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Long-read-metagenomic sequencing is the best method for analyzing the sinus microbiome. 16s-rRNA-sequencing (both long and short read) results in PCR amplification bias that significantly distorts the sinus microbiome.
BACKGROUND: Pathogenic variants in OTOF are a major cause of auditory synaptopathy. However, challenges remain in interpreting OTOF variants, including difficulties in confirming haplotype phasing using traditional short-read sequencing (SRS) due to the large gene size, the potential incomplete penetrance of certain variants, and difficulties in assessing variants at non-canonical splice sites. This study aims to revisit the genetic landscape of OTOF variants in a Taiwanese non-syndromic auditory neuropathy spectrum disorder (ANSD) cohort using a combination of sequencing technologies, predictive tools, and experimental validations. METHODS: We performed SRS to analyze OTOF variants in 65 unrelated Taiwanese patients diagnosed with non-syndromic ANSD, complemented by long-read sequencing (LRS) for haplotype phasing. A prediction-to-validation pipeline was implemented to assess the pathogenicity of cryptic variants using SpliceAI software and minigene assays. RESULTS: Biallelic pathogenic OTOF variants were identified in 33 patients (50.8%), while monoallelic variants were found in five patients. Three novel variants, c.3864G > A (p.Ala1288 =), c.4501G > A (p.Ala1501Thr), and c.5813 + 2T > C, were detected. The pathogenicity of two non-canonical mis-splicing variants, c.3894 + 5G > C and c.3864G > A (p.Ala1288 =), was confirmed by minigene assays. LRS-based haplotype phasing revealed that the common missense variant c.5098G > C (p.Glu1700Gln) and the novel variant c.5975A > G (p.Lys1992Arg) are in cis and form a founder pathogenic allele in the Taiwanese population. CONCLUSIONS: Our study highlights the genetic heterogeneity of DFNB9 and emphasizes the importance of population-specific variant interpretation. The integration of advanced sequencing technologies, predictive algorithms, and functional validation assays will improve the accuracy of molecular diagnosis and inform personalized treatment strategies for individuals with DFNB9.
Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50, is rising across high- and middle-income settings whilst organised screening stays anchored to older age thresholds. Blood-based liquid biopsy, combined with machine learning, is the most plausible route to early detection in this group because it does not depend on bowel preparation, endoscopy capacity, or adherence to stool-based testing. The gap is structural: incidence climbs fastest in the population below the age at which any guideline-endorsed modality is offered. The analytical challenge is that early-stage tumour-derived signals in plasma are low in abundance and distributed across heterogeneous molecular layers: circulating tumour DNA mutations, aberrant methylation, cfDNA fragmentomics, and small non-coding RNA. Machine learning converts these into a single calibrated probability. This review examines where artificial intelligence (AI)-driven liquid biopsy genuinely adds diagnostic value in EOCRC, distinguishes components in which learned models are decorative from those in which they are mechanistically necessary, and identifies the validation deficit separating research cohorts from deployable clinical tools. It summarises the first-generation tools used clinically for early detection and post-treatment monitoring, then considers analytes from exosome-bound microRNAs to long-read whole-genome sequencing of circulating plasma DNA, which reads cytosine modification natively, resolves methylation and fragmentation on single molecules, and characterises structural events short reads cannot anchor. Any analyte can feed a learned model, but more diverse input yields better discrimination. The central argument is that approved, guideline-included blood tests were validated in populations aged 45 and above, and their performance in younger patients cannot be assumed.
Influenza A viruses (IAVs) remain a major global health threat, affecting both human and animal populations. Whole-genome sequencing is essential for monitoring viral evolution, zoonotic transmission, and emerging variants. However, conventional RT-PCR methods often result in incomplete gene coverage, amplification biases, and reduced sequencing accuracy, particularly in clinical samples. We developed a robust In-house method for IAV full-genome sequencing using the Oxford Nanopore Technologies (ONT) long-read sequencing platform. This method integrates an in-house multisegment Reverse Transcription PCR (RT-PCR) method with a streamlined 2-pool primer design targeting all eight IAV gene segments. RNA extracted from clinical and stock virus samples was reverse-transcribed and amplified using Superscript IV-based chemistry, followed by magnetic bead purification to ensure high-quality amplicons. Sequencing libraries were prepared with the Native Barcoding Kit 24 (SQK-NBD114.24) and sequenced on R10.4.1 flow cells on the MinION MK1C device. Data analysis using the Iterative Refinement Meta-Assembler (IRMA) confirmed improved read depth, uniform coverage, and complete genome recovery. Compared to conventional methods, our In-House Multisegment 2-Pool (IH-MS2P) RT-PCR method generated higher numbers of matched read counts, minimized chimeric artifacts, and delivered superior genome coverage across human, swine, and avian isolates. This optimized RT-PCR method provides a high-performance, time-efficient, and portable solution for influenza genomics, demonstrating robust applicability even with clinical samples of low RNA yield.
The Oxford Nanopore (ONT) MinION enables sequencing of longer DNA/RNA fragments compared to other sequencers, such as Illumina, etc. This nanopore method provides distinct advantages for generating complete genome assemblies from microorganisms. Specifically, the R9.4 flow cells used for MinION sequencing have much lower error rates compared with earlier versions of the ONT platform. Coupled with base calling using Dorado software, higher-quality long reads can now be generated for complete bacterial genome assembly. In this chapter, we describe a detailed MinION method to assemble a complete genome from a microorganism, polish the final assembly, and evaluate the genome quality using various software tools. Because of the low cost for MinION sequencing, this platform could be an asset for virtually any laboratory interested in generating complete genomes from microorganisms.
We established primary fibroblast cultures from a Japanese raccoon dog (Nyctereutes viverrinus) and a Japanese red fox (Vulpes vulpes japonica) and generated highly contiguous reference genome assemblies using Oxford Nanopore Technologies PromethION long-read sequencing. The Japanese raccoon dog assembly spanned 2.69 Gb in 813 scaffolds, with a scaffold N50 of 52 Mb and a Benchmarking Universal Single-Copy Orthologs (BUSCO) completeness score of 98.2%. The Japanese red fox assembly spanned 2.47 Gb in 903 scaffolds, with a scaffold N50 of 139 Mb and a BUSCO completeness score of 97.5%. Phylogenomic analysis placed the Japanese raccoon dog in a lineage distinct from the continental raccoon dog, supporting its evolutionary differentiation within Nyctereutes. The Japanese red fox formed a distinct lineage within the red fox clade, consistent with its recognized regional differentiation. These genome assemblies and associated fibroblast cultures provide resources for studies of canid systematics, population history, local adaptation, comparative genome evolution, and conservation genetics.
Long-read sequencing, paralog-aware variant calling, and telomere-to-telomere (T2T) human genome assemblies now enable the resolution of copy-, haplotype-, and nucleotide-level complexities in segmentally duplicated loci, which were previously inaccessible with short-read sequencing. In this review, we highlight how current technologies and analysis methods reveal extensive diversity in copy number (CN), structure, and gene conversion within the spinal muscular atrophy-associated survival motor neuron (SMN) locus. We summarize how understanding population-level structural variation could be translated into clinical practice, where a nucleotide-level view of the SMN locus may refine prognostic accuracy beyond SMN2 CN and explain variable treatment responses. Finally, we discuss how the approaches and methodologies required to study the SMN locus may be applied elsewhere, providing a scaffold to characterize other complex human genetic regions.
In metazoans, gene expression is typically regulated by a cis-regulatory landscape (CRL) composed of a promoter and multiple enhancers. How these cis-regulatory elements (CREs) coordinate their function across large genomic distances remains unclear. For example, is the simultaneous activation of multiple enhancers required to promote transcription? Here, we combined single-molecule footprinting with long-read sequencing to quantify how often chromatin accessibility and transcription factor binding co-occur across entire CRLs in the Drosophila genome. Analysis of thousands of individual DNA molecules at each locus revealed that CREs form a specific network with shared single-molecule chromatin accessibility profiles. Co-accessibility is not limited to adjacent CREs and is frequently observed between CREs brought into proximity by chromatin looping. Co-accessible CREs exhibit strong coordination in their cell-type-specific accessibility, linking enhancer activity with transcriptional activation. Our data uncover dependencies between CREs genome-wide and suggest that coordinated enhancer activation is a widespread mechanism regulating gene expression.
Oxford Nanopore Technologies (ONT) sequencing offers several advantages for metagenomics, including long reads, rapid turnaround, low upfront cost, scalability and portability. However, for ONT metagenomics, DNA yield, quality and integrity are important considerations when selecting an extraction method. Many metagenomic extraction methods use harsh lysis conditions to extract a wide range of species and provide an accurate community composition, but these conditions can compromise DNA fragment length. Therefore, extraction methods for ONT metagenomics must balance DNA shearing and recovery with representative community lysis. We systematically evaluated DNA extraction methods for ONT metagenomic sequencing using a use case-oriented framework. Among nearly 50 extraction methods screened, 7 were selected for detailed comparison based on suitability for metagenomics, variation in methodology, availability, cost and processing time: Norgen BioTek Corp's Stool DNA Isolation (NG), Zymo Research's ZymoBIOMICS Quick-DNA HMW MagBead (ZMG), Qiagen's DNeasy Blood and Tissue (QBT), Macherey-Nagel's NucleoMag DNA Microbiome (MN), Zymo Research's ZymoBIOMICS DNA Mini Prep (ZMI), Qiagen's DNeasy PowerSoil/QIAamp PowerFecal Pro (PS) and Qiagen's QIAamp Fast DNA Stool Mini (QIA). Methods were tested using Zymo Research's ZymoBIOMICS Microbial Community Standard (MCS), a matrix-free mock community with known composition. DNA extracts were sequenced on an ONT PromethION using the Rapid Barcoding Kit, except QIA due to insufficient DNA yield. Metrics for the method, DNA extracts, sequencing and genomes were evaluated, revealing trade-offs between methods. The two magnetic bead methods, MN and ZMG, produced the highest mean read length N50 values (13.9 and 16.5 kb, respectively) but showed apparent community compositions skewed towards Gram-negative bacteria. In contrast, ZMI and PS maintained a community composition close to expected, with reduced mean read length N50 values (4.5 vs. 7.5 kb). Performance across various metrics is presented in the context of the following use cases: maximizing genome coverage and assembly completeness, preserving composition accuracy, targeting specific species and limiting required resources (equipment, time or budget). The metrics and use case considerations presented offer practical guidance for informed selection of DNA extraction methods for ONT metagenomics. For accurate community composition, ZMI or PS are recommended, while PS and ZMG perform best at maximizing genome coverage and assembly completeness. NG and QBT may be the most economical options, though performance trade-offs were observed. Finally, PS may be the preferred method for time-sensitive diagnostic or field applications.
The Micrapis subgenus, which includes the black dwarf honey bee (Apis andreniformis) and the red dwarf honey bee (Apis florea), remains underrepresented in genomic studies despite its ecological significance. Here, we present high-quality de novo genome assemblies for both species, generated using a hybrid sequencing approach combining Oxford Nanopore Technologies long reads with Illumina short reads. The final assemblies are highly contiguous, with contig N50 values of 5.0 Mb (A. andreniformis) and 4.3 Mb (A. florea), representing a major improvement over the previously published A. florea genome. Genome completeness assessments indicate high quality, with BUSCO scores exceeding 98.5% using the Hymenoptera database and k-mer analyses supporting base-level accuracy. Repeat annotation revealed a relatively low repetitive sequence content (∼6%), consistent with other Apis species. Using RNA sequencing data, we annotated 12,189 genes for A. andreniformis and 12,207 genes for A. florea, with ∼98% completeness in predicted proteomes. These genome assemblies provide a valuable resource for comparative and functional genomic studies, with the potential to offer new insights into the genetic basis of dwarf honey bee adaptations.
Genetic variation within intron 3 of the CACNA1C calcium channel gene is associated with schizophrenia and other neuropsychiatric disorders, but analysis of the causal variants and their effect is complicated by a nearby variable-number tandem repeat (VNTR). Here, we explored the structure and population variability of the CACNA1C intron 3 VNTR using 155 long-read genome assemblies from 78 diverse individuals. Based on sequence differences among repeat units, we clustered individual sequences into 7 VNTR structural alleles called Types. Three Types were related through large duplications, but the other Types diverged much earlier such that only 12 repeat units at the 5' end of the VNTR were shared across most Types. The most diverged Types were rare and present only in individuals with African ancestry, but a multiallelic structural polymorphism was present across populations at different frequencies, consistent with expansion of the VNTR preceding the emergence of early hominins. We demonstrated that this polymorphism was in complete linkage disequilibrium with fine-mapped schizophrenia variants from genome-wide association studies (GWAS), and that this risk haplotype was associated with decreased CACNA1C gene expression in the brain. Our work suggests that sequence variation within a human-specific VNTR affects gene expression, and provides a detailed characterization of new alleles at a flagship neuropsychiatric locus.
Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.
COX4I1 gene encodes cytochrome c oxidase subunit 4 isoform 1, involved in the early assembly stages of mitochondrial respiratory chain complex IV. To date, COX4I1 pathogenic variants have been reported in only a few cases, each exhibiting heterogeneous clinical phenotypes and limited functional data. Here, we describe the fourth reported case of COX4I1 deficiency associated with human disease, expanding the phenotypic and genetic spectrum of this rare mitochondrial disorder and providing novel clinical, molecular, and functional data. The herein reported individual presented with progressive deterioration of motor skills, intellectual disability and brain imaging abnormalities compatible with Leigh syndrome. Genetic studies combining short and long read next generation sequencing uncovered a peculiar genetic combination in this patient, harboring a de novo COX4I1 nonsense substitution in trans with an inherited deep intronic variant (c.[64C>T];[73+1511A>G]; p.[Arg22Ter];[Glu25ValfsTer9]). Functional studies performed in patient's tissues and transiently transfected cell lines demonstrated that the identified variants mainly exert their pathogenic effect by targeting COX4I1 protein levels, thereby impairing the proper assembly and activity of complex IV.Additionally, proteomic data in patient's fibroblasts suggested an underlying pathomechanism that involves not only the regulation of complex IV function but also the levels of mitoribosomal proteins. In summary, our findings shed light to clarify some of the main clinical features associated with COX4I1 deficiency and the molecular mechanisms involved in the pathogenesis of this disorder.
Down syndrome, the most common form of human intellectual disability, is caused by nondisjunction and chromosome 21 trisomy (T21). Small centromeres have been hypothesized to contribute to its aetiology and studies on mammals suggest that larger centromeres are more efficiently transmitted, yet complete sequencing of chromosome 21 (chr21) centromeres has been particularly challenging. Using long-read sequencing, we sequenced and assembled the centromeres from eight families that include a child with free T21 (1 trio, 6 child-mother duos, and 1 singleton) all resulting from maternal meiosis I errors. Two of these families carry the smallest chr21 centromeres (143 and 181 kbp) observed in female individuals to date, exhibiting a ~10.7- and ~19.4-fold centromeric α-satellite higher-order repeat array size difference between the maternally inherited homologs, respectively. In both cases, the longer centromere harbors a poorly defined centromere dip region, marked by DNA hypomethylation, in the proband but not in the mother. A comparison of all proband chr21 centromeres (n=24) to those of controls (n=261) shows that small centromeres are not enriched in families with T21 (p-value=0.73); contrarily, chr21 extreme centromere size asymmetry (>10-fold) is unique of T21 (p-value=0.003), suggesting that this feature may represent a genetic risk factor for a subset of families with free T21. Additionally, phylogenetic reconstruction reveals that human chr21 has been particularly prone to such variation with some of the biggest size differences occurring over the last ~17 thousand years of human evolution.
Cryptococcus neoformans is a pathogenic yeast that is the causative agent of cryptococcal meningitis. While it is well known that the genotype of C. neoformans impacts patient outcomes, the reason for this association has not been well elucidated. In this study, we examined the relationship between two subpopulations in the sequence type 93 clade of C. neoformans: ST93A and ST93B. We found extensive linkage disequilibrium (LD) among the single nucleotide polymorphisms (SNPs) that differentiate ST93A from ST93B. We also found differences in the extent of linkage among SNPs within each subpopulation; LD was more extensive within ST93B than ST93A. SNPs associated with virulence were in long-range linkage disequilibrium with less frequency than recurrent SNPs not associated with virulence. We investigated the karyotype of ST93A and ST93B using contour-clamped gel electrophoresis and long-read sequencing and found that the extensive long-range linkage was not due to chromosomal rearrangements. Overall, we found that the two subpopulations in ST93 are driven by SNPs in LD. We additionally found that recurrent SNPs associated with virulence were less frequently evolutionarily linked and were two times more likely to be independent, congruent mutations rather than tied to phylogeny.IMPORTANCECryptococcus neoformans is an important pathogen that is widely distributed and ubiquitous in the environment. The majority of the human population has a latent, controlled infection suggesting that C. neoformans is uniquely adapted to cause infection. In spite of this, the reason C. neoformans is a pathogen remains unknown; interestingly, most environmental isolates are avirulent but are genetically very similar to disease-causing virulent isolates. Recent evidence from genome-wide association studies shows that small mutations in key virulence-associated genes are associated with the virulence of specific isolates. The data presented here provide an evolutionary framework for those small mutations. The mutations that impact disease are not being collected over long-term evolution. The mutations may instead occur independently during infection. Identifying these genes that are more likely to be mutated during infection will be fundamental for understanding C. neoformans virulence.
Whereas genome sequencing and assembly technologies are improving, cost can still be prohibitive for plant species with large, complex genomes. As a consequence, genomics work on some taxa in evolutionarily pivotal positions in the vascular plant tree of life has been hampered. The species-rich genus Ranunculus (Ranunculaceae) is an important angiosperm group for the study of polyploidy, apomixis, and reticulate evolution. However, neither mitochondrial nor high-quality nuclear genome sequences are available. This limits phylogenomic, functional, and taxonomic analyses thus far. Here, we tested Illumina short-read, Oxford Nanopore Technology (ONT) and PacBio (HiFi) long-read, and hybrid-read assembly strategies. We sequenced the diploid progenitor species R. cassubicifolius (R. auricomus species complex) and selected the best assemblies in terms of completeness, contiguity, and quality scores. We first assembled the plastome (156 kbp, 85 genes) and mitogenome (1.18 Mbp, 40 genes) sequences using Illumina and Illumina-PacBio-hybrid strategies, respectively. We also present an updated plastome and the first mitogenome phylogeny of Ranunculaceae, including studies of gene loss (e.g., infA, ycf15, or rps) with evolutionary implications. For the nuclear genome sequence, we favored a PacBio-based assembly polished three times with filtered short reads and subsequently scaffolded into eight pseudochromosomes by chromatin conformation data (Hi-C). We obtained a haploid genome sequence of 2.69 Gbp, with 94.1% complete BUSCO genes found and 35 482 annotated genes, and inferred ancient gene duplications compared to existing Ranunculales genomes. The genomic information presented here will enable advanced evolutionary-functional analyses for the species complex, but also for the genus and beyond Ranunculaceae.
MOTIVATION: Spliced alignment refers to the alignment of messenger RNA (mRNA) or protein sequences to eukaryotic genomes. It plays a critical role in gene annotation and the study of gene functions. Accurate spliced alignment demands sophisticated modeling of splice sites, but current aligners use simple models, which may affect their accuracy given dissimilar sequences. RESULTS: We implemented minisplice to learn splice signals with a one-dimensional convolutional neural network (1D-CNN) and trained a model with 7,026 parameters for vertebrate and insect genomes. It captures conserved splice signals across phyla and reveals GC-rich introns specific to mammals and birds. We used this model to estimate the empirical splicing probability for every GT and AG in genomes, and modified minimap2 and miniprot to leverage pre-computed splicing probability during alignment. Evaluation on human long-read RNA-seq data and cross-species protein datasets showed our method greatly improves the junction accuracy especially for noisy long RNA-seq reads and proteins of distant homology. AVAILABILITY AND IMPLEMENTATION: https://github.com/lh3/minisplice.
SUMMARY: Accurate annotation of coding sequences and translational features within transcript models is essential for interpreting assembled transcriptomes and their functional potential. Existing open reading frame (ORF) prediction tools typically operate on transcript FASTA files and do not reintegrate coding sequence (CDS) information back into transcript models, limiting their utility in long-read sequencing workflows where GTF/GFF annotations are the primary output. We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file. In addition, ORFannotate provides biologically informative translational context by annotating Kozak sequence strength, detecting non-overlapping upstream ORFs (uORFs) with coding probabilities, characterising 5' and 3' untranslated regions (UTRs), and predicting nonsense-mediated decay (NMD) susceptibility. All annotations are consolidated in a transcript-level summary to support downstream analysis. By generating GTF files with accurate CDS annotations, ORFannotate facilitates reproducible analysis of both long- and short-read transcriptomes and integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows. ORFannotate is fast, scalable and provides a practical solution for transcriptome annotation beyond coding potential prediction alone. AVAILABILITY AND IMPLEMENTATION: ORFannotate is implemented in Python and freely available under the GNU General Public License v3 (GPL-3.0) at: https://github.com/egustavsson/ORFannotate (DOI: https://doi.org/10.5281/zenodo.16812866).