Long read sequencing technologies provide an efficient approach to generating highly contiguous and informative assemblies. However, higher relative error rates can introduce frameshifts and premature stop codons that pseudogenize genes, hindering downstream analyses. We developed a software tool that detects gene-fragmenting errors in draft assemblies of small genomes through comparison with a curated set of reference genome sequences and raw read information. In our presented example, detected errors represent less than 0.05% of the genome, but when corrected reduced the rate of pseudogenes from 23.3 to 5.6% in example long read assemblies, comparable to the rate of pseudogenes in short read assemblies. We demonstrate that this software can detect assembly errors in long read assemblies generated from small genomes and correct them to de-fragment genes.

Genome assemblers are a critical component of genome science, but the choice of assembly software and protocols can be daunting. Here, we investigate genome assembly variation and its implications for gene discovery across three nematode species-Caenorhabditis bovis, Haemonchus contortus, and Heligmosomoides bakeri-highlighting the critical interplay between assembly choice and downstream genomic analysis. Selecting commonly used genome assemblers, we generated multiple assemblies for each species, analyzing their structure, completeness, and effect on gene family analysis. Our findings demonstrate that assembly variations can significantly affect gene family composition, with notable differences in gene families important in anthelmintic discovery and immunomodulation. Despite broadly similar performance using various assembly metrics, comparisons of assemblies with a single species revealed underlying structural rearrangements and inconsistencies in gene content, which would affect downstream analyses. This emphasizes the need for continuous refinement of genome assemblies and their annotations.

Advanced long-read sequencing technologies, such as those from Oxford Nanopore Technologies and Pacific Biosciences, are finding a wide use in de novo genome sequencing projects. However, long reads typically have higher error rates relative to short reads. If left unaddressed, subsequent genome assemblies may exhibit high base error rates that compromise the reliability of downstream analysis. Several specialized error correction tools for genome assemblies have since emerged, employing a range of algorithms and strategies to improve base quality. However, despite these efforts, many genome assembly workflows still produce regions with elevated error rates, such as gaps filled with unpolished or ambiguous bases. To address this, we introduce GoldPolish-Target, a modular targeted sequence polishing pipeline. Coupled with GoldPolish, a linear-time genome assembly algorithm, GoldPolish-Target isolates and polishes user-specified assembly loci, offering a resource-efficient means for polishing targeted regions of draft genomes.

Experiments using Drosophila melanogaster and Homo sapiens datasets demonstrate that GoldPolish-Target can reduce insertion/deletion (indel) and mismatch errors by up to 49.2% and 55.4% respectively, achieving base accuracy values upwards of 99.9% (Phred score Q > 30). This polishing accuracy is comparable to the current state-of-the-art, Medaka, while exhibiting up to 27-fold shorter run times and consuming 95% less memory, on average.

GoldPolish-Target, in contrast to most other polishing tools, offers the ability to target specific regions of a genome assembly for polishing, providing a computationally light-weight and highly scalable solution for base error correction.

Oxford Nanopore Technology (ONT) sequencing is a third-generation sequencing technology that enables cost-effective long-read sequencing, with broad applications in biological research. However, its high sequencing error rate in low-complexity regions hampers its applications in short tandem repeat (STR)-related research. To address this, we generated a comprehensive STR error profile of ONT by analyzing publicly available Nanopore sequencing datasets. We show that the sequencing error rate is influenced not only by STR length but also by the repeat unit and the flanking sequences of STR regions. Interestingly, certain flanking sequences were associated with higher sequencing accuracy, suggesting that certain STR loci are more suitable for Nanopore sequencing compared to other loci. While base quality scores of substitution errors within the STR regions were lower than those of correctly sequenced bases, such patterns were not observed for indel errors. Furthermore, choosing the most recent basecaller version and using the super accuracy model significantly improved STR sequencing accuracy. Finally, we present NanoMnT, a lightweight Python tool that corrects STR sequencing errors in sequencing data and estimates STR allele sizes. NanoMnT leverages the characteristics of ONT when estimating STR allele size and exhibits superior results for 1-bp- and 2-bp repeat STR compared to existing tools. By integrating our findings, we improved STR allele estimation accuracy for Ax10 repeats from 55% to 78% and up to 85% when excluding loci with unfavorable flanking sequences. Using NanoMnT, we present the utility of our findings by identifying microsatellite instability status in cancer sequencing data. NanoMnT is publicly available at https://github.com/18parkky/NanoMnT.

Accurate and high coverage genome assemblies are the basis for downstream analysis of metagenomic studies. Long-read sequencing technology is an ideal tool to facilitate the assemblies of metagenome, except for the drawback of usually producing reads with high sequencing error rate. Many polishing tools were developed to correct the sequencing error, but most are designed on the ground of one or two species. Considering the complexity and uneven depth of metagenomic study, we present a novel deep-learning polishing tool named MetaCONNET for polishing metagenomic assemblies. We evaluate MetaCONNET against Medaka, CONNET and NextPolish in accuracy, coverage, contiguity and resource consumption. Our results demonstrate that MetaCONNET provides a valuable polishing tool and can be applied to many metagenomic studies.

Hybrid metagenomic assembly of microbial communities, leveraging both long- and short-read sequencing technologies, is becoming an increasingly accessible approach, yet its widespread application faces several challenges. High-quality references may not be available for assembly accuracy comparisons common for benchmarking, and certain aspects of hybrid assembly may benefit from dataset-dependent, empiric guidance rather than the application of a uniform approach. In this study, several simple, reference-free characteristics-particularly coding gene content and read recruitment profiles-were hypothesized to be reliable indicators of assembly quality improvement during iterative error-fixing processes. These characteristics were compared to reference-dependent genome- and gene-centric analyses common for microbial community metagenomic studies. Two laboratory-scale bioreactors were sequenced with short- and long-read platforms, and assembled with commonly used software packages. Following long read assembly, long read correction and short read polishing were iterated up to ten times to resolve errors. These iterative processes were shown to have a substantial effect on gene- and genome-centric community compositions. Simple, reference-free assembly characteristics, specifically changes in gene fragmentation and short read recruitment, were robustly correlated with advanced analyses common in published comparative studies, and therefore are suitable proxies for hybrid metagenome assembly quality to simplify the identification of the optimal number of correction and polishing iterations. As hybrid metagenomic sequencing approaches will likely remain relevant due to the low added cost of short-read sequencing for differential coverage binning or the ability to access lower abundance community members, it is imperative that users are equipped to estimate assembly quality prior to downstream analyses.

Fungal α/β-glucans have significant importance in cellular functions including cell wall structure, host-pathogen interactions and energy storage, and wide application in high-profile fields, including food, nutrition, and pharmaceuticals. Fungal species and their growth/developmental stages result in a diversity of glucan contents, structures and bioactivities. Substantial progresses have been made to elucidate the fine structures and functions, and reveal the potential molecular synthesis pathway of fungal α/β-glucans. Herein, we review the current knowledge about the biosynthetic machineries, including: precursor UDP-glucose synthesis, initiation, elongation/termination and remodeling of α/β-glucan chains, and molecular regulation to maximally produce glucans in edible fungi. This review would provide future perspectives to biosynthesize the targeted glucans and reveal the catalytic mechanism of enzymes associated with glucan synthesis, including: UDP-glucose pyrophosphate phosphorylases (UGP), glucan synthases, and glucanosyltransferases in edible fungi.

In the context of increasing antimicrobial resistance (AMR), whole-genome sequencing (WGS) of bacteria is considered a highly accurate and comprehensive surveillance method for detecting and tracking the spread of resistant pathogens. Two primary sequencing technologies exist: short-read sequencing (50-300 base pairs) and long-read sequencing (thousands of base pairs). The former, based on Illumina sequencing platforms (ISPs), provides extensive coverage and high accuracy for detecting single nucleotide polymorphisms (SNPs) and small insertions/deletions, but is limited by its read length. The latter, based on platforms such as Oxford Nanopore Technologies (ONT), enables the assembly of genomes, particularly those with repetitive regions and structural variants, although its accuracy has historically been lower.

We performed a head-to-head comparison of these techniques to sequence the K. pneumoniae VS17 isolate, focusing on blaNDM resistance gene alleles in the context of a surveillance program. Discrepancies between the ISP (blaNDM-4 allele identified) and ONT (blaNDM-1 and blaNDM-5 alleles identified) were observed. Conjugation assays and Sanger sequencing, used as the gold standard, confirmed the validity of ONT results. This study demonstrates the importance of long-read or hybrid assemblies for accurate carbapenemase resistance gene identification and highlights the limitations of short reads in the context of gene duplications or multiple alleles.

In this proof-of-concept study, we conclude that recent long-read sequencing technology may outperform standard short-read sequencing for the accurate identification of carbapenemase alleles. Such information is crucial given the rising prevalence of strains producing multiple carbapenemases, especially as WGS is increasingly used for epidemiological surveillance and infection control.

As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses.

We developed Trans2express - a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly.

We present Trans2express - a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.

The roles of Asgard archaea in eukaryogenesis and marine biogeochemical cycles are well studied, yet their contributions in soil ecosystems remain unknown. Of particular interest are Asgard archaeal contributions to methane cycling in wetland soils. To investigate this, we reconstructed two complete genomes for soil-associated Atabeyarchaeia, a new Asgard lineage, and a complete genome of Freyarchaeia, and predicted their metabolism in situ. Metatranscriptomics reveals expression of genes for [NiFe]-hydrogenases, pyruvate oxidation and carbon fixation via the Wood-Ljungdahl pathway. Also expressed are genes encoding enzymes for amino acid metabolism, anaerobic aldehyde oxidation, hydrogen peroxide detoxification and carbohydrate breakdown to acetate and formate. Overall, soil-associated Asgard archaea are predicted to include non-methanogenic acetogens, highlighting their potential role in carbon cycling in terrestrial environments.

Accurate haplotyping facilitates distinguishing allele-specific expression, identifying cis-regulatory elements, and characterizing genomic variations, which enables more precise investigations into the relationship between genotype and phenotype. Recent advances in third-generation single-molecule long read and synthetic co-barcoded read sequencing techniques have harnessed long-range information to simplify the assembly graph and improve assembly genomic sequence. However, it remains methodologically challenging to reconstruct the complete haplotypes due to high sequencing error rates of long reads and limited capturing efficiency of co-barcoded reads. We here present a pipeline, AsmMix, for generating both contiguous and accurate diploid genomes. It first assembles co-barcoded reads to generate accurate haplotype-resolved assemblies that may contain many gaps, while the long-read assembly is contiguous but susceptible to errors. Then two assembly sets are integrated into haplotype-resolved assemblies with reduced misassembles. Through extensive evaluation on multiple synthetic datasets, AsmMix consistently demonstrates high precision and recall rates for haplotyping across diverse sequencing platforms, coverage depths, read lengths, and read accuracies, significantly outperforming other existing tools in the field. Furthermore, we validate the effectiveness of our pipeline using a human whole genome dataset (HG002), and produce highly contiguous, accurate, and haplotype-resolved assemblies. These assemblies are evaluated using the GIAB benchmarks, confirming the accuracy of variant calling. Our results demonstrate that AsmMix offers a straightforward yet highly efficient approach that effectively leverages both long reads and co-barcoded reads for haplotype-resolved assembly.

Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks.

We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct.

Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

Metagenomic taxonomic classifiers analyze either DNA or amino acid (AA) sequences. Metabuli ( https://metabuli.steineggerlab.com ), however, jointly analyzes both DNA and AA to leverage AA conservation for sensitive homology detection and DNA mutations for specific differentiation of closely related taxa. In the Critical Assessment of Metagenome Interpretation 2 plant-associated dataset, Metabuli covered 99% and 98% of classifications of state-of-the-art DNA- and AA-based classifiers, respectively.

Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied.

We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits.

As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

The advent of viral metagenomics, or viromics, has improved our knowledge and understanding of global viral diversity. High-throughput sequencing technologies enable explorations of the ecological roles, contributions to host metabolism, and the influence of viruses in various environments, including the human intestinal microbiome. However, bacterial metagenomic studies frequently have the advantage. The adoption of advanced technologies like long-read sequencing has the potential to be transformative in refining viromics and metagenomics. Here, we examined the effectiveness of long-read and hybrid sequencing by comparing Illumina short-read and Oxford Nanopore Technology (ONT) long-read sequencing technologies and different assembly strategies on recovering viral genomes from human faecal samples. Our findings showed that if a single sequencing technology is to be chosen for virome analysis, Illumina is preferable due to its superior ability to recover fully resolved viral genomes and minimise erroneous genomes. While ONT assemblies were effective in recovering viral diversity, the challenges related to input requirements and the necessity for amplification made it less ideal as a standalone solution. However, using a combined, hybrid approach enabled a more authentic representation of viral diversity to be obtained within samples.

With the advancement of long-read sequencing technologies and their increasing use for bacterial genomics, several methods for generating genome assemblies from error-prone long reads have been developed. These are complemented by various tools for assembly polishing using either long reads, short reads, or reference genomes. End users are therefore left with a plethora of possible combinations of programs for obtaining a final trusted assembly. Hence, there is also a need to measure the completeness and accuracy of such assemblies, for which, again, several evaluation methods implemented in various programs are available. In order to automatically run multiple genome assembly and evaluation programs at once, I developed two workflows for the workflow management system Snakemake, which provide end users with an easy-to-run solution for testing various genome assemblies from their sequencing data. Both workflows use the conda packaging system, so there is no need for manual installation of each program.

The workflows are available as open source software under the MIT license at github.com/pmenzel/ont-assembly-snake and github.com/pmenzel/score-assemblies.

Genome assembly and annotation using short-paired reads is challenging for eukaryotic organisms due to their large size, variable ploidy and large number of repetitive elements. However, the use of single-molecule long reads improves assembly quality (completeness and contiguity), but haplotype duplications still pose assembly challenges. To address the effect of read length on genome assembly quality, gene prediction and annotation, we compared genome assemblers and sequencing technologies with four strains of the ectomycorrhizal fungus Laccaria trichodermophora. By analysing the predicted repertoire of carbohydrate enzymes, we investigated the effects of assembly quality on functional inferences. Libraries were generated using three different sequencing platforms (Illumina Next-Seq, Mi-Seq and PacBio Sequel), and genomes were assembled using single and hybrid assemblies/libraries. Long reads or hybrid assemby resolved the collapsing of repeated regions, but the nuclear heterozygous versions remained unresolved. In dikaryotic fungi, each cell includes two nuclei and each nucleus has differences not only in allelic gene version but also in gene composition and synteny. These heterokaryotic cells produce fragmentation and size overestimation of the genome assembly of each nucleus. Hybrid assembly revealed a wider functional diversity of genomes. Here, several predicted oxidizing activities on glycosyl residues of oligosaccharides and several chitooligosaccharide acetylase activities would have passed unnoticed in short-read assemblies. Also, the size and fragmentation of the genome assembly, in combination with heterozygosity analysis, allowed us to distinguish homokaryotic and heterokaryotic strains isolated from L. trichodermophora fruit bodies.

Tryptase, the most abundant mast cell granule protein, is elevated in severe asthma patients independent of type 2 inflammation status. Higher active β tryptase allele counts are associated with higher levels of peripheral tryptase and lower clinical benefit from anti-IgE therapies. Tryptase is a therapeutic target of interest in severe asthma and chronic spontaneous urticaria. Active and inactive allele counts may enable stratification to assess response to therapies in asthmatic patient subpopulations. Tryptase gene loci TPSAB1 and TPSB2 have high levels of sequence identity, which makes genotyping a challenging task. Here, we report a targeted next-generation sequencing (NGS) assay and downstream bioinformatics analysis for determining polymorphisms at tryptase TPSAB1 and TPSB2 loci. Machine learning modeling using multiple polymorphisms in the tryptase loci was used to improve the accuracy of genotyping calls. The assay was tested and qualified on DNA extracted from whole blood of healthy donors and asthma patients, achieving accuracy of 96%, 96% and 94% for estimation of inactive α and βΙΙΙFS tryptase alleles and α duplication on TPSAB1, respectively. The reported NGS assay is a cost-effective method that is more efficient than Sanger sequencing and provides coverage to evaluate known as well as unreported tryptase polymorphisms.

We analyzed the pan-genome and gene content modulation of the most diverse genome data set of the Mycobacterium tuberculosis complex (MTBC) gathered to date. The closed pan-genome of the MTBC was characterized by reduced accessory and strain-specific genomes, compatible with its clonal nature. However, significantly fewer gene families were shared between MTBC genomes as their phylogenetic distance increased. This effect was only observed in inter-species comparisons, not within-species, which suggests that species-specific ecological characteristics are associated with changes in gene content. Gene loss, resulting from genomic deletions and pseudogenization, was found to drive the variation in gene content. This gene erosion differed among MTBC species and lineages, even within M. tuberculosis, where L2 showed more gene loss than L4. We also show that phylogenetic proximity is not always a good proxy for gene content relatedness in the MTBC, as the gene repertoire of Mycobacterium africanum L6 deviated from its expected phylogenetic niche conservatism. Gene disruptions of virulence factors, represented by pseudogene annotations, are mostly not conserved, being poor predictors of MTBC ecotypes. Each MTBC ecotype carries its own accessory genome, likely influenced by distinct selective pressures such as host and geography. It is important to investigate how gene loss confer new adaptive traits to MTBC strains; the detected heterogeneous gene loss poses a significant challenge in elucidating genetic factors responsible for the diverse phenotypes observed in the MTBC. By detailing specific gene losses, our study serves as a resource for researchers studying the MTBC phenotypes and their immune evasion strategies.IMPORTANCEIn this study, we analyzed the gene content of different ecotypes of the Mycobacterium tuberculosis complex (MTBC), the pathogens of tuberculosis. We found that changes in their gene content are associated with their ecological features, such as host preference. Gene loss was identified as the primary driver of these changes, which can vary even among different strains of the same ecotype. Our study also revealed that the gene content relatedness of these bacteria does not always mirror their evolutionary relationships. In addition, some genes of virulence can be variably lost among strains of the same MTBC ecotype, likely helping them to evade the immune system. Overall, our study highlights the importance of understanding how gene loss can lead to new adaptations in these bacteria and how different selective pressures may influence their genetic makeup.

Viral metagenomics has fuelled a rapid change in our understanding of global viral diversity and ecology. Long-read sequencing and hybrid assembly approaches that combine long- and short-read technologies are now being widely implemented in bacterial genomics and metagenomics. However, the use of long-read sequencing to investigate viral communities is still in its infancy. While Nanopore and PacBio technologies have been applied to viral metagenomics, it is not known to what extent different technologies will impact the reconstruction of the viral community. Thus, we constructed a mock bacteriophage community of previously sequenced phage genomes and sequenced them using Illumina, Nanopore and PacBio sequencing technologies and tested a number of different assembly approaches. When using a single sequencing technology, Illumina assemblies were the best at recovering phage genomes. Nanopore- and PacBio-only assemblies performed poorly in comparison to Illumina in both genome recovery and error rates, which both varied with the assembler used. The best Nanopore assembly had errors that manifested as SNPs and INDELs at frequencies 41 and 157 % higher than found in Illumina only assemblies, respectively. While the best PacBio assemblies had SNPs at frequencies 12 and 78 % higher than found in Illumina-only assemblies, respectively. Despite high-read coverage, long-read-only assemblies recovered a maximum of one complete genome from any assembly, unless reads were down-sampled prior to assembly. Overall the best approach was assembly by a combination of Illumina and Nanopore reads, which reduced error rates to levels comparable with short-read-only assemblies. When using a single technology, Illumina only was the best approach. The differences in genome recovery and error rates between technology and assembler had downstream impacts on gene prediction, viral prediction, and subsequent estimates of diversity within a sample. These findings will provide a starting point for others in the choice of reads and assembly algorithms for the analysis of viromes.

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Identifying homologs is an important process in the analysis of genetic patterns underlying traits and evolutionary relationships among species. Analysis of gene families is often used to form and support hypotheses on genetic patterns such as gene presence, absence, or functional divergence which underlie traits examined in functional studies. These analyses often require precise identification of all members in a targeted gene family. Manual pipelines where homology search and orthology assignment tools are used separately are the most common approach for identifying small gene families where accurate identification of all members is important. The ability to curate sequences between steps in manual pipelines allows for simple and precise identification of all possible gene family members. However, the validity of such manual pipeline analyses is often decreased by inappropriate approaches to homology searches including too relaxed or stringent statistical thresholds, inappropriate query sequences, homology classification based on sequence similarity alone, and low-quality proteome or genome sequences. In this article, we propose several approaches to mitigate these issues and allow for precise identification of gene family members and support for hypotheses linking genetic patterns to functional traits.

Improvements in long read DNA sequencing and related techniques facilitated the generation of complex eukaryotic genomes. Despite these advances, the quality of constructed plant reference genomes remains relatively poor due to the large size of genomes, high content of repetitive sequences, and wide variety of ploidy. Here, we developed the de novo sequencing and assembly of high polyploid plant genome, Hibiscus syriacus, a flowering plant species of the Malvaceae family, using the Oxford Nanopore Technologies and Pacific Biosciences Sequel sequencing platforms. We investigated an efficient combination of high-quality and high-molecular-weight DNA isolation procedure and suitable assembler to achieve optimal results using long read sequencing data. We found that abundant ultra-long reads allow for large and complex polyploid plant genome assemblies with great recovery of repetitive sequences and error correction even at relatively low depth Nanopore sequencing data and polishing compared to previous studies. Collectively, our combination provides cost effective methods to improve genome continuity and quality compared to the previously reported reference genome by accessing highly repetitive regions. The application of this combination may enable genetic research and breeding of polyploid crops, thus leading to improvements in crop production.

Assembly of a high-quality genome is important for downstream comparative and functional genomic studies. However, most tools for genome assembly assessment only give qualitative reports, which do not pinpoint assembly errors at specific regions. Here, we develop a new reference-free tool, Clipping information for Revealing Assembly Quality (CRAQ), which maps raw reads back to assembled sequences to identify regional and structural assembly errors based on effective clipped alignment information. Error counts are transformed into corresponding assembly evaluation indexes to reflect the assembly quality at single-nucleotide resolution. Notably, CRAQ distinguishes assembly errors from heterozygous sites or structural differences between haplotypes. This tool can clearly indicate low-quality regions and potential structural error breakpoints; thus, it can identify misjoined regions that should be split for further scaffold building and improvement of the assembly. We have benchmarked CRAQ on multiple genomes assembled using different strategies, and demonstrated the misjoin correction for improving the constructed pseudomolecules.

Although current long-read sequencing technologies have a long-read length that facilitates assembly for genome reconstruction, they have high sequence errors. While various assemblers with different perspectives have been developed, no systematic evaluation of assemblers with long reads for diploid genomes with varying heterozygosity has been performed. Here, we evaluated a series of processes, including the estimation of genome characteristics such as genome size and heterozygosity, de novo assembly, polishing, and removal of allelic contigs, using six genomes with various heterozygosity levels. We evaluated five long-read-only assemblers (Canu, Flye, miniasm, NextDenovo and Redbean) and five hybrid assemblers that combine short and long reads (HASLR, MaSuRCA, Platanus-allee, SPAdes and WENGAN) and proposed a concrete guideline for the construction of haplotype representation according to the degree of heterozygosity, followed by polishing and purging haplotigs, using stable and high-performance assemblers: Redbean, Flye and MaSuRCA.

Genome assembly is a computational technique that involves piecing together deoxyribonucleic acid (DNA) fragments generated by sequencing technologies to create a comprehensive and precise representation of the entire genome. Generating a high-quality human reference genome is a crucial prerequisite for comprehending human biology, and it is also vital for downstream genomic variation analysis. Many efforts have been made over the past few decades to create a complete and gapless reference genome for humans by using a diverse range of advanced sequencing technologies. Several available tools are aimed at enhancing the quality of haploid and diploid human genome assemblies, which include contig assembly, polishing of contig errors, scaffolding and variant phasing. Selecting the appropriate tools and technologies remains a daunting task despite several studies have investigated the pros and cons of different assembly strategies. The goal of this paper was to benchmark various strategies for human genome assembly by combining sequencing technologies and tools on two publicly available samples (NA12878 and NA24385) from Genome in a Bottle. We then compared their performances in terms of continuity, accuracy, completeness, variant calling and phasing. We observed that PacBio HiFi long-reads are the optimal choice for generating an assembly with low base errors. On the other hand, we were able to produce the most continuous contigs with Oxford Nanopore long-reads, but they may require further polishing to improve on quality. We recommend using short-reads rather than long-reads themselves to improve the base accuracy of contigs from Oxford Nanopore long-reads. Hi-C is the best choice for chromosome-level scaffolding because it can capture the longest-range DNA connectedness compared to 10× linked-reads and Bionano optical maps. However, a combination of multiple technologies can be used to further improve the quality and completeness of genome assembly. For diploid assembly, hifiasm is the best tool for human diploid genome assembly using PacBio HiFi and Hi-C data. Looking to the future, we expect that further advancements in human diploid assemblers will leverage the power of PacBio HiFi reads and other technologies with long-range DNA connectedness to enable the generation of high-quality, chromosome-level and haplotype-resolved human genome assemblies.

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Extrachromosomal elements of bacterial cells such as plasmids are notorious for their importance in evolution and adaptation to changing ecology. However, high-resolution population-wide analysis of plasmids has only become accessible recently with the advent of scalable long-read sequencing technology. Current typing methods for the classification of plasmids remain limited in their scope which motivated us to develop a computationally efficient approach to simultaneously recognize novel types and classify plasmids into previously identified groups. Here, we introduce mge-cluster that can easily handle thousands of input sequences which are compressed using a unitig representation in a de Bruijn graph. Our approach offers a faster runtime than existing algorithms, with moderate memory usage, and enables an intuitive visualization, classification and clustering scheme that users can explore interactively within a single framework. Mge-cluster platform for plasmid analysis can be easily distributed and replicated, enabling a consistent labelling of plasmids across past, present, and future sequence collections. We underscore the advantages of our approach by analysing a population-wide plasmid data set obtained from the opportunistic pathogen Escherichia coli, studying the prevalence of the colistin resistance gene mcr-1.1 within the plasmid population, and describing an instance of resistance plasmid transmission within a hospital environment.

Alternative splicing is an important mechanism that enhances protein functional diversity. To date, our understanding of alternative splicing variants has been based on mRNA transcript data, but due to the difficulty in predicting protein structures, protein tertiary structures have been largely unexplored. However, with the release of AlphaFold, which predicts three-dimensional models of proteins, this challenge is rapidly being overcome. Here, we present a dataset of 315 predicted structures of abnormal isoforms in 18 uveal melanoma patients based on second- and third-generation transcriptome-sequencing data. This information comprises a high-quality set of structural data on recurrent aberrant isoforms that can be used in multiple types of studies, from those aimed at revealing potential therapeutic targets to those aimed at recognizing of cancer neoantigens at the atomic level.

Recent advances in long read technologies not only enable large consortia to aim to sequence all eukaryotes on Earth, but they also allow individual laboratories to sequence their species of interest with relatively low investment. Long read technologies embody the promise of overcoming scaffolding problems associated with repeats and low complexity sequences, but the number of contigs often far exceeds the number of chromosomes and they may contain many insertion and deletion errors around homopolymer tracts. To overcome these issues, we have implemented the ILRA pipeline to correct long read-based assemblies. Contigs are first reordered, renamed, merged, circularized, or filtered if erroneous or contaminated. Illumina short reads are used subsequently to correct homopolymer errors. We successfully tested our approach by improving the genome sequences of Homo sapiens, Trypanosoma brucei, and Leptosphaeria spp., and by generating four novel Plasmodium falciparum assemblies from field samples. We found that correcting homopolymer tracts reduced the number of genes incorrectly annotated as pseudogenes, but an iterative approach seems to be required to correct more sequencing errors. In summary, we describe and benchmark the performance of our new tool, which improved the quality of novel long read assemblies up to 1 Gbp. The pipeline is available at GitHub: https://github.com/ThomasDOtto/ILRA.

Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein-coding gene predictions.

The impact of repeat masking, long-read and short-read inputs, and de novo and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity.

Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended.

While the annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions.

Xylella fastidiosa is a vascular plant pathogenic bacterium native to the Americas that is causing significant epidemics and economic losses in olive and almonds in Europe, where it is a quarantine pathogen. Since its first detection in 2013 in Italy, mandatory surveys across Europe revealed the presence of the bacterium also in France, Spain, and Portugal. Combining Oxford Nanopore Technologies and Illumina sequencing data, we assembled high-quality complete genomes of seven X. fastidiosa subsp. fastidiosa strains isolated from different plants in Spain, the United States, and Mexico. Comparative genomic analyses discovered differences in plasmid content among strains, including plasmids that had been overlooked previously when using the Illumina sequencing platform alone. Interestingly, in strain CFBP8073, intercepted in France from plants imported from Mexico, three plasmids were identified, including two (plasmids pXF-P1.CFBP8073 and pXF-P2.CFBP8073) not previously described in X. fastidiosa and one (pXF5823.CFBP8073) almost identical to a plasmid described in a X. fastidiosa strain from citrus. Plasmids found in the Spanish strains here were similar to those described previously in other strains from the same subspecies and ST1 isolated in the Balearic Islands and the United States. The genome resources from this work will assist in further studies on the role of plasmids in the epidemiology, ecology, and evolution of this plant pathogen.

Next-generation whole-genome sequencing is essential for high-resolution surveillance of bacterial pathogens, for example, during outbreak investigations or for source tracking and escape variant analysis. However, current global sequencing and bioinformatic bottlenecks and a long time to result with standard technologies demand new approaches. In this study, we investigated whether novel nanopore Q20+ long-read chemistry enables standardized and easily accessible high-resolution typing combined with core genome multilocus sequence typing (cgMLST). We set high requirements for discriminatory power by using the slowly evolving bacterium Bordetella pertussis as a model pathogen. Our results show that the increased raw read accuracy enables the description of epidemiological scenarios and phylogenetic linkages at the level of gold-standard short reads. The same was true for our variant analysis of vaccine antigens, resistance genes, and virulence factors, demonstrating that nanopore sequencing is a legitimate competitor in the area of next-generation sequencing (NGS)-based high-resolution bacterial typing. Furthermore, we evaluated the parameters for the fastest possible analysis of the data. By combining the optimized processing pipeline with real-time basecalling, we established a workflow that allows for highly accurate and extremely fast high-resolution typing of bacterial pathogens while sequencing is still in progress. Along with advantages such as low costs and portability, the approach suggested here might democratize modern bacterial typing, enabling more efficient infection control globally.

Metagenomic strategy serves as the foundation for the ecological exploration of novel bioresources (e.g., industrial enzymes and bioactive molecules) and biohazards (e.g., pathogens and antibiotic resistance genes) in natural and engineered microbial systems across multiple disciplines. Recent advancements in sequencing technology have fostered rapid development in the field of microbiome research where an increasing number of studies have applied both illumina short reads (SRs) and nanopore long reads (LRs) sequencing in their metagenomic workflow. However, given the high complexity of an environmental microbiome data set and the bioinformatic challenges caused by the unique features of these sequencing technologies, integrating SRs and LRs is not as straightforward as one might assume. The fast renewal of existing tools and growing diversity of new algorithms make access to this field even more difficult. Therefore, here we systematically summarized the complete workflow from DNA extraction to data processing strategies for applying illumina and nanopore-integrated metagenomics in the investigation in environmental microbiomes. Overall, this review aims to provide a timely knowledge framework for researchers that are interested in or are struggling with the SRs and LRs integration in their metagenomic analysis. The discussions presented will facilitate improved ecological understanding of community functionalities and assembly of natural, engineered, and human microbiomes, benefiting researchers from multiple disciplines.