Rapid identification and subsequent contextualization of an outbreak of methicillin-resistant Staphylococcus aureus in a neonatal intensive care unit using nanopore sequencing.

Outbreaks of methicillin-resistant Staphylococcus aureus (MRSA) are well described in the neonatal intensive care unit (NICU) setting. Genomics has revolutionized the investigation of such outbreaks; however, to date, this has largely been completed retrospectively and has typically relied on short-read platforms. In 2022, our laboratory established a prospective genomic surveillance system using Oxford Nanopore Technologies sequencing for rapid outbreak detection. Herein, using this system, we describe the detection and control of an outbreak of sequence-type (ST)97 MRSA in our NICU. The outbreak was identified 13 days after the first MRSA-positive culture and at a point where there were only two known cases. Ward screening rapidly defined the extent of the outbreak, with six other infants found to be colonized. There was minimal transmission once the outbreak had been detected and appropriate infection control measures had been instituted; only two further ST97 cases were detected, along with three unrelated non-ST97 MRSA cases. To contextualize the outbreak, core-genome single-nucleotide variants were identified for phylogenetic analysis after de novo assembly of nanopore data. Comparisons with global (n=45) and national surveillance (n=35) ST97 genomes revealed the stepwise evolution of methicillin resistance within this ST97 subset. A distinct cluster comprising nine of the ten ST97-IVa genomes from the NICU was identified, with strains from 2020 to 2022 national surveillance serving as outgroups to this cluster. One ST97-IVa genome presumed to be part of the outbreak formed an outgroup and was retrospectively excluded. A second phylogeny was created using Illumina sequencing, which considerably reduced the branch lengths of the NICU isolates on the phylogenetic tree. However, the overall tree topology and conclusions were unchanged, with the exception of the NICU outbreak cluster, where differences in branch lengths were observed. This analysis demonstrated the ability of a nanopore-only prospective genomic surveillance system to rapidly identify and contextualize an outbreak of MRSA in a NICU.

Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing.

DNA methylation plays an important role in various biological processes, including cell differentiation, ageing, and cancer development. The most important methylation in mammals is 5-methylcytosine mostly occurring in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing successfully detect 5-methylcytosine DNA modifications. However, they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish, a deep learning algorithm that significantly improves read-level 5-methylcytosine detection by using Nanopore sequencing. Rockfish is compared with other methods based on Nanopore sequencing on R9.4.1 and R10.4.1 datasets. There is an increase in the single-base accuracy and the F1 measure of up to 5 percentage points on R.9.4.1 datasets, and up to 0.82 percentage points on R10.4.1 datasets. Moreover, Rockfish shows a high correlation with whole-genome bisulfite sequencing, requires lower read depth, and achieves higher confidence in biologically important regions such as CpG-rich promoters while being computationally efficient. Its superior performance in human and mouse samples highlights its versatility for studying 5-methylcytosine methylation across varied organisms and diseases. Finally, its adaptable architecture ensures compatibility with new versions of pores and chemistry as well as modification types.

LongTR: genome-wide profiling of genetic variation at tandem repeats from long reads.

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve tandem repeat analysis, especially for long or complex repeats. Here, we introduce LongTR, which accurately genotypes tandem repeats from high-fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr and https://zenodo.org/doi/10.5281/zenodo.11403979 .

First Molecular Detection and Epidemiological Analysis of Equine Influenza Virus in Two Regions of Colombia, 2020-2023.

Equine influenza is a viral disease caused by the equine influenza virus (EIV), and according to the WOAH, it is mandatory to report these infections. In Latin America and Colombia, EIV risk factors have not been analyzed. The objective of this research is to perform an epidemiological and molecular analysis of the EIV in horses with respiratory symptoms from 2020 to 2023 in Colombia. Molecular EIV detection was performed using RT-qPCR and nanopore sequencing. A risk analysis was also performed via the GEE method. A total of 188 equines with EIV respiratory symptoms were recruited. The positivity rate was 33.5%. The descriptive analysis showed that only 12.8% of the horses were vaccinated, and measures such as the quarantine and isolation of symptomatic animals accounted for 91.5% and 88.8%, respectively. The variables associated with the EIV were the non-isolation of positive individuals (OR = 8.16, 95% CI (1.52-43.67), p = 0.014) and sharing space with poultry (OR = 2.16, 95% CI (1.09-4.26), p = 0.027). In conclusion, this is the first EIV investigation in symptomatic horses in Colombia, highlighting the presence of the virus in the country and the need to improve preventive and control measures.

From SARS-CoV-2 to Global Preparedness: A Graphical Interface for Standardised High-Throughput Bioinformatics Analysis in Pandemic Scenarios and Surveillance of Drug Resistance.

The COVID-19 pandemic highlighted the need for a rapid, convenient, and scalable diagnostic method for detecting a novel pathogen amidst a global pandemic. While command-line interface tools offer automation for SARS-CoV-2 Oxford Nanopore Technology sequencing data analysis, they are inapplicable to users with limited programming skills. A solution is to establish such automated workflows within a graphical user interface software. We developed two workflows in the software Geneious Prime 2022.1.1, adapted for data obtained from the Midnight and Artic's nCoV-2019 sequencing protocols. Both workflows perform trimming, read mapping, consensus generation, and annotation on SARS-CoV-2 Nanopore sequencing data. Additionally, one workflow includes phylogenetic assignment using the bioinformatic tools pangolin and Nextclade as plugins. The basic workflow was validated in 2020, adhering to the requirements of the European Centre for Disease Prevention and Control for SARS-CoV-2 sequencing and analysis. The enhanced workflow, providing phylogenetic assignment, underwent validation at Uppsala University Hospital by analysing 96 clinical samples. It provided accurate diagnoses matching the original results of the basic workflow while also reducing manual clicks and analysis time. These bioinformatic workflows streamline SARS-CoV-2 Nanopore data analysis in Geneious Prime, saving time and manual work for operators lacking programming knowledge.

Borg extrachromosomal elements of methane-oxidizing archaea have conserved and expressed genetic repertoires.

Borgs are huge extrachromosomal elements (ECE) of anaerobic methane-consuming "Candidatus Methanoperedens" archaea. Here, we used nanopore sequencing to validate published complete genomes curated from short reads and to reconstruct new genomes. 13 complete and four near-complete linear genomes share 40 genes that define a largely syntenous genome backbone. We use these conserved genes to identify new Borgs from peatland soil and to delineate Borg phylogeny, revealing two major clades. Remarkably, Borg genes encoding nanowire-like electron-transferring cytochromes and cell surface proteins are more highly expressed than those of host Methanoperedens, indicating that Borgs augment the Methanoperedens activity in situ. We reconstructed the first complete 4.00 Mbp genome for a Methanoperedens that is inferred to be a Borg host and predicted its methylation motifs, which differ from pervasive TC and CC methylation motifs of the Borgs. Thus, methylation may enable Methanoperedens to distinguish their genomes from those of Borgs. Very high Borg to Methanoperedens ratios and structural predictions suggest that Borgs may be capable of encapsulation. The findings clearly define Borgs as a distinct class of ECE with shared genomic signatures, establish their diversification from a common ancestor with genetic inheritance, and raise the possibility of periodic existence outside of host cells.

Construction of a Full-Length Transcriptome of Western Honeybee Midgut Tissue and Improved Genome Annotation.

Honeybees are an indispensable pollinator in nature with pivotal ecological, economic, and scientific value. However, a full-length transcriptome for Apis mellifera, assembled with the advanced third-generation nanopore sequencing technology, has yet to be reported. Here, nanopore sequencing of the midgut tissues of uninoculated and Nosema ceranae-inoculated A. mellifera workers was conducted, and the full-length transcriptome was then constructed and annotated based on high-quality long reads. Next followed improvement of sequences and annotations of the current reference genome of A. mellifera. A total of 5,942,745 and 6,664,923 raw reads were produced from midguts of workers at 7 days post-inoculation (dpi) with N. ceranae and 10 dpi, while 7,100,161 and 6,506,665 raw reads were generated from the midguts of corresponding uninoculated workers. After strict quality control, 6,928,170, 6,353,066, 5,745,048, and 6,416,987 clean reads were obtained, with a length distribution ranging from 1 kb to 10 kb. Additionally, 16,824, 17,708, 15,744, and 18,246 full-length transcripts were respectively detected, including 28,019 nonredundant ones. Among these, 43,666, 30,945, 41,771, 26,442, and 24,532 full-length transcripts could be annotated to the Nr, KOG, eggNOG, GO, and KEGG databases, respectively. Additionally, 501 novel genes (20,326 novel transcripts) were identified for the first time, among which 401 (20,255), 193 (13,365), 414 (19,186), 228 (12,093), and 202 (11,703) were respectively annotated to each of the aforementioned five databases. The expression and sequences of three randomly selected novel transcripts were confirmed by RT-PCR and Sanger sequencing. The 5' UTR of 2082 genes, the 3' UTR of 2029 genes, and both the 5' and 3' UTRs of 730 genes were extended. Moreover, 17,345 SSRs, 14,789 complete ORFs, 1224 long non-coding RNAs (lncRNAs), and 650 transcription factors (TFs) from 37 families were detected. Findings from this work not only refine the annotation of the A. mellifera reference genome, but also provide a valuable resource and basis for relevant molecular and -omics studies.

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads.

MOTIVATION: Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. RESULTS: Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. AVAILABILITY AND IMPLEMENTATION: The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

Using cerebrospinal fluid nanopore sequencing assay to diagnose tuberculous meningitis: a retrospective cohort study in China.

OBJECTIVE: This study aimed to evaluate the efficiency of nanopore sequencing for the early diagnosis of tuberculous meningitis (TBM) using cerebrospinal fluid and compared it with acid-fast bacilli (AFB) smear, mycobacterial growth indicator tube culture and Xpert Mycobacterium tuberculosis (MTB)/rifampicin (RIF). DESIGN: Single-centre retrospective study. SETTING: The Tuberculosis Diagnosis and Treatment Center of Zhejiang Chinese and Western Medicine Integrated Hospital. PARTICIPANTS: We enrolled 64 adult patients with presumptive TBM admitted to our hospital from August 2021 to August 2023. METHODS: We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of AFB smear, culture, Xpert MTB/RIF and nanopore sequencing to evaluate their diagnostic efficacy compared with a composite reference standard for TBM. RESULTS: Among these 64 patients, all tested negative for TBM by AFB smear. The sensitivity, specificity, PPV and NPV were 11.11%, 100%, 100% and 32.2% for culture, 13.33%, 100%, 100% and 2.76% for Xpert MTB/RIF, and 77.78%, 100%, 100% and 65.52% for nanopore sequencing, respectively. CONCLUSION: The diagnostic accuracy of the nanopore sequencing test was significantly higher than that of conventional testing methods used to detect TBM.

Nanopore sequencing for smear-negative pulmonary tuberculosis-a multicentre prospective study in China.

PURPOSE: In this prospective study, the diagnosis accuracy of nanopore sequencing-based Mycobacterium tuberculosis (MTB) detection was determined through examining bronchoalveolar lavage fluid (BALF) samples from pulmonary tuberculosis (PTB) -suspected patients. Compared the diagnostic performance of nanopore sequencing, mycobacterial growth indicator tube (MGIT) culture and Xpert MTB/rifampin resistance (MTB/RIF) assays. METHODS: Specimens collected from suspected PTB cases across China from September 2021 to April 2022 were tested then assay diagnostic accuracy rates were compared. RESULTS: Among the 111 suspected PTB cases that were ultimately diagnosed as PTB, the diagnostic rate of nanopore sequencing was statistically significant different from other assays (P < 0.05). Fleiss' kappa values of 0.219 and 0.303 indicated fair consistency levels between MTB detection results obtained using nanopore sequencing versus other assays, respectively. Respective PTB diagnostic sensitivity rates of MGIT culture, Xpert MTB/RIF and nanopore sequencing of 36.11%, 40.28% and 83.33% indicated superior sensitivity of nanopore sequencing. Analysis of area under the curve (AUC), Youden's index and accuracy values and the negative predictive value (NPV) indicated superior MTB detection performance for nanopore sequencing (with Xpert MTB/RIF ranking second), while the PTB diagnostic accuracy rate of nanopore sequencing exceeded corresponding rates of the other methods. CONCLUSIONS: In comparison with MGIT culture and Xpert MTB/RIF assays, BALF's nanopore sequencing provided superior MTB detection sensitivity and thus is suitable for testing of sputum-scarce suspected PTB cases. However, negative results obtained using these assays should be confirmed based on additional evidence before ruling out a PTB diagnosis.

Internal mRNA 2'-O-methyl mapping by nanopore sequencing and consequence on mRNA stability and role in cancer.

In this issue, Li et al.1 report internal mRNA 2'-O-methyl (Nm) modification mapping by nanopore sequencing and the effect of Nm on mRNA stability and cancer cell progression.

2'-O-methylation at internal sites on mRNA promotes mRNA stability.

2'-O-methylation (Nm) is a prominent RNA modification well known in noncoding RNAs and more recently also found at many mRNA internal sites. However, their function and base-resolution stoichiometry remain underexplored. Here, we investigate the transcriptome-wide effect of internal site Nm on mRNA stability. Combining nanopore sequencing with our developed machine learning method, NanoNm, we identify thousands of Nm sites on mRNAs with a single-base resolution. We observe a positive effect of FBL-mediated Nm modification on mRNA stability and expression level. Elevated FBL expression in cancer cells is associated with increased expression levels for 2'-O-methylated mRNAs of cancer pathways, implying the role of FBL in post-transcriptional regulation. Lastly, we find that FBL-mediated 2'-O-methylation connects to widespread 3' UTR shortening, a mechanism that globally increases RNA stability. Collectively, we demonstrate that FBL-mediated Nm modifications at mRNA internal sites regulate gene expression by enhancing mRNA stability.

NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data.

Human leukocyte antigen (HLA) typing is of great importance in clinical applications such as organ transplantation, blood transfusion, disease diagnosis and treatment, and forensic analysis. In recent years, nanopore sequencing technology has emerged as a rapid and cost-effective option for HLA typing. However, due to the principles and data characteristics of nanopore sequencing, there was a scarcity of robust and generalizable bioinformatics tools for its downstream analysis, posing a significant challenge in deciphering the thousands of HLA alleles present in the human population. To address this challenge, we developed NanoHLA as a tool for high-resolution typing of HLA class I genes without error correction based on nanopore sequencing. The method integrated the concepts of HLA type coverage analysis and the data conversion techniques employed in Nano2NGS, which was characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. In validation with public nanopore sequencing datasets, NanoHLA showed an overall concordance rate of 84.34% for HLA-A, HLA-B, and HLA-C, and demonstrated superior performance in comparison to existing tools such as HLA-LA. NanoHLA provides tools and solutions for use in HLA typing related fields, and look forward to further expanding the application of nanopore sequencing technology in both research and clinical settings. The code is available at https://github.com/langjidong/NanoHLA .

Direct RNA sequencing of astronaut blood reveals spaceflight-associated m6A increases and hematopoietic transcriptional responses.

The advent of civilian spaceflight challenges scientists to precisely describe the effects of spaceflight on human physiology, particularly at the molecular and cellular level. Newer, nanopore-based sequencing technologies can quantitatively map changes in chemical structure and expression at single molecule resolution across entire isoforms. We perform long-read, direct RNA nanopore sequencing, as well as Ultima high-coverage RNA-sequencing, of whole blood sampled longitudinally from four SpaceX Inspiration4 astronauts at seven timepoints, spanning pre-flight, day of return, and post-flight recovery. We report key genetic pathways, including changes in erythrocyte regulation, stress induction, and immune changes affected by spaceflight. We also present the first m6A methylation profiles for a human space mission, suggesting a significant spike in m6A levels immediately post-flight. These data and results represent the first longitudinal long-read RNA profiles and RNA modification maps for each gene for astronauts, improving our understanding of the human transcriptome's dynamic response to spaceflight.

Exploring the potential of Oxford Nanopore Technologies sequencing for Mycobacterium tuberculosis sequencing: An assessment of R10 flowcells and V14 chemistry.

Oxford Nanopore Technologies (ONT) sequencing is a promising technology. We assessed the performance of the new ONT R10 flowcells and V14 rapid sequencing chemistry for Mtb whole genome sequencing of Mycobacterium tuberculosis (Mtb) DNA extracted from clinical primary liquid cultures (CPLCs). Using the recommended protocols for MinION Mk1C, R10.4.1 MinION flowcells, and the ONT Rapid Sequencing Kit V14 on six CPLC samples, we obtained a pooled library yield of 10.9 ng/µl, generated 1.94 Gb of sequenced bases and 214k reads after 48h in a first sequencing run. Only half (49%) of all generated reads met the Phred Quality score threshold (>8). To assess if the low data output and sequence quality were due to impurities present in DNA extracted directly from CPLCs, we added a pre-library preparation bead-clean-up step and included purified DNA obtained from an Mtb subculture as a control sample in a second sequencing run. The library yield for DNA extracted from four CPLCs and one Mtb subculture (control) was similar (10.0 ng/µl), 2.38 Gb of bases and 822k reads were produced. The quality was slightly better with 66% of the produced reads having a Phred Quality >8. A third run of DNA from six CPLCs with bead clean-up pre-processing produced a low library yield (±1 Gb of bases, 166k reads) of low quality (51% of reads with a Phred Quality score >8). A median depth of coverage above 10× was only achieved for five of 17 (29%) sequenced libraries. Compared to Illumina WGS of the same samples, accurate lineage predictions and full drug resistance profiles from the generated ONT data could not be determined by TBProfiler. Further optimization of the V14 ONT rapid sequencing chemistry and library preparation protocol is needed for clinical Mtb WGS applications.

Diagnostic accuracy of nanopore sequencing for the rapid diagnosis of pulmonary tuberculosis: A protocol for a systematic review and meta-analysis.

BACKGROUND: Pulmonary tuberculosis (PTB) is the most common type of tuberculosis (TB). Rapid diagnosis of PTB can help in TB control. Although the use of molecular tests (such as the GeneXpert MTB/RIF) has improved the ability to rapidly diagnose PTB, there is still room for improvement. Nanopore sequencing is a novel means of rapid TB detection. The purpose of this study was to establish a systematic review and meta-analysis protocol for evaluating the accuracy of nanopore sequencing for the rapid diagnosis of PTB. METHODS: We completed this protocol according to the Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement and registered on the PROSPERO platform. We will screen studies related to nanopore sequencing for diagnosis of PTB by searching through PubMed, EMBASE, the Cochrane Library using English, and Wanfang database, CNKI (China National Knowledge Infrastructure) using Chinese. Eligible studies will be screened according to the inclusion and exclusion criteria established in the study protocol. We will evaluate the methodological quality of the individual included studies using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). We will use Stata (version 15.0) with the midas command and RevMan (version 5.3) for meta-analysis and forest plots and SROC curves generation. A p < 0.05 was treated as a statistically significant difference. When significant heterogeneity exists between studies, we will explore sources of heterogeneity through meta-regression analysis and subgroup analysis. CONCLUSION: To the best of our knowledge, this will be the first systematic review and meta-analysis of nanopore sequencing for the diagnosis of PTB. We hope that this study will find a new and effective tool for the early diagnosis of PTB. PROSPERO REGISTRATION NUMBER: CRD42023495593.

Sigmoni: classification of nanopore signal with a compressed pangenome index.

SUMMARY: Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10-100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. AVAILABILITY AND IMPLEMENTATION: Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.

Effective training of nanopore callers for epigenetic marks with limited labelled data.

Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been trained on data that span all modifications on all possible DNA [Formula: see text]-mer backgrounds-a complete training dataset. However, as nanopore technology is pushed to more and more epigenetic modifications, such complete training data will not be feasible to obtain. Nanopore calling has historically been performed with hidden Markov models (HMMs) that cannot make successful calls for [Formula: see text]-mer contexts not seen during training because of their independent emission distributions. However, deep neural networks (DNNs), which share parameters across contexts, are increasingly being used as callers, often outperforming their HMM cousins. It stands to reason that a DNN approach should be able to better generalize to unseen [Formula: see text]-mer contexts. Indeed, herein we demonstrate that a common DNN approach (DeepSignal) outperforms a common HMM approach (Nanopolish) in the incomplete data setting. Furthermore, we propose a novel hybrid HMM-DNN approach, amortized-HMM, that outperforms both the pure HMM and DNN approaches on 5mC calling when the training data are incomplete. This type of approach is expected to be useful for calling other base modifications such as 5-hydroxymethylcytosine and for the simultaneous calling of different modifications, settings in which complete training data are not likely to be available.

Identification of transcriptionally-active human papillomavirus integrants through nanopore sequencing reveals viable targets for gene therapy against cervical cancer.

Integration of the human papillomavirus (HPV) genome into the cellular genome is a key event that leads to constitutive expression of viral oncoprotein E6/E7 and drives the progression of cervical cancer. However, HPV integration patterns differ on a case-by-case basis among related malignancies. Next-generation sequencing technologies still face challenges for interrogating HPV integration sites. In this study, utilizing Nanopore long-read sequencing, we identified 452 and 108 potential integration sites from the cervical cancer cell lines (CaSki and HeLa) and five tissue samples, respectively. Based on long Nanopore chimeric reads, we were able to analyze the methylation status of the HPV long control region (LCR), which controls oncogene E6/E7 expression, and to identify transcriptionally-active integrants among the numerous integrants. As a proof of concept, we identified an active HPV integrant in between RUNX2 and CLIC5 on chromosome 6 in the CaSki cell line, which was supported by ATAC-seq, H3K27Ac ChIP-seq, and RNA-seq analysis. Knockout of the active HPV integrant, by the CRISPR/Cas9 system, dramatically crippled cell proliferation and induced cell senescence. In conclusion, identifying transcriptionally-active HPV integrants with Nanopore sequencing can provide viable targets for gene therapy against HPV-associated cancers.

Digital telomere measurement by long-read sequencing distinguishes healthy aging from disease.

Telomere length is an important biomarker of organismal aging and cellular replicative potential, but existing measurement methods are limited in resolution and accuracy. Here, we deploy digital telomere measurement (DTM) by nanopore sequencing to understand how distributions of human telomere length change with age and disease. We measure telomere attrition and de novo elongation with up to 30 bp resolution in genetically defined populations of human cells, in blood cells from healthy donors and in blood cells from patients with genetic defects in telomere maintenance. We find that human aging is accompanied by a progressive loss of long telomeres and an accumulation of shorter telomeres. In patients with defects in telomere maintenance, the accumulation of short telomeres is more pronounced and correlates with phenotypic severity. We apply machine learning to train a binary classification model that distinguishes healthy individuals from those with telomere biology disorders. This sequencing and bioinformatic pipeline will advance our understanding of telomere maintenance mechanisms and the use of telomere length as a clinical biomarker of aging and disease.