Long-read amplicon denoising

Abstract Long-read next-generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies from PacBio reads. Called ‘amplicon denoising’, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not always successfully generalize to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower. On two Mock Virus Community datasets with ground truth, each sequenced on a different PacBio instrument, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method, successfully discriminating templates that differ by a just single nucleotide. Julia implementations of Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD), and a webserver interface, are freely available.

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Long-read amplicon denoising

10.1101/383794 ◽

2018 ◽

Cited By ~ 3

Author(s):

Venkatesh Kumar ◽

Thomas Vollbrecht ◽

Mark Chernyshev ◽

Sanjay Mohan ◽

Brian Hanst ◽

...

Keyword(s):

Ground Truth ◽

Amplicon Sequencing ◽

Error Rates ◽

Sequencing Error ◽

Short Read Sequencing ◽

Sequencing Technologies ◽

Medium Length ◽

Long Reads ◽

Long Read ◽

Error Profiles

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called “amplicon denoising”, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.On one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

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Amplicon Sequencing in the Era of Highly-Accurate Long Reads

ARPHA Conference Abstracts ◽

10.3897/aca.4.e65405 ◽

2021 ◽

Vol 4 ◽

Author(s):

Benjamin Callahan

Keyword(s):

Dna Sequencing ◽

New Technology ◽

Amplicon Sequencing ◽

Error Rates ◽

Important Advance ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read

An important advance in DNA sequencing has been the development of long-read sequencing technologies that produce sequencing reads of tens to hundreds of kilobases in length. However, these technologies typically have high (~8%) per-base error rates. Recently, an effectively new technology I call highly-accurate long-read sequencing has been developed, that allows for the generation of multi-kilobase reads with extremely high per-base accuracies (>99.9%). I will present and evaluate two such technologies, PacBio HiFi and LoopSeq SLR sequencing, and discuss potential metabarcoding applications of highly-accurate long-read amplicon sequencing in general.

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Factorial estimating assembly base errors using k-mer abundance difference (KAD) between short reads and genome assembled sequences

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa075 ◽

2020 ◽

Vol 2 (3) ◽

Author(s):

Cheng He ◽

Guifang Lin ◽

Hairong Wei ◽

Haibao Tang ◽

Frank F White ◽

...

Keyword(s):

Copy Number ◽

Error Rates ◽

Genome Sequences ◽

Short Reads ◽

Sequencing Technologies ◽

Insertion And Deletion ◽

Novel Approach ◽

Long Reads ◽

Long Read ◽

Genome Assemblies

Abstract Genome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists, but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as k-mer abundance difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Collectively, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses.

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rMETL: sensitive mobile element insertion detection with long read realignment

Bioinformatics ◽

10.1093/bioinformatics/btz106 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3484-3486 ◽

Cited By ~ 3

Author(s):

Tao Jiang ◽

Bo Liu ◽

Junyi Li ◽

Yadong Wang

Keyword(s):

Rapid Development ◽

Mobile Element ◽

Error Rates ◽

Supplementary Information ◽

Sequencing Error ◽

Complex Signals ◽

Element Insertion ◽

Sequencing Technologies ◽

Long Read ◽

Mobile Element Insertion

Abstract Summary Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies. Availability and implementation rMETL is available from https://github.com/hitbc/rMETL. Supplementary information Supplementary data are available at Bioinformatics online.

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Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

10.1101/632703 ◽

2019 ◽

Cited By ~ 1

Author(s):

Laura H. Tung ◽

Mingfu Shao ◽

Carl Kingsford

Keyword(s):

Single Molecule ◽

Error Rates ◽

Human Transcriptome ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Long Read ◽

Transcript Assembly ◽

Novel Isoforms ◽

Generation Sequencing

AbstractThird-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100–4000 more (for 18 human datasets) or 1100–2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4–4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie). Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

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Rapid multi-locus sequence typing direct from uncorrected long reads using Krocus

10.1101/259150 ◽

2018 ◽

Author(s):

Andrew J. Page ◽

Jacqueline A. Keane

Keyword(s):

Error Rates ◽

Multi Locus Sequence Typing ◽

Short Read Sequencing ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Standard Tool ◽

Long Read ◽

Sequence Types ◽

Very High

AbstractGenome sequencing is rapidly being adopted in reference labs and hospitals for bacterial outbreak investigation and diagnostics where time is critical. Seven gene multi-locus sequence typing is a standard tool for broadly classifying samples into sequence types, allowing, in many cases, to rule a sample in or out of an outbreak, or allowing for general characteristics about a bacterial strain to be inferred. Long read sequencing technologies, such as from PacBio or Oxford Nanopore, can produce read data within minutes of an experiment starting, unlike short read sequencing technologies which require many hours/days. However, the error rates of raw uncorrected long read data are very high. We present Krocus which can predict a sequence type directly from uncorrected long reads, and which was designed to consume read data as it is produced, providing results in minutes. It is the only tool which can do this from uncorrected long reads. We tested Krocus on over 600 samples sequenced with using long read sequencing technologies from PacBio and Oxford Nanopore. It provides sequence types on average within 90 seconds, with a sensitivity of 94% and specificity of 97%, directly from uncorrected raw sequence reads. The software is written in Python and is available under the open source license GNU GPL version 3.

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Scalable long read self-correction and assembly polishing with multiple sequence alignment

Scientific Reports ◽

10.1038/s41598-020-80757-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pierre Morisse ◽

Camille Marchet ◽

Antoine Limasset ◽

Thierry Lecroq ◽

Arnaud Lefebvre

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Correction Method ◽

Error Rates ◽

Multiple Sequence ◽

De Bruijn Graphs ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read ◽

Human Dataset

AbstractThird-generation sequencing technologies allow to sequence long reads of tens of kbp, that are expected to solve various problems. However, they display high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads, and allows to process a full human dataset, containing reads reaching up to 1.5 Mbp, in 10 days. Moreover, our experiments show that error correction with CONSENT improves the quality of Flye assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools, while providing comparable results. Furthermore, we show that, on a human dataset, assembling the raw data and polishing the assembly is less resource consuming than correcting and then assembling the reads, while providing better results. CONSENT is available at https://github.com/morispi/CONSENT.

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Detecting and phasing minor single-nucleotide variants from long-read sequencing data

Nature Communications ◽

10.1038/s41467-021-23289-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zhixing Feng ◽

Jose C. Clemente ◽

Brandon Wong ◽

Eric E. Schadt

Keyword(s):

Genetic Heterogeneity ◽

Error Rates ◽

Metagenomic Data ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read ◽

Technological Limitations

AbstractCellular 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.

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Parameter exploration improves the accuracy of long-read genome assembly

10.1101/2021.05.28.446135 ◽

2021 ◽

Author(s):

Anurag Priyam ◽

Alicja Witwicka ◽

Anindita Brahma ◽

Eckart Stolle ◽

Yannick Wurm

Keyword(s):

Genome Assembly ◽

Reference Genome ◽

Error Rates ◽

Fine Tuning ◽

Sequencing Error ◽

High Quality ◽

Long Read ◽

Genome Assemblies ◽

Error Profiles

Long-molecule sequencing is now routinely applied to generate high-quality reference genome assemblies. However, datasets differ in repeat composition, heterozygosity, read lengths and error profiles. The assembly parameters that provide the best results could thus differ across datasets. By integrating four complementary and biologically meaningful metrics, we show that simple fine-tuning of assembly parameters can substantially improve the quality of long-read genome assemblies. In particular, modifying estimates of sequencing error rates improves some metrics more than two-fold. We provide a flexible software, CompareGenomeQualities, that automates comparisons of assembly qualities for researchers wanting a straightforward mechanism for choosing among multiple assemblies.

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Estimating Assembly Base Errors Using K-mer Abundance Difference (KAD) Between Short Reads and Genome Assembled Sequences

10.1101/2020.03.17.994566 ◽

2020 ◽

Cited By ~ 1

Author(s):

Cheng He ◽

Guifang Lin ◽

Hairong Wei ◽

Haibao Tang ◽

Frank F White ◽

...

Keyword(s):

Copy Number ◽

Error Rates ◽

Genome Sequences ◽

Short Reads ◽

Sequencing Technologies ◽

Insertion And Deletion ◽

Novel Approach ◽

Long Reads ◽

Long Read ◽

Genome Assemblies

ABSTRACTGenome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as K-mer Abundance Difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Therefore, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses.

Download Full-text