Finding the right fit: A comprehensive evaluation of short-read and long-read sequencing approaches to maximize the utility of clinical microbiome data

A longstanding challenge in human microbiome research is achieving the taxonomic and functional resolution needed to generate testable hypotheses about the gut microbiome's impact on health and disease. More recently, this challenge has extended to a need for in-depth understanding of the pharmacokinetics and pharmacodynamics of clinical microbiome-based interventions. Whole genome metagenomic sequencing provides high taxonomic resolution and information on metagenome functional capacity, but the required deep sequencing is costly. For this reason, short-read sequencing of the bacterial 16S ribosomal RNA (rRNA) gene is the standard for microbiota profiling, despite its poor taxonomic resolution. The recent falling costs and improved fidelity of long-read sequencing warrant an evaluation of this approach for clinical microbiome analysis. We used samples from participants enrolled in a Phase 1b clinical trial of a novel live biotherapeutic product to perform a comparative analysis of short-read and long-read amplicon and metagenomic sequencing approaches to assess their value for generating informative and actionable clinical microbiome data. Comparison of ubiquitous short-read 16S rRNA amplicon profiling to long-read profiling of the 16S-ITS-23S rRNA amplicon showed that only the latter provided strain-level community resolution and insight into novel taxa. Across all methods, overall community taxonomic profiles were comparable and relationships between samples were conserved, highlighting the accuracy of modern microbiome analysis pipelines. All methods identified an active ingredient strain in treated study participants, though detection confidence was higher for long-read methods. Read coverage from both metagenomic methods provided evidence of active ingredient strain replication in some treated participants. Compared to short-read metagenomics, approximately twice the proportion of long reads were assigned functional annotations (63% vs. 34%). Finally, similar bacterial metagenome-assembled genomes (MAGs) were recovered across short-read and long-read metagenomic methods, although MAGs recovered from long reads were more complete. Overall, despite higher costs, long-read microbiome characterization provides added scientific value for clinical microbiome research in the form of higher taxonomic and functional resolution and improved recovery of microbial genomes compared to traditional short-read methodologies.

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Surveillance of potential pathogens and antibiotic resistance in wastewater and surface water from Boston, USA and Vellore, India using long-read metagenomic sequencing

10.1101/2021.04.22.21255864 ◽

2021 ◽

Author(s):

Erica R Fuhrmeister ◽

Lee E Voth-Gaeddert ◽

Angeline Metilda ◽

Albert Tai ◽

Rebecca E Batorsky ◽

...

Keyword(s):

Antibiotic Resistance ◽

Surface Water ◽

Real Time ◽

Resistance Genes ◽

Real Time Pcr ◽

Metagenomic Sequencing ◽

Antibiotic Resistant ◽

Short Read ◽

Long Reads ◽

Long Read

Environmental sampling (wastewater) could be an efficient surveillance strategy to capture global emerging trends in the spread of antibiotic resistance. Long-read DNA sequencing can resolve the genetic context of antibiotic resistance genes (ARGs) and is a promising tool for non- culture-based monitoring of antibiotic-resistant pathogens and ARGs in environmental samples, but has not been rigorously validated against conventional methods. We tested long-read sequencing using the portable Nanopore MinION for surveying pathogens, ARGs, and antibiotic- resistant pathogens in municipal wastewater, hospital wastewater, and surface water collected from Boston, USA and Vellore, India. We compared detection of enteric pathogens by assembly of long reads, with and without short-read polishing, and unassembled raw long reads for ARGs to multiplex real-time PCR. Using real-time PCR as a benchmark, long-read metagenomics was 49% sensitive and 75% specific at pathogen detection in assembled contigs, and 16% sensitive and 100% specific at detecting 28 clinically relevant resistance genes in raw long reads. Short- read polishing did not substantially improve pathogen identification or impact ARG identification in the assembled contigs, demonstrating that short-read polishing is not required, which greatly reduces costs. The high specificity of ARG detection supports portable long-read sequencing as a valuable tool to profile ARGs and antibiotic-resistant pathogens for environmental surveillance programs.

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Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads

BMC Genomics ◽

10.1186/s12864-021-07702-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Seth Commichaux ◽

Kiran Javkar ◽

Padmini Ramachandran ◽

Niranjan Nagarajan ◽

Denis Bertrand ◽

...

Keyword(s):

Public Health ◽

Public Health Response ◽

High Quality ◽

Short Read ◽

Short Reads ◽

The Core ◽

Long Reads ◽

Health Response ◽

Long Read ◽

Core Genes

Abstract Background Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads. Results We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies. Conclusion The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

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Biosynthetic potential of uncultured Antarctic soil bacteria revealed through long-read metagenomic sequencing

The ISME Journal ◽

10.1038/s41396-021-01052-3 ◽

2021 ◽

Author(s):

Valentin Waschulin ◽

Chiara Borsetto ◽

Robert James ◽

Kevin K. Newsham ◽

Stefano Donadio ◽

...

Keyword(s):

Genome Mining ◽

Gene Clusters ◽

Biosynthetic Gene Cluster ◽

Full Length ◽

Metagenomic Sequencing ◽

Short Read ◽

Short Read Sequencing ◽

Rich Diversity ◽

Long Read ◽

The Rich

AbstractThe growing problem of antibiotic resistance has led to the exploration of uncultured bacteria as potential sources of new antimicrobials. PCR amplicon analyses and short-read sequencing studies of samples from different environments have reported evidence of high biosynthetic gene cluster (BGC) diversity in metagenomes, indicating their potential for producing novel and useful compounds. However, recovering full-length BGC sequences from uncultivated bacteria remains a challenge due to the technological restraints of short-read sequencing, thus making assessment of BGC diversity difficult. Here, long-read sequencing and genome mining were used to recover >1400 mostly full-length BGCs that demonstrate the rich diversity of BGCs from uncultivated lineages present in soil from Mars Oasis, Antarctica. A large number of highly divergent BGCs were not only found in the phyla Acidobacteriota, Verrucomicrobiota and Gemmatimonadota but also in the actinobacterial classes Acidimicrobiia and Thermoleophilia and the gammaproteobacterial order UBA7966. The latter furthermore contained a potential novel family of RiPPs. Our findings underline the biosynthetic potential of underexplored phyla as well as unexplored lineages within seemingly well-studied producer phyla. They also showcase long-read metagenomic sequencing as a promising way to access the untapped genetic reservoir of specialised metabolite gene clusters of the uncultured majority of microbes.

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Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

Nature Communications ◽

10.1038/s41467-021-23821-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Verónica Lloréns-Rico ◽

Sara Vieira-Silva ◽

Pedro J. Gonçalves ◽

Gwen Falony ◽

Jeroen Raes

Keyword(s):

Microbial Communities ◽

Quantitative Methods ◽

Microbial Load ◽

Metagenomic Sequencing ◽

Sampling Depth ◽

Microbiome Research ◽

Microbiome Data ◽

The Impact ◽

Analytical Approaches ◽

Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

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Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

10.1101/2020.05.31.126490 ◽

2020 ◽

Author(s):

Andrew J. Page ◽

Nabil-Fareed Alikhan ◽

Michael Strinden ◽

Thanh Le Viet ◽

Timofey Skvortsov

Keyword(s):

Mycobacterium Tuberculosis ◽

State Of The Art ◽

Sequence Data ◽

Human Pathogen ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing ◽

Long Reads ◽

Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

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Assembling reads improves taxonomic classification of species

10.21203/rs.3.rs-22309/v1 ◽

2020 ◽

Author(s):

Quang Tran ◽

Vinhthuy Phan

Keyword(s):

Classification Performance ◽

Performance Characteristics ◽

Metagenomic Data ◽

Species Classification ◽

Short Read ◽

Short Reads ◽

Sequencing Errors ◽

Trade Offs ◽

Long Reads ◽

Long Read

Abstract Background: Most current metagenomic classifiers and profilers employ short reads to classify, bin and profile microbial genomes that are present in metagenomic samples. Many of these methods adopt techniques that aim to identify unique genomic regions of genomes so as to differentiate them. Because of this, short-read lengths might be suboptimal. Longer read lengths might improve the performance of classification and profiling. However, longer reads produced by current technology tend to have a higher rate of sequencing errors, compared to short reads. It is not clear if the trade-off between longer length versus higher sequencing errors will increase or decrease classification and profiling performance.Results: We compared performance of popular metagenomic classifiers on short reads and longer reads, which are assembled from the same short reads. When using a number of popular assemblers to assemble long reads from the short reads, we discovered that most classifiers made fewer predictions with longer reads and that they achieved higher classification performance on synthetic metagenomic data. Specifically, across most classifiers, we observed a significant increase in precision, while recall remained the same, resulting in higher overall classification performance. On real metagenomic data, we observed a similar trend that classifiers made fewer predictions. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall with longer reads.Conclusions: This finding has two main implications. First, it suggests that classifying species in metagenomic environments can be achieved with higher overall performance simply by assembling short reads. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall as shorter reads. Second, this finding suggests that it might be a good idea to consider utilizing long-read technologies in species classification for metagenomic applications. Current long-read technologies tend to have higher sequencing errors and are more expensive compared to short-read technologies. The trade-offs between the pros and cons should be investigated.

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VirION2: a short- and long-read sequencing and informatics workflow to study the genomic diversity of viruses in nature

10.1101/2020.10.28.359364 ◽

2020 ◽

Author(s):

Olivier Zablocki ◽

Michelle Michelsen ◽

Marie Burris ◽

Natalie Solonenko ◽

Joanna Warwick-Dugdale ◽

...

Keyword(s):

Error Correction ◽

Previous Method ◽

Genomic Diversity ◽

Viral Metagenomics ◽

Natural Seawater ◽

Natural Ecosystem ◽

Gene Markers ◽

Short Read ◽

Long Reads ◽

Long Read

ABSTRACTMicrobes play fundamental roles in shaping natural ecosystem properties and functions, but do so under constraints imposed by their viral predators. However, studying viruses in nature can be challenging due to low biomass and the lack of universal gene markers. Though metagenomic short-read sequencing has greatly improved our virus ecology toolkit— and revealed many critical ecosystem roles for viruses — microdiverse populations and fine-scale genomic traits are missed. Some of these microdiverse populations are abundant and the missed regions may be of interest for identifying selection pressures that underpin evolutionary constraints associated with hosts and environments. Though long-read sequencing promises complete virus genomes on single reads, it currently suffers from high DNA requirements and sequencing errors that limit accurate gene prediction. Here we introduce VirION2, an integrated short- and long-read metagenomic wet-lab and informatics pipeline that updates our previous method (VirION) to further enhance the utility of long-read viral metagenomics. Using a viral mock community, we first optimized laboratory protocols (polymerase choice, DNA shearing size, PCR cycling) to enable 76% longer reads (now median length of 6,965 bp) from 100-fold less input DNA (now 1 nanogram). Using a virome from a natural seawater sample, we compared viromes generated with VirION2 against other library preparation options (unamplified, original VirION, and short-read), and optimized downstream informatics for improved long-read error correction and assembly. VirION2 assemblies combined with short-read based data (‘enhanced’viromes), provided significant improvements over VirION libraries in the recovery of longer and more complete viral genomes, and our optimized error-correction strategy using long- and short-read data achieved 99.97% accuracy. In the seawater virome, VirION2 assemblies captured 5,161 viral populations (including all of the virus populations observed in the other assemblies), 30% of which were uniquely assembled through inclusion of long-reads, and 22% of the top 10% most abundant virus populations derived from assembly of long-reads. Viral populations unique to VirION2 assemblies had significantly higher microdiversity, which may explain why short-read virome approaches failed to capture them. These findings suggest the VirION2 sample prep and workflow (updated at protocols.io) can help researchers better investigate the virosphere, even from challenging low-biomass samples. Our new protocols are available to the research community on protocols.io as a ‘living document’ to facilitate dissemination of updates to keep pace with the rapid evolution of long read sequencing technology. Taken together, the addition of long-reads uniquely enabled the recovery of 22% of the most abundant viruses—that would have been missed in short-read only assemblies.

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Fast-SG: An alignment-free algorithm for hybrid assembly

10.1101/209122 ◽

2017 ◽

Author(s):

Alex Di Genova ◽

Gonzalo A. Ruz ◽

Marie-France Sagot ◽

Alejandro Maass

Keyword(s):

De Novo ◽

Reference Level ◽

Hybrid Assembly ◽

Short Read ◽

Sequencing Technologies ◽

Alignment Free ◽

Long Reads ◽

Long Read ◽

Definition Of ◽

Large Genomes

ABSTRACTLong read sequencing technologies are the ultimate solution for genome repeats, allowing near reference level reconstructions of large genomes. However, long read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods which combine short and long read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. In this paper, we propose a new method, called FAST-SG, which uses a new ultra-fast alignment-free algorithm specifically designed for constructing a scaffolding graph using light-weight data structures. FAST-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how FAST-SG outperforms the state-of-the-art short read aligners when building the scaffolding graph, and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using FAST-SG with shallow long read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878).

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A hybrid and scalable error correction algorithm for indel and substitution errors of long reads

BMC Genomics ◽

10.1186/s12864-019-6286-9 ◽

2019 ◽

Vol 20 (S11) ◽

Author(s):

Arghya Kusum Das ◽

Sayan Goswami ◽

Kisung Lee ◽

Seung-Jong Park

Keyword(s):

Error Correction ◽

Error Rates ◽

De Bruijn Graph ◽

Correction Algorithm ◽

Short Read ◽

Short Reads ◽

Long Reads ◽

Long Read ◽

De Bruijn ◽

Error Correction Algorithm

Abstract Background Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the PacBio long-read sequences.ParLECH first constructs a de Bruijn graph from the short reads, and then replaces the indel error regions of the long reads with their corresponding widest path (or maximum min-coverage path) in the short read-based de Bruijn graph. ParLECH then utilizes the k-mer coverage information of the short reads to divide each long read into a sequence of low and high coverage regions, followed by a majority voting to rectify each substituted error base. Results ParLECH outperforms latest state-of-the-art hybrid error correction methods on real PacBio datasets. Our experimental evaluation results demonstrate that ParLECH can correct large-scale real-world datasets in an accurate and scalable manner. ParLECH can correct the indel errors of human genome PacBio long reads (312 GB) with Illumina short reads (452 GB) in less than 29 h using 128 compute nodes. ParLECH can align more than 92% bases of an E. coli PacBio dataset with the reference genome, proving its accuracy. Conclusion ParLECH can scale to over terabytes of sequencing data using hundreds of computing nodes. The proposed hybrid error correction methodology is novel and rectifies both indel and substitution errors present in the original long reads or newly introduced by the short reads.

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Improved Transcriptome Assembly Using a Hybrid of Long and Short Reads with StringTie

10.1101/2021.12.08.471868 ◽

2021 ◽

Author(s):

Alaina Shumate ◽

Brandon Wong ◽

Geo Pertea ◽

Mihaela Pertea

Keyword(s):

Rna Sequencing ◽

Open Source Software ◽

Transcriptome Assembly ◽

Simulated Data ◽

Real Data ◽

Short Read ◽

Short Reads ◽

Long Reads ◽

Long Read ◽

Improved Accuracy

Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are unable to span multiple exons. Long-read technology can capture full-length transcripts, but its high error rate often leads to mis-identified splice sites, and its low throughput makes quantification difficult. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus,and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie.

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