Genomic Datasets Research Articles

Identification of cis-sQTL demonstrates genetic associations and functional implications of inflammatory processes in Nelore cattle muscle tissue.

This study aimed to identify splicing quantitative trait loci (cis-sQTL) in Nelore cattle muscle tissue and explore the involvement of spliced genes (sGenes) in immune system-related biological processes. Genotypic data from 80 intact male Nelore cattle were obtained using SNP-Chip technology, while RNA-Seq analysis was performed to measure gene expression levels, enabling the integration of genomic and transcriptomic datasets. The normalized expression levels of spliced transcripts were associated with single nucleotide polymorphisms (SNPs) through an analysis of variance using an additive linear model with the MatrixEQTL package. A permutation analysis then assessed the significance of the best SNPs for each spliced transcript. Functional enrichment analysis was performed on the sGenes to investigate their roles in the immune system. In total, 3,187 variants were linked to 3,202 spliced transcripts, with 83 sGenes involved in immune system processes. Of these, 31 sGenes were enriched for five transcription factors. Most cis-sQTL effects were found in intronic regions, with 27 sQTL variants associated with disease susceptibility and resistance in cattle. Key sGenes identified, such as GSDMA, NLRP6, CASP6, GZMA, CASP4, CASP1, TREM2, NLRP1, and NAIP, were related to inflammasome formation and pyroptosis. Additionally, genes like PIDD1, OPTN, NFKBIB, STAT1, TNIP3, and TREM2 were involved in regulating the NF-kB pathway. These findings lay the groundwork for breeding disease-resistant cattle and enhance our understanding of genetic mechanisms in immune responses.

Phylogeography and genetic diversity of Ulmus elongata (Ulmaceae), a Tertiary relict tree with extremely small populations (PSESP)

BackgroundAssessing the current status and identifying the mechanisms threatening endangered plants are significant challenges and fundamental to biodiversity conservation, particularly for protecting Tertiary relict trees and plant species with extremely small populations (PSESP). Ulmus elongata (Ulmus, Ulmaceae) with high values for the ornamental application, is a Tertiary relict tree species and one of the members from PSESP in China. Currently, the wild populations of U. elongata have been threatened by excessive deforestation and urbanization, but limited knowledges of its genetic diversity seriously hinder conservation efforts. Therefore, a further study on the genetic diversity and drivers of genetic pattern in U. elongata is crucial for preserving genetic resources and can serve as a reference for other Tertiary relict plants and PSESP under climate change.ResultsHere, a total of 12 populations from 70 individuals of U. elongata were collected covering its geographical distribution in China. Utilizing chloroplast genome datasets, we found that U. elongata exhibited remarkably low nucleotide diversity and gene flow (π = 0.00013, Nm = 0.03). Analysis of molecular variance (AMOVA) showed that genetic variation in U. elongata occurs mainly between eight clades (60.95%). The Mantel tests indicated a significant correlation between genetic differentiation and geographical distances (r = 0.3777, p < 0.05) in U. elongata populations. A notable phylogeographic structure was identified in U. elongata, comprising eight distinct haplogroups (NST = 0.917, GST = 0.876, p < 0.05), which was attributed to the global cooling in the East Asia and Quaternary climate oscillations.ConclusionsOverall, our study using Ulmus elongata as a representative supported the hypothesis that plants belonging to Tertiary relict species and PSESP simultaneously exhibits significantly lower genetic diversity compared to those are either Tertiary relict species or PSESP individually. Furthermore, the low genetic diversity and significant genetic differentiation in U. elongata populations can be primarily ascribed to a combination of factors, including habitat fragmentation resulting from human activities, populations contraction during LGM and small population sizes. This provides a crucial foundation for guiding conservation efforts and implementing management strategies for other Tertiary relict tree species and PSESP. Our findings also provide evidence for the important roles of East Asian monsoon system and climate oscillations in shaping the phylogeographic pattern in subtropical broad-leaved forests.

Microfluidic and Computational Tools for Neurodegeneration Studies.

Understanding the molecular, cellular, and physiological components of neurodegenerative diseases (NDs) is paramount for developing accurate diagnostics and efficacious therapies. However, the complexity of ND pathology and the limitations associated with conventional analytical methods undermine research. Fortunately, microfluidic technology can facilitate discoveries through improved biomarker quantification, brain organoid culture, and small animal model manipulation. Because this technology can increase experimental throughput and the number of metrics that can be studied in concert, it demands more sophisticated computational tools to process and analyze results. Advanced analytical algorithms and machine learning platforms can address this challenge in data generated from microfluidic systems, but they can also be used outside of devices to discern patterns in genomic, proteomic, anatomical, and cognitive data sets. We discuss these approaches and their potential to expedite research discoveries and improve clinical outcomes through ND characterization, diagnosis, and treatment platforms.

3D genomic features across &gt;50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci, we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18, and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 – an inflammation-responsive gene in nerve nociceptors – was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Pangenome mining of the Streptomyces genus redefines species’ biosynthetic potential

BackgroundStreptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives.ResultsWe perform pangenome analysis on 2371 Streptomyces genomes, including approximately 1200 complete assemblies. Employing a data-driven approach based on genome similarities, the Streptomyces genus was classified into 7 primary and 42 secondary Mash-clusters, forming the basis for comprehensive pangenome mining. A refined workflow for grouping biosynthetic gene clusters (BGCs) redefines their diversity across different Mash-clusters. This workflow also reassigns 2729 known BGC families to only 440 families, a reduction caused by inaccuracies in BGC boundary detections. When the genomic location of BGCs is included in the analysis, a conserved genomic structure, or synteny, among BGCs becomes apparent within species and Mash-clusters. This synteny suggests that vertical inheritance is a major factor in the diversification of BGCs.ConclusionsOur analysis of a genomic dataset at a scale of thousands of genomes refines predictions of BGC diversity using Mash-clusters as a basis for pangenome analysis. The observed conservation in the order of BGCs’ genomic locations shows that the BGCs are vertically inherited. The presented workflow and the in-depth analysis pave the way for large-scale pangenome investigations and enhance our understanding of the biosynthetic potential of the Streptomyces genus.

Genomic Profiling of Biliary Tract Cancers: Comprehensive Assessment of Anatomic and Geographic Heterogeneity, Co-Alterations and Outcomes.

Biliary tract cancers (BTCs) represent distinct biological and genomic entities. Anatomic and geographic heterogeneity in genomic profiling of BTC subtypes, genomic co-alterations, and their impact on long-term outcomes are not well defined. Genomic data to characterize alterations among patients with BTCs were derived from the AACR GENIE registry (v15.1) and other genomic data sets. Patterns of mutational co-occurrence, frequency of co-alterations, and their impact on long-term outcomes among BTC patients were examined. Alterations in IDH1 and FGFR2 genes were mostly noted among intrahepatic cholangiocarcinoma (iCCA) samples, TP53, ERBB2/HER2, and SMAD4 mutations were more frequent among gallbladder cancer (GBC) samples while extrahepatic cholangiocarcinoma (eCCA) more commonly harbored KRAS mutations (all Q < 0.001). Alterations in IDH1 and FGFR2 genes were more frequent among iCCA samples from Western vs. Eastern populations, while KRAS, SMAD4, and ERBB2 mutations were more commonly observed among Eastern populations(all Q < 0.05). FGFR2 gene was frequently co-mutated with BAP1 (log2OR: 1.55, Q < 0.001), while IDH1 gene was commonly co-mutated with PBRM1 (log2OR: 1.09, Q < 0.001). Co-alteration rate among patients with IDH1-mutant iCCAs, FGFR2-rearranged iCCAs, KRAS-mutant eCCA, and HER2-mutant GBCs were 80.8%, 85.2%, 76.7%, and 100%, respectively. Among patients with iCCA and FGFR2 fusions/rearrangements, harboring co-alterations in the TP53 pathway or PI3K pathway correlated with worse overall survival (OS), while patients with IDH1-mutant iCCA had worse OS when harboring co-alterations in the cell cycle pathway. Marked genomic heterogeneity exists among patients with BTCs based on anatomic and geographic location. The overwhelming majority of BTC patients with clinically significant mutations had concurrent genomic co-alterations. The current study highlights the molecular complexity of BTCs with multiple alterations that commonly co-exist and could potentially be targeted to treat BTCs.

The mutational landscape of Staphylococcus aureus during colonisation

Staphylococcus aureus is an important human pathogen and a commensal of the human nose and skin. Survival and persistence during colonisation are likely major drivers of S. aureus evolution. Here we applied a genome-wide mutation enrichment approach to a genomic dataset of 3060 S. aureus colonization isolates from 791 individuals. Despite limited within-host genetic diversity, we observed an excess of protein-altering mutations in metabolic genes, in regulators of quorum-sensing (agrA and agrC) and in known antibiotic targets (fusA, pbp2, dfrA and ileS). We demonstrated the phenotypic effect of multiple adaptive mutations in vitro, including changes in haemolytic activity, antibiotic susceptibility, and metabolite utilisation. Nitrogen metabolism showed the strongest evidence of adaptation, with the assimilatory nitrite reductase (nasD) and urease (ureG) showing the highest mutational enrichment. We identified a nasD natural mutant with enhanced growth under urea as the sole nitrogen source. Inclusion of 4090 additional isolate genomes from 731 individuals revealed eight more genes including sasA/sraP, darA/pstA, and rsbU with signals of adaptive variation that warrant further characterisation. Our study provides a comprehensive picture of the heterogeneity of S. aureus adaptive changes during colonisation, and a robust methodological approach applicable to study in host adaptive evolution in other bacterial pathogens.

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources. We argue that these models are the equivalent of foundation models in natural language processing in their utility, as they encode within them chromatin state in its different aspects, providing useful representations that allow quick deployment of accurate models of gene regulation. We demonstrate this premise by leveraging the recently created Sei model to develop simple, interpretable models of intron retention, and demonstrate their advantage over models based on the DNA language model DNABERT-2. Our work also demonstrates the impact of chromatin state on the regulation of intron retention. Using representations learned by Sei, our model is able to discover the involvement of transcription factors and chromatin marks in regulating intron retention, providing better accuracy than a recently published custom model developed for this purpose.

A phylogenetic approach to comparative genomics.

Comparative genomics, whereby the genomes of different species are compared, has the potential to address broad and fundamental questions at the intersection of genetics and evolution. However, species, genomes and genes cannot be considered as independent data points within statistical tests. Closely related species tend to be similar because they share genes by common descent, which must be accounted for in analyses. This problem of non-independence may be exacerbated when examining genomes or genes but can be addressed by applying phylogeny-based methods to comparative genomic analyses. Here, we review how controlling for phylogeny can change the conclusions of comparative genomics studies. We address common questions on how to apply these methods and illustrate how they can be used to test causal hypotheses. The combination of rapidly expanding genomic datasets and phylogenetic comparative methods is set to revolutionize the biological insights possible from comparative genomic studies.

Neuropathology-based approach reveals novel Alzheimer's Disease genes and highlights female-specific pathways and causal links to disrupted lipid metabolism: insights into a vicious cycle

Dementia refers to an umbrella phenotype of many different underlying pathologies with Alzheimer’s disease (AD) being the most common type. Neuropathological examination remains the gold standard for accurate AD diagnosis, however, most that we know about AD genetics is based on Genome-Wide Association Studies (GWAS) of clinically defined AD. Such studies have identified multiple AD susceptibility variants with a significant portion of the heritability unexplained and highlighting the phenotypic and genetic heterogeneity of the clinically defined entity. Furthermore, despite women’s increased susceptibility to dementia, there is a lack of sex-specific genetic studies and understanding of sex-specific background for the disorder. Here, we aim to tackle the heterogeneity of AD by specifically concentrating on neuropathological features and pursuing sex-specific analysis. We bring together 14 different genomic and neuropathology datasets (6960 individuals) and we integrate our GWAS findings with transcriptomic and phenotypic data aiming to also identify biomarkers for AD progression. We uncover novel genetic associations to AD neuropathology, including BIN1 and OPCML. Our sex-specific analysis points to a role for BIN1 specifically in women as well as novel AD loci including QRFPR and SGCZ. Post-GWAS analyses illuminate the functional and biological mechanisms underlying AD and reveal sex-specific differences. Finally, through PheWAS and Mendelian Randomization analysis, we identify causal links with AD neuropathology pointing to disrupted lipid metabolism, as well as impaired peripheral immune response and liver dysfunction as part of a vicious cycle that fuels neurodegeneration.

Phylogenomic insights into the taxonomy, ecology, and mating systems of the lorchel family Discinaceae (Pezizales, Ascomycota).

Lorchels, also known as false morels (Gyromitra sensu lato), are iconic due to their brain-shaped mushrooms and production of gyromitrin, a deadly mycotoxin. Molecular phylogenetic studies have hitherto failed to resolve deep-branching relationships in the lorchel family, Discinaceae, hampering our ability to settle longstanding taxonomic debates and to reconstruct the evolution of toxin production. We generated 75 draft genomes from cultures and ascomata (some collected as early as 1960), conducted phylogenomic analyses using 1542 single-copy orthologs to infer the early evolutionary history of lorchels, and identified genomic signatures of trophic mode and mating-type loci to better understand lorchel ecology and reproductive biology. Our phylogenomic tree was supported by high gene tree concordance, facilitating taxonomic revisions in Discinaceae. We recognized 10 genera across two tribes: tribe Discineae (Discina, Maublancomyces, Neogyromitra, Piscidiscina, and Pseudodiscina) and tribe Gyromitreae (Gyromitra, Hydnotrya, Paragyromitra, Pseudorhizina, and Pseudoverpa); Piscidiscina was newly erected and 26 new combinations were formalized. Paradiscina melaleuca and Marcelleina donadinii formed their own family-level clade sister to Morchellaceae, which merits further taxonomic study. Genome size and CAZyme content were consistent with a mycorrhizal lifestyle for the truffle species (Hydnotrya spp.), whereas the other Discinaceae genera possessed genomic properties of a saprotrophic habit. Lorchels were found to be predominantly heterothallic-either MAT1-1 or MAT1-2-but a single occurrence of colocalized mating-type idiomorphs indicative of homothallism was observed in Gyromitra esculenta strain CBS101906 and requires additional confirmation and follow-up study. Lastly, we confirmed that gyromitrin has a phylogenetically discontinuous distribution, having been detected exclusively in two distantly related genera (Gyromitra and Piscidiscina) belonging to separate tribes. Our genomic dataset will facilitate further investigations into the gyromitrin biosynthesis genes and their evolutionary history. With additional sampling of Geomoriaceae and Helvellaceae-two closely related families with no publicly available genomes-these data will enable comprehensive studies on the independent evolution of truffles and ecological diversification in an economically important group of pezizalean fungi.

Epigenomic and 3D genomic mapping reveals developmental dynamics and subgenomic asymmetry of transcriptional regulatory architecture in allotetraploid cotton

Although epigenetic modification has long been recognized as a vital force influencing gene regulation in plants, the dynamics of chromatin structure implicated in the intertwined transcriptional regulation of duplicated genes in polyploids have yet to be understood. Here, we document the dynamic organization of chromatin structure in two subgenomes of allotetraploid cotton (Gossypium hirsutum) by generating 3D genomic, epigenomic and transcriptomic datasets from 12 major tissues/developmental stages covering the life cycle. We systematically identify a subset of genes that are closely associated with specific tissue functions. Interestingly, these genes exhibit not only higher tissue specificity but also a more pronounced homoeologous bias. We comprehensively elucidate the intricate process of subgenomic collaboration and divergence across various tissues. A comparison among subgenomes in the 12 tissues reveals widespread differences in the reorganization of 3D genome structures, with the Dt subgenome exhibiting a higher extent of dynamic chromatin status than the At subgenome. Moreover, we construct a comprehensive atlas of putative functional genome elements and discover that 37 cis-regulatory elements (CREs) have selection signals acquired during domestication and improvement. These data and analyses are publicly available to the research community through a web portal. In summary, this study provides abundant resources and depicts the regulatory architecture of the genome, which thereby facilitates the understanding of biological processes and guides cotton breeding.

Selection signatures associated with adaptation in South African Drakensberger, Nguni, and Tuli beef breeds

In the present study 1,709 cattle, including 1,118 Drakensberger (DRB), 377 Nguni (NGI), and 214 Tuli (TUL), were genotyped using the GeneSeek® Genomic Profiler™ 150 K bovine SNP panel. A genomic data set of 122,632 quality-filtered single nucleotide polymorphisms (SNPs) were used to identify selection signatures within breeds based on conserved runs of homozygosity (ROH) and heterozygosity (ROHet) estimated with the detectRUNS R package. The mean number of ROH per animal varied across breeds ranging from 36.09 ± 12.82 (NGI) to 51.82 ± 21.01 (DRB), and the mean ROH length per breed ranged between 2.31 Mb (NGI) and 3.90 Mb (DRB). The smallest length categories i.e., ROH < 4 Mb were most frequent, indicating historic inbreeding effects for all breeds. The ROH based inbreeding coefficients (FROH) ranged between 0.033 ± 0.024 (NGI) and 0.081 ± 0.046 (DRB). Genes mapped to candidate regions were associated with immunity (ADAMTS12, LY96, WDPCP) and adaptation (FKBP4, CBFA2T3, TUBB3) in cattle and genes previously only reported for immunity in mice and human (EXOC3L1, MYO1G). The present study contributes to the understanding of the genetic mechanisms of adaptation, providing information for potential molecular application in genetic evaluation and selection programs.

Sharing of cmeRABC alleles between C. coli and C. jejuni associated with extensive drug resistance in Campylobacter isolates from infants and poultry in the Peruvian Amazon.

Campylobacter is a serious health threat because of the rapid progressive evolution of antimicrobial resistance and efficient transmission from zoonotic as well as human sources. Resistance to fluoroquinolones and macrolides is particularly concerning as this compromises the two most effective oral antibiotic agents currently available for human campylobacteriosis. Here, we report on the prevalence and worldwide distribution of the operon cmeRABC, which encodes an efflux pump conferring high levels of combined resistance to fluoroquinolones and macrolides in Campylobacter strains isolated from poultry (n = 75) and children (n = 177). These mutations were found to be highly prevalent in isolates from poultry (62.7%) and children (29.4%) in Iquitos, Peru. We investigated the population structure of genes in the cmeRABC operon and identified a potential genetic bottleneck for the cmeA and cmeB genes. While most cmeB alleles segregate by species, alleles associated with high resistance to fluoroquinolones and macrolides were found in both Campylobacter jejuni and Campylobacter coli. We inferred that the likely ancestry of these alleles was from C. jejuni and was later acquired by C. coli through recombination. Publicly accessible global genomic data from 16,120 Campylobacter genomes identified these mutations in approximately 6% of C. jejuni and C. coli isolates globally, with higher prevalence in samples from poultry in many countries, including Peru. Our findings suggest that these extensively drug-resistant Campylobacter strains originated from C. jejuni in poultry.IMPORTANCEAntimicrobial resistance in Campylobacter is a growing public health concern, driven by the rapid evolution and zoonotic transmission of resistant strains. This study focuses on mutations in the cmeABC efflux pump, which confer high resistance to fluoroquinolones and macrolides, the two most effective oral antibiotics for human campylobacteriosis. By analyzing genomes from poultry and children in Iquitos, Peru, as well as global genomic data sets, we identified a significant prevalence of these resistance-associated mutations, particularly in poultry and children. Our findings suggest that these mutations originated in Campylobacter jejuni and spread to C. coli through recombination. Globally, these mutations are found in approximately 6% of isolates, with higher prevalence in poultry in multiple countries. This research underscores the critical role of genomic epidemiology in understanding the origins, evolution, and dissemination of antimicrobial resistance and highlights the need to address poultry as a reservoir for resistant Campylobacter.

GeniePool 2.0: advancing variant analysis through CHM13-T2T, AlphaMissense, gnomAD V4 integration, and variant co-occurrence queries.

Originally developed to meet the challenges of genomic data deluge, GeniePool emerged as a pioneering platform, enabling efficient storage, accessibility, and analysis of vast genomic datasets, enabled due to its data lake architecture. Building on this foundation, GeniePool 2.0 advances genomic analysis through the integration of cutting-edge variant databases, such as CHM13-T2T, AlphaMissense, and gnomAD V4, coupled with the capability for variant co-occurrence queries. This evolution offers an unprecedented level of granularity and scope in genomic analyses, from enhancing our understanding of variant pathogenicity and phenotypic associations to facilitating research collaborations. The introduction of CHM13-T2T provides a more accurate reference for human genetic variation, AlphaMissense enriches the platform with protein-level impact predictions of missense mutations, and gnomAD V4 offers a comprehensive view of human genetic diversity. Additionally, the innovative feature for variant co-occurrence analysis is pivotal for exploring the combined effects of genetic variations, advancing our comprehension of compound heterozygosity, epistasis, and polygenic risk factors in disease pathogenesis. GeniePool 2.0 is a comprehensive and scalable platform, which aims to enhance genomic data analysis and contribute to genomic research, potentially supporting new discoveries and clinical innovations. Database URL: https://GeniePool.link.

Comparison of qPCR protocols for quantification of "Candidatus Saccharibacteria", belonging to the Candidate Phyla Radiation, suggests that 23S rRNA is a better target than 16S rRNA.

Candidate Phyla Radiation (CPR) is a large monophyletic group encompassing about 25% of bacterial diversity. Among CPR, "Candidatus Saccharibacteria" is one of the most clinically relevant phyla. Indeed, it is enriched in the oral microbiota of subjects suffering from immune-mediated disorders and it has been found to have immunomodulatory activities. For these reasons, it is crucial to have reliable methods to detect and quantify this bacterial lineage in human samples, including saliva. Four qPCR protocols for quantifying "Ca. Saccharibacteria" (one targeting the 23S rRNA gene and three the 16S) were tested and compared. The efficiency and coverage of these four protocols were evaluated in silico on large genomic datasets, and in vitro on salivary DNA samples, already characterized by amplicon sequencing on the V3-V4 regions of the 16S rRNA. In silico PCR analyses showed that all qPCR primers lose part of the "Ca. Saccharibacteria" genetic variability, even if the 23S qPCR primers matched more lineages than the 16S qPCR primers. In vitro qPCR experiments confirmed that all 16S-based protocols strongly underestimated "Ca. Saccharibacteria" in salivary DNA, while the 23S qPCR protocol gave quantifications more comparable to 16S amplicon sequencing. Overall, our results show that the 23S-based qPCR protocol is more precise than the 16S-based ones in quantifying "Ca. Saccharibacteria", although all protocols probably underestimate specific lineages. These results underline the current limits in quantifying "Ca. Saccharibacteria", highlighting the needs for novel experimental strategies or methods. Indeed, the underestimation of "Ca. Saccharibacteria" in clinical samples could hide its role in human health and in the development of immune-mediated diseases.

Rnalib: a Python library for custom transcriptomics analyses.

The efficient and reproducible analysis of high-throughput sequencing datasets necessitates the development of methodical and robust computational pipelines that integrate established and bespoke bioinformatics analysis tools, often written in high-level programming languages such as Python. Despite the increasing availability of programming libraries for genomics, there is a noticeable lack of tools specifically focused on transcriptomics. Key tasks in this area include the association of gene features (e.g. transcript isoforms, introns or untranslated regions) with relevant subsections of (large) genomics datasets across diverse data formats, as well as efficient querying of these data based on genomic locations and annotation attributes. To address the needs of transcriptomics data analyses, we developed rnalib, a Python library designed for creating custom bioinformatics analysis methods. Built on existing Python libraries like pysam and pyBigWig, rnalib offers random access support, enabling efficient access to relevant subregions of large, genome-wide datasets. Rnalib extends the filtering and access capabilities of these libraries and includes additional checks to prevent common errors when integrating genomics datasets. The library is centred on an object-oriented Transcriptome class that provides methods for stepwise annotation of gene features with both, local and remote data sources. The rnalib Application Programming Interface cleanly separates immutable genomic locations from associated, mutable data, and offers a wide range of methods for iterating, querying, and exporting collated datasets. Rnalib establishes a fast, readable, reproducible, and robust framework for developing novel transcriptomics data analysis tools and methods. Source code, documentation, and tutorials are available at https://github.com/popitsch/rnalib.

Dna2bit: high performance genomic distance estimation software for microbial genome analysis

dna2bit is an ultra-fast software specifically engineered for microbial genome analysis, particularly adept at calculating genome distances within metagenome and single amplified genome datasets. Distinguished from existing software such as Mash and Dashing, dna2bit employs feature hashing technique and Hamming distance to achieve enhanced speed and memory utilization, without sacrifice in the accuracy of average nucleotide identity calculations. dna2bit has promising applications in various domains such as average nucleotide identity approximation, metagenomic sequence clustering, and homology querying. dna2bit significantly boosts computational efficiency in handling large datasets including single amplified genomes, thereby facilitating a better understanding of the population heterogeneity and comparative genomics of microorganisms. dna2bit is available at https://github.com/lijuzeng/dna2bit.

K-hyperparameter tuning in high-dimensional genomics using joint optimization of deep differential evolutionary algorithm and unsupervised transfer learning from intelligent GenoUMAP embeddings

AbstractK-hyperparameter optimization in high-dimensional genomics remains a critical challenge, impacting the quality of clustering. Improved quality of clustering can enhance models for predicting patient outcomes and identifying personalized treatment plans. Subsequently, these enhanced models can facilitate the discovery of biomarkers, which can be essential for early diagnosis, prognosis, and treatment response in cancer research. Our paper addresses this challenge through a four-fold approach. Firstly, we empirically evaluate the k-hyperparameter optimization algorithms in genomics analysis using a correlation based feature selection method and a stratified k-fold cross-validation strategy. Secondly, we evaluate the performance of the best optimization algorithm in the first step using a variety of the dimensionality reduction methods applied for reducing the hyperparameter search spaces in genomics. Building on the two, we propose a novel algorithm for this optimization problem in the third step, employing a joint optimization of Deep-Differential-Evolutionary Algorithm and Unsupervised Transfer Learning from Intelligent GenoUMAP (Uniform Manifold Approximation and Projection). Finally, we compare it with the existing algorithms and validate its effectiveness. Our approach leverages UMAP pre-trained special autoencoder and integrates a deep-differential-evolutionary algorithm in tuning k. These choices are based on empirical analysis results. The novel algorithm balances population size for exploration and exploitation, helping to find diverse solutions and the global optimum. The learning rate balances iterations and convergence speed, leading to stable convergence towards the global optimum. UMAP’s superior performance, demonstrated by short whiskers and higher median values in the comparative analysis, informs its choice for training the special autoencoder in the new algorithm. The algorithm enhances clustering by balancing reconstruction accuracy, local structure preservation, and cluster compactness. The comprehensive loss function optimizes clustering quality, promotes hyperparameter diversity, and facilitates effective knowledge transfer. This algorithm’s multi-objective joint optimization makes it effective in genomics data analysis. The validation on this algorithm on three genomic datasets demonstrates superior clustering scores. Additionally, the convergence plots indicate relatively smoother curves and an excellent fitness landscape. These findings hold significant promise for advancing cancer research and computational genomics at large.

Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review

Diabetes mellitus is a long-term metabolic condition marked by high blood sugar levels due to issues with insulin production, insulin effectiveness, or a combination of both. It stands as one of the fastest-growing diseases worldwide, projected to afflict 693 million adults by 2045. The escalating prevalence of diabetes and associated health complications (kidney disease, retinopathy, and neuropathy) underscore the imperative to devise predictive models for early diagnosis and intervention. These complications contribute to increased mortality rates, blindness, kidney failure, and an overall diminished quality of life in individuals living with diabetes. While clinical risk factors and glycemic control provide valuable insights, they alone cannot reliably predict the onset of vascular complications. Genetic biomarkers and machine learning techniques have emerged as promising tools for predicting diabetes development risk and associated complications. Despite the emergence of numerous smart AI models for diabetes prediction, there is still a need for a thorough review outlining their progress and challenges. To address this gap, this paper offers a systematic review of the literature on AI-based models for diabetes identification, following the PRISMA extension for scoping reviews guidelines. Our review revealed that multimodal diabetes prediction models outperformed unimodal models. Most studies focused on classical machine learning models, with SNPs being the most used data type, followed by gene expression profiles, while lipidomic and metabolomic data were the least utilized. Moreover, some studies focused on identifying genetic determinants of diabetes complications relied on familial linkage analysis, tailored for robust effect loci. However, these approaches had limitations, including susceptibility to false positives in candidate gene studies and underpowered AI models capabilities due to sample size constraints. The landscape shifted dramatically with the proliferation of genomic datasets, fueled by the emergence of biobanks and the amalgamation of global cohorts. This surge has led to a more than twofold increase in genetic discoveries related to both diabetes and its complications using AI. Our focus here is on these genetic breakthroughs, particularly those empowered by AI models. However, we also highlight the existing gaps in research and underscore the need for further advancements to propel genomic discovery to the next level.