration of genetic responses to environmental variables and neutral genetic variation. Using a seascape genomics approach combined with population demographic modelling, we explored the interplay of demographic and environmental factors that shaped the current population structure in Indo-Pacific bottlenose dolphins (Tursiops aduncus) along the Western Australian coastline. We combined large-scale environmental data gathered via remote sensing with RADseq genomic data from 133 individuals at 19 sampling sites. Using population genetic and outlier detection analyses, we identified three distinct genetic clusters, coinciding with tropical, subtropical and temperate provincial bioregions. In contrast to previous studies, our demographic models indicated that populations occupying the paleo-shoreline split into two demographically independent lineages before the last glacial maximum (LGM). A subsequent split after the LGM 12-15 kya gave rise to the Shark Bay population, thereby creating the three currently observed clusters. Although multi-locus heterozygosity declined from north to south, dolphins from the southernmost cluster inhabiting temperate waters had higher heterozygosity in potentially adaptive loci compared to dolphins from subtropical and tropical waters. These findings suggest ongoing adaptation to cold-temperate waters in the southernmost cluster, possibly linked to distinct selective pressures between the different bioregions. Our study demonstrated that in the marine realm, without apparent physical boundaries, only a combined approach can fully elucidate the intricate environmental and genetic interactions shaping the evolutionary trajectory of marine mammals.

A Low Transformed Tubal Rank Tensor Model Using a Spatial-Tubal Constraint for Sample Clustering with Cancer Multi-omics Data

Background: Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed. Objective: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer. Method: In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively. Results: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. result: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. Conclusion: The LTTRT method achieves precise segmentation of different types of cancer samples.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Diversity, functional classification and genotyping of SHV β-lactamases in Klebsiella pneumoniae.

Interpreting the phenotypes of bla SHV alleles in Klebsiella pneumoniae genomes is complex. Whilst all strains are expected to carry a chromosomal copy conferring resistance to ampicillin, they may also carry mutations in chromosomal bla SHV alleles or additional plasmid-borne bla SHV alleles that have extended-spectrum β-lactamase (ESBL) activity and/or β-lactamase inhibitor (BLI) resistance activity. In addition, the role of individual mutations/a changes is not completely documented or understood. This has led to confusion in the literature and in antimicrobial resistance (AMR) gene databases [e.g. the National Center for Biotechnology Information (NCBI) Reference Gene Catalog and the β-lactamase database (BLDB)] over the specific functionality of individual sulfhydryl variable (SHV) protein variants. Therefore, the identification of ESBL-producing strains from K. pneumoniae genome data is complicated. Here, we reviewed the experimental evidence for the expansion of SHV enzyme function associated with specific aa substitutions. We then systematically assigned SHV alleles to functional classes (WT, ESBL and BLI resistant) based on the presence of these mutations. This resulted in the re-classification of 37 SHV alleles compared with the current assignments in the NCBI's Reference Gene Catalog and/or BLDB (21 to WT, 12 to ESBL and 4 to BLI resistant). Phylogenetic and comparative genomic analyses support that (i) SHV-1 (encoded by bla SHV-1) is the ancestral chromosomal variant, (ii) ESBL- and BLI-resistant variants have evolved multiple times through parallel substitution mutations, (iii) ESBL variants are mostly mobilized to plasmids and (iv) BLI-resistant variants mostly result from mutations in chromosomal bla SHV. We used matched genome-phenotype data from the KlebNET-GSP AMR Genotype-Phenotype Group to identify 3999 K. pneumoniae isolates carrying one or more bla SHV alleles but no other acquired β-lactamases to assess genotype-phenotype relationships for bla SHV. This collection includes human, animal and environmental isolates collected between 2001 and 2021 from 24 countries. Our analysis supports that mutations at Ambler sites 238 and 179 confer ESBL activity, whilst most omega-loop substitutions do not. Our data also provide support for the WT assignment of 67 protein variants, including 8 that were noted in public databases as ESBL. These eight variants were reclassified as WT because they lack ESBL-associated mutations, and our phenotype data support susceptibility to third-generation cephalosporins (SHV-27, SHV-38, SHV-40, SHV-41, SHV-42, SHV-65, SHV-164 and SHV-187). The approach and results outlined here have been implemented in Kleborate v2.4.1 (a software tool for genotyping K. pneumoniae), whereby known and novel bla SHV alleles are classified based on causative mutations. Kleborate v2.4.1 was updated to include ten novel protein variants from the KlebNET-GSP dataset and all alleles in public databases as of November 2023. This study demonstrates the power of sharing AMR phenotypes alongside genome data to improve the understanding of resistance mechanisms.

PneumoBrowse 2: an integrated visual platform for curated genome annotation and multiomics data analysis of Streptococcus pneumoniae.

Streptococcus pneumoniae is an opportunistic human pathogen responsible for high morbidity and mortality rates. Extensive genome sequencing revealed its large pangenome, serotype diversity, and provided insight into genome dynamics. However, functional genome analysis has lagged behind, as that requires detailed and time-consuming manual curation of genome annotations and integration of genomic and phenotypic data. To remedy this, PneumoBrowse was presented in 2018, a user-friendly interactive online platform, which provided the detailed annotation of the S. pneumoniae D39V genome, alongside transcriptomic data. Since 2018, many new studies on S. pneumoniae genome biology and protein functioning have been performed. Here, we present PneumoBrowse 2 (https://veeninglab.com/pneumobrowse), fully rebuilt in JBrowse 2. We updated annotations for transcribed and transcriptional regulatory features in the D39V genome. We added genome-wide data tracks for high-resolution chromosome conformation capture (Hi-C) data, chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-Seq), ribosome profiling, CRISPRi-seq gene essentiality data and more. Additionally, we included 18 phylogenetically diverse S. pneumoniae genomes and their annotations. By providing easy access to diverse high-quality genome annotations and links to other databases (including UniProt and AlphaFold), PneumoBrowse 2 will further accelerate research and development into preventive and treatment strategies, through increased understanding of the pneumococcal genome.