Computational tools for tandem repeat detection using long-read sequencing.

BackgroundLong-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies–based long-read sequencing “nanopore" platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the complex data generated by long-read sequencing platforms necessitates accompanying specialized bioinformatics platforms and tools to process the long-read data correctly. Importantly, such tools should additionally help democratize bioinformatics analysis by enabling easy access and ease-of-use solutions for researchers.ResultsThe Galaxy platform provides a user-friendly interface to computational command line–based tools, handles the software dependencies, and provides refined workflows. The users do not have to possess programming experience or extended computer skills. The interface enables researchers to perform powerful bioinformatics analysis, including the assembly and analysis of short- or long-read sequence data. The newly developed “NanoGalaxy" is a Galaxy-based toolkit for analysing long-read sequencing data, which is suitable for diverse applications, including de novo genome assembly from genomic, metagenomic, and plasmid sequence reads.ConclusionsA range of best-practice tools and workflows for long-read sequence genome assembly has been integrated into a NanoGalaxy platform to facilitate easy access and use of bioinformatics tools for researchers. NanoGalaxy is freely available at the European Galaxy server https://nanopore.usegalaxy.eu with supporting self-learning training material available at https://training.galaxyproject.org.

SummaryDespite the continuous discovery of new transcript isoforms, fueled by the recent increase in accessibility and accuracy of long-read RNA sequencing data, functional differences between isoforms originating from the same gene often remain obscure. To address this issue and enable researchers to assess potential functional consequences of transcript isoform variation on the proteome, we developed IsoTV. IsoTV is a versatile pipeline to process, predict and visualize the functional features of translated transcript isoforms. Attributes such as gene and isoform expression, transcript composition and functional features are summarized in an easy-to-interpret visualization. IsoTV is able to analyze a variety of data types from all eukaryotic organisms, including short- and long-read RNA-seq data. Using Oxford Nanopore long read data, we demonstrate that IsoTV facilitates the understanding of potential protein isoform function in different cancer cell types.Availability and implementationIsoTV is available at https://github.molgen.mpg.de/MayerGroup/IsoTV, with the corresponding documentation at https://isotv.readthedocs.io/.Supplementary informationSupplementary data are available at Bioinformatics online.

Somatic SVs significantly contribute to cancer development and progression. Characterizing these variations has traditionally been difficult due to the limitations of short-read sequencing technologies and the diverse types and lengths of SVs. The advent of long-read sequencing has enabled a more comprehensive analysis of germline SVs, highlighting its potential applications in cancer genomics. The melanoma cell line COLO829, along with its normal counterpart COLO829BL, is a standard reference for somatic SV detection. Valle-Inclan et al. recently validated 68 somatic SVs combining short-read, long-read, and linked-read sequencing data. However, the sensitivity may be compromised as only a limited number of alignment-based callers were employed, such as NanoSV, Sniffles (for Oxford Nanopore Technologies (ONT)), and pbsv for (PacBio HiFi). As more alignment-based SV callers are being developed, it is crucial to integrate a broader range of tools to construct a comprehensive call set of somatic SVs. Moreover, the performance of assembly-based methods in somatic SV detection within cancer remains largely unexplored. In this study, we aimed to establish a comprehensive spectrum of somatic SVs in the COLO829 melanoma cell line by employing both alignment-based and assembly-based methods with long-read whole-genome sequencing data from PacBio and ONT aligned with Minimap2, SVs were identified and integrated from 7 alignment-based callers: cuteSV, NanoVar, DeBreak, Sniffles2, Svision-Pro, NanoSV, and pbsv. In addition, genome assemblies were constructed via Hifiasm and Verkko, incorporating HiFi reads with Hi-C and ultra-long ONT integration, followed by SV detection using four assembly-based callers: Dipcall, PAV, SVIM-asm, and cuteSV. A dedicated pipeline was developed to identify and filter somatic SVs from germline events, which considers mapping quality, sequencing depth of SV regions and their flanking regions, and 50% reciprocal overlap. Compared to the truth set established by Valle-Inclan et al., we discovered 19 novel somatic SVs, including 10 deletions, 4 insertions, 3 duplications, and 2 inversions. As a comparison, assembly-based methods detected all the deletions, insertions and duplications found by the alignment-based methods. However, it missed 20 inversions and 6 translocations. Our findings highlight the superior sensitivity of alignment-based techniques in somatic SV detection and underscore the complementary nature of assembly-based approaches. This work enhances the benchmark for somatic SV characterization in cancer genomics and confirms the utility of integrated long-read sequencing analyses. Citation Format: Zishan Peng, Zechen Chong. Full spectrum of somatic structural variations (SVs) detection in COLO829 with long-read sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5056.

Motivationde novo variants (DNVs) are variants that are present in offspring but not in their parents. DNVs are both important for examining mutation rates as well as in the identification of disease-related variation. While efforts have been made to call DNVs, calling of DNVs is still challenging from parent–child sequenced trio data. We developed Hare And Tortoise (HAT) as an automated DNV detection workflow for highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genomics and HAT addresses this need.ResultsHAT is a computational workflow that begins with aligned read data (i.e. CRAM or BAM) from a parent–child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from Illumina short-read whole-exome sequencing, Illumina short-read whole-genome sequencing, and highly accurate PacBio HiFi long-read whole-genome sequencing data. The quality of these DNVs is high based on a series of quality metrics including number of DNVs per individual, percent of DNVs at CpG sites, and percent of DNVs phased to the paternal chromosome of origin.Availability and implementationhttps://github.com/TNTurnerLab/HAT

BackgroundAs a domesticated species vital to humans, horses are raised worldwide as a source of mechanical energy for sports, leisure, food production, and transportation. The gut microbiota plays an important role in the health, diseases, athletic performance, and behaviour of horses.ResultsHere, using approximately 2.2 Tb of metagenomic sequencing data from gut samples from 242 horses, including 110 samples from the caecum and 132 samples from the rectum (faeces), we assembled 4142 microbial metagenome-assembled genomes (MAG), 4015 (96.93%) of which appear to correspond to new species. From long-read data, we successfully assembled 13 circular whole-chromosome bacterial genomes representing novel species. The MAG contained over 313,568 predicted carbohydrate-active enzymes (CAZy), over 59.77% of which had low similarity match in CAZy public databases. High abundance and diversity of antibiotic resistance genes (ARG) were identified in the MAG, likely showing the wide use of antibiotics in the management of horse. The abundances of at least 36 MAG (e.g. MAG belonging to Lachnospiraceae, Oscillospiraceae, and Ruminococcus) were higher in racehorses than in nonracehorses. These MAG enriched in racehorses contained every gene in a major pathway for producing acetate and butyrate by fibre fermentation, presenting potential for greater amount of short-chain fatty acids available to fuel athletic performance.ConclusionOverall, we assembled 4142 MAG from short- and long-read sequence data in the horse gut. Our dataset represents an exhaustive microbial genome catalogue for the horse gut microbiome and provides a valuable resource for discovery of performance-enhancing microbes and studies of horse gut microbiome.5Q4GzktoVGndspuVBLwUREVideo

Long-read sequencing data is useful for detecting large and complex structural variations; however, technical artifacts can lead to false structural variant calls. In our analyses, we became aware of a foldback artifact in long-read data. Therefore, we developed the open-source Breakinator tool to flag putative foldback artifact reads, as well as previously known chimeric artifacts. Through an alignment-based approach, Breakinator can detect artifacts missed by existing quality control tools. We profiled the occurrences of foldbacks and chimeric reads in both Oxford Nanopore and PacBio sequences across a range of specimens, library types, sequencing chemistries, sequencing machines, and base-calling software.

Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants play an instrumental role in deciphering cellular genetic heterogeneity, but they are still difficult tasks because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, provide an opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrate that iGDA can accurately reconstruct haplotypes in closely related strains of the same species (divergence ≥0.011%) from long-read metagenomic data.

BackgroundCharacterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.ResultsWe demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.ConclusionsHS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1479-3) contains supplementary material, which is available to authorized users.

Objective:The aim of this project was to implement long-read sequencing for BCR-ABL1 TKI resistance mutation screening in a clinical setting for patients undergoing treatment for chronic myeloid leukemia.Materials and Methods:Processes were established for registering and transferring samples from the clinic to an academic sequencing facility for long-read sequencing. An automated analysis pipeline for detecting mutations was established, and an information system was implemented comprising features for data management, analysis and visualization. Clinical validation was performed by identifying BCR-ABL1 TKI resistance mutations by Sanger and long-read sequencing in parallel. The developed software is available as open source via GitHub at https://github.com/pharmbio/clampResults:The information system enabled traceable transfer of samples from the clinic to the sequencing facility, robust and automated analysis of the long-read sequence data, and communication of results from sequence analysis in a reporting format that could be easily interpreted and acted upon by clinical experts. In a validation study, all 17 resistance mutations found by Sanger sequencing were also detected by long-read sequencing. An additional 16 mutations were found only by long-read sequencing, all of them with frequencies below the limit of detection for Sanger sequencing. The clonal distributions of co-existing mutations were automatically resolved through the long-read data analysis. After the implementation and validation, the clinical laboratory switched their routine protocol from using Sanger to long-read sequencing for this application.Conclusions:Long-read sequencing delivers results with higher sensitivity compared to Sanger sequencing and enables earlier detection of emerging TKI resistance mutations. The developed processes, analysis workflow, and software components lower barriers for adoption and could be extended to other applications.

Obtaining complete and accurate bacterial genomes is vital for studying the characteristics of uncultured bacteria. Single-cell genomics is a promising approach for the culture-independent recovery of bacterial genomes from individual cells. However, single-amplified genomes (SAGs) often have fragmented and incomplete sequences due to chimeric and biased sequences introduced during the genome amplification process. To address this, we developed a single-cell amplified genome long-read assembly (scALA) workflow to construct complete circular SAGs (cSAGs) from long-read single-cell sequencing data of uncultured bacteria. We used the SAG-gel platform, which is both cost-effective and high-throughput, to obtain hundreds of short-read and long-read sequencing data for specific bacterial strains. The scALA workflow generated cSAGs by repeated in silico processing for sequence bias reduction and contig assembly. From 12 human fecal samples, including two cohabitant groups, scALA generated 16 cSAGs of three specifically targeted bacterial species: Anaerostipes hadrus, Agathobacter rectalis, and Ruminococcus gnavus. We discovered strain-specific structural variations shared among cohabiting hosts, while all cSAGs of the same species showed high homology in aligned genomic regions. A. hadrus cSAGs exhibited 10 kbp-long phage insertions, various saccharide metabolic capabilities, and different CRISPR-Cas systems in each strain. The sequence similarity of A. hadrus genomes did not necessarily correspond with orthologous functional genes, while host geographical regionality seemed to be highly related to gene possession. scALA allowed us to obtain closed circular genomes of specifically targeted bacteria from human microbiota samples, leading to an understanding of within-species diversities, including structural variations and linking mobile genetic elements, such as phages, to hosts. These analyses provide insight into microbial evolution, the adaptation of the community to environmental changes, and interactions with hosts. cSAGs constructed using this method can expand bacterial genome databases and our understanding of within-species diversities in uncultured bacteria.

Introduction: Accurate detection and prioritization of tumor-specific somatic variants is foundational to the development of personalized immunotherapies. Traditional pipelines for neoepitope identification rely heavily on short-read next-generation sequencing (NGS) and DNA-based variant calling, followed by RNA expression filtering and HLA-binding prediction. However, this approach is limited by time-intensive workflows, potential false positives from DNA-only calls, and poor capture of complex isoforms or RNA-level editing. Moreover, the accuracy of neoepitope prediction is constrained by conventional sequence-based HLA binding algorithms that do not account for structural and biophysical features of peptide-MHC interactions, particularly across underrepresented HLA alleles. Methods: To overcome these limitations, we developed a streamlined, sequencing-to-vaccine framework that leverages long-read RNA sequencing for direct detection of expressed somatic variants, including fusions, indels, and single nucleotide variants. Using Oxford Nanopore Technologies’ cDNA-PCR approach, we enable comprehensive, full-length transcript characterization from both fresh and frozen breast tumor samples. Our bioinformatic pipeline, NeoTarget, integrates alignment, variant calling, and neoepitope inference from tumor RNA in a single, modular workflow. A core innovation of this pipeline is HLA-Inception, a deep convolutional neural network trained to predict peptide-MHC class I binding using features derived from molecular dynamics-based modeling of electrostatic potential at the peptide-HLA interface, enhancing generalizability across diverse HLA subtypes. Results: Expanding on previous results, the current NeoTarget pipeline identifies, evaluates, and prioritizes single nucleotide variants, indels, and fusions. To validate this system, we used a set of 9 human breast cancer tumors benchmarked with short read sequencing. Identified neoantigens included most variants shown by clinical short read sequencing and also novel structural variants (i.e., fusions) only revealed with long read sequencing with high confidence. Conclusion: Here, we describe the design and performance of the NeoTarget pipeline, and show proof-of-concept feasibility of applying this fast and cost-effective workflow for HLA-restricted epitope discovery in breast tumors using long-read RNA sequencing. Overall, these methods can be applied to rapidly identify and prioritize neoantigens for personalized neoantigen-based immune therapies. Citation Format: F. Batalini, S. Henry, Y. Zhang, R. Foster, J. M. Gayton, A. Hostal, E. A. Wilson, A. Singharoy, M. Gustafson, B. Pockaj, J. Park, K. S. Anderson. Somatic variant detection using long read RNA sequencing reveals HLA-restricted targets for immunotherapy [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-08.

Transposable elements, such as Long INterspersed Elements (LINEs), are DNA sequences that can replicate within genomes. LINEs replicate using an RNA intermediate followed by reverse transcription and are typically a few kilobases in length. LINE activity creates genomic structural variants in human populations and leads to somatic alterations in cancer genomes. Long-read RNA sequencing technologies, including Oxford Nanopore and PacBio, can directly sequence relatively long transcripts, thus providing the opportunity to examine full-length LINE transcripts. This study focuses on the development of a new bioinformatics pipeline for the identification and quantification of active, full-length LINE transcripts in diverse human tissues and cell lines. In our pipeline, we utilized RepeatMasker to identify LINE-1 (L1) transcripts from long-read transcriptome data and incorporated several criteria, such as transcript start position, divergence, and length, to remove likely false positives. Comparisons between cancerous and normal cell lines, as well as human tissue samples, revealed elevated expression levels of young LINEs in cancer, particularly at intact L1 loci. By employing bioinformatics methodologies on long-read transcriptome data, this study demonstrates the landscape of L1 expression in tissues and cell lines.

The root systems of most plant species are aided by the soil-foraging capacities of symbiotic arbuscular mycorrhizal (AM) fungi of the Glomeromycotina subphylum. Despite recent advances in our knowledge of the ecology and molecular biology of this mutualistic symbiosis, our understanding of the AM fungi genome biology is just emerging. Presented here is a close to T2T genome assembly of the model AM fungus Rhizophagus irregularis DAOM197198, achieved through Nanopore long-read DNA sequencing and Hi-C data. This haploid genome assembly of R. irregularis, alongside short- and long-read RNA-Sequencing data, was used to produce a comprehensive annotation catalog of gene models, repetitive elements, small RNA loci, and DNA cytosine methylome. A phylostratigraphic gene age inference framework revealed that the birth of genes associated with nutrient transporter activity and transmembrane ion transport systems predates the emergence of Glomeromycotina. While nutrient cycling in AM fungi relies on genes that existed in ancestor lineages, a burst of Glomeromycotina-restricted genetic innovation is also detected. Analysis of the chromosomal distribution of genetic and epigenetic features highlights evolutionarily young genomic regions that produce abundant small RNAs, suggesting active RNA-based monitoring of genetic sequences surrounding recently evolved genes. This chromosome-scale view of the genome of an AM fungus genome reveals previously unexplored sources of genomic novelty in an organism evolving under an obligate symbiotic life cycle.

BackgroundVancomycin-resistant enterococci (VRE) are successful nosocomial pathogens able to cause hospital outbreaks. In the Netherlands, core-genome MLST (cgMLST) based on short-read sequencing is often used for molecular typing. Long-read sequencing is more rapid and provides useful information about the genome’s structural composition but lacks the precision required for SNP-based typing and cgMLST. Here we compared prophages among 50 complete E. faecium genomes belonging to different lineages to explore whether a phage signature would be usable for typing and identifying an outbreak caused by VRE. As a proof of principle, we investigated if long-read sequencing data would allow for identifying phage signatures and thereby outbreak-related isolates.ResultsAnalysis of complete genome sequences of publicly available isolates showed variation in phage content among different lineages defined by MLST. We identified phage present in multiple STs as well as phages uniquely detected within a single lineage. Next, in silico phage typing was applied to twelve MinION sequenced isolates belonging to two different genetic backgrounds, namely ST117/CT24 and ST80/CT16. Genomic comparisons of the long-read-based assemblies allowed us to correctly identify isolates of the same complex type based on global genome architecture and specific phage signature similarity.ConclusionsFor rapid identification of related VRE isolates, phage content analysis in long-read sequencing data is possible. This allows software development for real-time typing analysis of long-read sequencing data, which will generate results within several hours. Future studies are required to assess the discriminatory power of this method in the investigation of ongoing outbreaks over a longer time period.

Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology through the comprehensive identification and quantification of full-length mRNA isoforms. Despite great promise, challenges remain in the widespread implementation of LRS technologies for RNA-based applications, including concerns about low coverage, high sequencing error, and robust computational pipelines. Although much focus has been placed on defining mRNA exon composition and structure with LRS data, less careful characterization has been done of the ability to assess the terminal ends of isoforms, specifically, transcription start and end sites. Such characterization is crucial for completely delineating full mRNA molecules and regulatory consequences. However, there are substantial inconsistencies in both start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. Here, we describe the specific challenges of identifying and quantifying mRNA terminal ends with LRS technologies and how these issues influence biological interpretations of LRS data. We then review recent experimental and computational advances designed to alleviate these problems, with ideal use cases for each approach. Finally, we outline anticipated developments and necessary improvements for the characterization of terminal ends from LRS data.