scholarly journals Improved assembly of noisy long reads by k-mer validation

2016 ◽  
Author(s):  
A. Bernardo Carvalho ◽  
Eduardo G Dupim ◽  
Gabriel Nassar
Keyword(s):  
Low Cost ◽  
Error Rates ◽  
Read Length ◽  
Celera Assembler ◽  
Short Reads ◽  
Sequencing Errors ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Validation Procedure

Genome assembly depends critically on read length. Two recent technologies, PacBio and Oxford Nanopore, produce read lengths above 20 kb, which yield genome assemblies that are vastly superior to those based on Sanger or short-reads. However, the very high error rates of both technologies (around 15%-20%) makes assembly computationally expensive and imprecise at repeats longer than the read length. Here we show that the efficiency and quality of the assembly of these noisy reads can be significantly improved at a minimal cost, by leveraging on the low error rate and low cost of Illumina short reads. Namely, k-mers from the PacBio raw reads that are not present in the Illumina reads (which account for ~95% of the distinct k-mers) are deemed as sequencing errors and ignored at the seed alignment step. By focusing on ~5% of the k-mers which are error-free, read overlap sensitivity is dramatically increased. Equally important, the validation procedure can be extended to exclude repetitive k-mers, which avoids read miscorrection at repeats and further improve the resulting assemblies. We tested the k-mer validation procedure in one long-read technology (PacBio) and one assembler (MHAP/ Celera Assembler), but is likely to yield analogous improvements with alternative long-read technologies and overlappers, such as Oxford Nanopore and BLASR/DAligner.

scholarly journals Direct sequencing of RNA with MinION Nanopore: detecting mutations based on associations

2019 ◽  
Author(s):  
Noam Harel ◽  
Moran Meir ◽  
Uri Gophna ◽  
Adi Stern
Keyword(s):  
Direct Sequencing ◽  
Error Rates ◽  
Next Generation Sequencing Data ◽  
Random Pattern ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Different Strains

Abstract One of the key challenges in the field of genetics is the inference of haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing long reads, with the potential of sequencing complete genes, and even complete genomes of viruses, in individual reads. However, MinION suffers from high error rates, rendering the detection of true variants difficult. Here, we propose a new statistical approach named AssociVar, which differentiates between true mutations and sequencing errors from direct RNA/DNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from evolved populations of the MS2 bacteriophage, whose small genome makes it ideal for MinION sequencing. AssociVar inferred several mutations in the phage genome, which were corroborated using parallel Illumina sequencing. This allowed us to reconstruct full genome viral haplotypes constituting different strains that were present in the sample. Our approach is applicable to long read sequencing data from any organism for accurate detection of bona fide mutations and inter-strain polymorphisms.

scholarly journals Direct Sequencing of RNA with MinION Nanopore: Detecting Mutations based on Associations

2019 ◽  
Author(s):  
Noam Harel ◽  
Moran Meir ◽  
Uri Gophna ◽  
Adi Stern
Keyword(s):  
Direct Sequencing ◽  
Error Rates ◽  
Next Generation Sequencing Data ◽  
Random Pattern ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Different Strains

One of the key challenges in the field of genetics is the inference of haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing long reads, with the potential of sequencing complete genes, and even complete genomes of viruses, in individual reads. However, MinION suffers from high error rates, rendering the detection of true variants difficult. Here we propose a new statistical approach named AssociVar, which differentiates between true mutations and sequencing errors from direct RNA/DNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from evolved populations of the MS2 bacteriophage, whose small genome makes it ideal for MinION sequencing. AssociVar inferred several mutations in the phage genome, which were corroborated using parallel Illumina sequencing. This allowed us to reconstruct full genome viral haplotypes constituting different strains that were present in the sample. Our approach is applicable to long read sequencing data from any organism for accurate detection of bona fide mutations and inter-strain polymorphisms.

scholarly journals Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Jean-Marc Aury ◽  
Benjamin Istace
Keyword(s):  
Single Molecule ◽  
Direct Consequence ◽  
High Quality ◽  
Sequencing Errors ◽  
Coding Regions ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Genome Assemblies

Abstract Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

scholarly journals Assembling reads improves taxonomic classification of species

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.

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

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.

scholarly journals LINKS: Scaffolding genome assemblies with kilobase-long nanopore reads

2015 ◽  
Author(s):  
Rene L Warren ◽  
Benjamin P Vandervalk ◽  
Steven JM Jones ◽  
Inanc Birol
Keyword(s):  
Genome Assembly ◽  
Error Rates ◽  
Great Promise ◽  
Genomic Information ◽  
E Coli ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
K 12 ◽  
Genome Assemblies

Owing to the complexity of the assembly problem, we do not yet have complete genome sequences. The difficulty in assembling reads into finished genomes is exacerbated by sequence repeats and the inability of short reads to capture sufficient genomic information to resolve those problematic regions. Established and emerging long read technologies show great promise in this regard, but their current associated higher error rates typically require computational base correction and/or additional bioinformatics pre-processing before they could be of value. We present LINKS, the Long Interval Nucleotide K-mer Scaffolder algorithm, a solution that makes use of the information in error-rich long reads, without the need for read alignment or base correction. We show how the contiguity of an ABySS E. coli K-12 genome assembly could be increased over five-fold by the use of beta-released Oxford Nanopore Ltd. (ONT) long reads and how LINKS leverages long-range information in S. cerevisiae W303 ONT reads to yield an assembly with less than half the errors of competing applications. Re-scaffolding the colossal white spruce assembly draft (PG29, 20 Gbp) and how LINKS scales to larger genomes is also presented. We expect LINKS to have broad utility in harnessing the potential of long reads in connecting high-quality sequences of small and large genome assembly drafts. Availability: http://www.bcgsc.ca/bioinfo/software/links

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

BMC Genomics ◽  
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.

scholarly journals Consistent ultra-long DNA sequencing with automated slow pipetting

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Trent M. Prall ◽  
Emma K. Neumann ◽  
Julie A. Karl ◽  
Cecilia G. Shortreed ◽  
David A. Baker ◽  
...  
Keyword(s):  
Low Cost ◽  
Volumetric Flow Rate ◽  
Read Length ◽  
Great Length ◽  
Nanopore Sequencing ◽  
Library Preparation ◽  
Average Read Length ◽  
Oxford Nanopore ◽  
Long Read ◽  
Significant Length

Abstract Background Oxford Nanopore Technologies’ instruments can sequence reads of great length. Long reads improve sequence assemblies by unambiguously spanning repetitive elements of the genome. Sequencing reads of significant length requires the preservation of long DNA template molecules through library preparation by pipetting reagents as slowly as possible to minimize shearing. This process is time-consuming and inconsistent at preserving read length as even small changes in volumetric flow rate can result in template shearing. Results We have designed SNAILS (Slow Nucleic Acid Instrument for Long Sequences), a 3D-printable instrument that automates slow pipetting of reagents used in long read library preparation for Oxford Nanopore sequencing. Across six sequencing libraries, SNAILS preserved more reads exceeding 100 kilobases in length and increased its libraries’ average read length over manual slow pipetting. Conclusions SNAILS is a low-cost, easily deployable solution for improving sequencing projects that require reads of significant length. By automating the slow pipetting of library preparation reagents, SNAILS increases the consistency and throughput of long read Nanopore sequencing.

scholarly journals De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

2018 ◽  
Author(s):  
Kristoffer Sahlin ◽  
Paul Medvedev
Keyword(s):  
Clustering Algorithm ◽  
De Novo ◽  
Substantial Improvement ◽  
Error Rates ◽  
Reconstruction Algorithms ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Transcript Reconstruction ◽  
Oxford Nanopore Technologies

AbstractLong-read sequencing of transcripts with PacBio Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (in order to scale) and makes use of quality values (in order to handle variable error rates). We test isONclust on three simulated and five biological datasets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large datasets. Our tool is available at https://github.com/ksahlin/isONclust.

scholarly journals Hercules: a profile HMM-based hybrid error correction algorithm for long reads

2017 ◽  
Author(s):  
Can Firtina ◽  
Ziv Bar-Joseph ◽  
Can Alkan ◽  
A. Ercument Cicek
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Machine Learning Algorithms ◽  
Read Length ◽  
Correction Algorithm ◽  
Short Reads ◽  
Error Profile ◽  
Long Reads ◽  
Long Read ◽  
Error Correction Algorithm

AbstractMotivationChoosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies.ResultsWe designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platform’s error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.AvailabilityHercules source code is available at https://github.com/BilkentCompGen/Hercules

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