scholarly journals Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads

2015 ◽  
Author(s):  
Chengxi Ye ◽  
Sam Ma
Keyword(s):  
Variant Calling ◽  
Genomic Region ◽  
Error Rates ◽  
Consensus Algorithm ◽  
High Quality ◽  
Consensus Sequences ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Testing Dataset ◽  
Oxford Nanopore

Motivation: The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its error rates are estimated in the range of 15-40%, much higher than the previous generation (approximately 1%). Fundamental tasks such as genome assembly and variant calling require us to obtain high quality sequences from these long erroneous sequences. Results: In this paper we describe a versatile and efficient linear complexity consensus algorithm Sparc that builds a sparse k-mer graph using a collection of sequences from the same genomic region. The heaviest path approximates the most likely genome sequence (consensus) and is sought through a sparsity-induced reweighted graph. Experiments show that our algorithm can efficiently provide high-quality consensus sequences with error rate <0.5% using both PacBio and Oxford Nanopore sequencing technologies. Compared with the existing approaches, Sparc calculates the consensus with higher accuracy, uses 80% less memory, and is 5x faster, approximately. Availability: The source code is available for download at http://sourceforge.net/p/sparc-consensus/code/ and a testing dataset is available: https://www.dropbox.com/sh/trng8vdaeqywx1e/AAASJesLVAJZcbORkU9f4LuBa?dl=0 (Please copy the link to a browser to access if directly clicking the link fails)

scholarly journals Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads

2015 ◽  
Author(s):  
Chengxi Ye ◽  
Sam Ma
Keyword(s):  
Variant Calling ◽  
Genomic Region ◽  
Error Rates ◽  
Consensus Algorithm ◽  
High Quality ◽  
Consensus Sequences ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Testing Dataset ◽  
Oxford Nanopore

Motivation: The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its error rates are estimated in the range of 15-40%, much higher than the previous generation (approximately 1%). Fundamental tasks such as genome assembly and variant calling require us to obtain high quality sequences from these long erroneous sequences. Results: In this paper we describe a versatile and efficient linear complexity consensus algorithm Sparc that builds a sparse k-mer graph using a collection of sequences from the same genomic region. The heaviest path approximates the most likely genome sequence (consensus) and is sought through a sparsity-induced reweighted graph. Experiments show that our algorithm can efficiently provide high-quality consensus sequences with error rate <0.5% using both PacBio and Oxford Nanopore sequencing technologies. Compared with the existing approaches, Sparc calculates the consensus with higher accuracy, uses 80% less memory, and is 5x faster, approximately. Availability: The source code is available for download at http://sourceforge.net/p/sparc-consensus/code/ and a testing dataset is available: https://www.dropbox.com/sh/trng8vdaeqywx1e/AAASJesLVAJZcbORkU9f4LuBa?dl=0 (Please copy the link to a browser to access if directly clicking the link fails)

scholarly journals Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2016 ◽  
Author(s):  
Chengxi Ye ◽  
Zhanshan (Sam) Ma
Keyword(s):  
Error Rate ◽  
Genome Assembly ◽  
De Novo ◽  
Consensus Sequence ◽  
Variant Calling ◽  
Error Rates ◽  
Consensus Algorithm ◽  
High Quality ◽  
Oxford Nanopore ◽  
Generation Sequencing

Motivation.The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its current error rates are estimated in the range of 15–40%, significantly higher than those of the prevalent next generation sequencing (NGS) technologies (less than 1%). Fundamental bioinformatics tasks such asde novogenome assembly and variant calling require high-quality sequences that need to be extracted from these long but erroneous 3GS sequences.Results.We describe a versatile and efficient linear complexity consensus algorithm Sparc to facilitatede novogenome assembly. Sparc builds a sparse k-mer graph using a collection of sequences from a targeted genomic region. The heaviest path which approximates the most likely genome sequence is searched through a sparsity-induced reweighted graph as the consensus sequence. Sparc supports using NGS and 3GS data together, which leads to significant improvements in both cost efficiency and computational efficiency. Experiments with Sparc show that our algorithm can efficiently provide high-quality consensus sequences using both PacBio and Oxford Nanopore sequencing technologies. With only 30× PacBio data, Sparc can reach a consensus with error rate <0.5%. With the more challenging Oxford Nanopore data, Sparc can also achieve similar error rate when combined with NGS data. Compared with the existing approaches, Sparc[i] calculates the consensus with higher accuracy, uses 80% less memory and time, approximately. The source code is available for download athttps://github.com/yechengxi/Sparc.

scholarly journals Benchmarking of long-read correction methods

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Juliane C Dohm ◽  
Philipp Peters ◽  
Nancy Stralis-Pavese ◽  
Heinz Himmelbauer
Keyword(s):  
Error Rate ◽  
Total Error ◽  
Error Rates ◽  
High Rate ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Oxford Nanopore ◽  
Long Read ◽  
Oxford Nanopore Technologies ◽  
Generation Sequencing

Abstract Third-generation sequencing technologies provided by Pacific Biosciences and Oxford Nanopore Technologies generate read lengths in the scale of kilobasepairs. However, these reads display high error rates, and correction steps are necessary to realize their great potential in genomics and transcriptomics. Here, we compare properties of PacBio and Nanopore data and assess correction methods by Canu, MARVEL and proovread in various combinations. We found total error rates of around 13% in the raw datasets. PacBio reads showed a high rate of insertions (around 8%) whereas Nanopore reads showed similar rates for substitutions, insertions and deletions of around 4% each. In data from both technologies the errors were uniformly distributed along reads apart from noisy 5′ ends, and homopolymers appeared among the most over-represented kmers relative to a reference. Consensus correction using read overlaps reduced error rates to about 1% when using Canu or MARVEL after patching. The lowest error rate in Nanopore data (0.45%) was achieved by applying proovread on MARVEL-patched data including Illumina short-reads, and the lowest error rate in PacBio data (0.42%) was the result of Canu correction with minimap2 alignment after patching. Our study provides valuable insights and benchmarks regarding long-read data and correction methods.

scholarly journals NGSpeciesID: DNA barcode and amplicon consensus generation from long-read sequencing data

2020 ◽  
Author(s):  
Kristoffer Sahlin ◽  
Marisa Lim ◽  
Stefan Prost
Keyword(s):  
High Throughput Sequencing ◽  
Dna Barcode ◽  
Amplicon Sequencing ◽  
Error Rates ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Consensus Sequences ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
Long Read

Third generation sequencing technologies, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), have gained popularity over the last years. These platforms can generate millions of long read sequences. This is not only advantageous for genome sequencing projects, but also for amplicon-based high-throughput sequencing experiments, such as DNA barcoding. However, the relatively high error rates associated with these technologies still pose challenges for generating high quality consensus sequences. Here we present NGSpeciesID, a program which can generate highly accurate consensus sequences from long-read amplicon sequencing technologies, including ONT and PacBio. The tool includes clustering of the reads to help filter out contaminants or reads with high error rates and employs polishing strategies specific to the appropriate sequencing platform. We show that NGSpeciesID produces consensus sequences with improved usability by minimizing preprocessing and software installation and scalability by enabling rapid processing of hundreds to thousands of samples, while maintaining similar consensus accuracy as current pipelines

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 PhaseME: Automatic rapid assessment of phasing quality and phasing improvement

GigaScience ◽  
2020 ◽  
Vol 9 (7) ◽  
Author(s):  
Sina Majidian ◽  
Fritz J Sedlazeck
Keyword(s):  
Rapid Assessment ◽  
Linkage Data ◽  
Universal Method ◽  
High Quality ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Linkage Information ◽  
Quality Assessments

Abstract Background The detection of which mutations are occurring on the same DNA molecule is essential to predict their consequences. This can be achieved by phasing the genomic variations. Nevertheless, state-of-the-art haplotype phasing is currently a black box in which the accuracy and quality of the reconstructed haplotypes are hard to assess. Findings Here we present PhaseME, a versatile method to provide insights into and improvement of sample phasing results based on linkage data. We showcase the performance and the importance of PhaseME by comparing phasing information obtained from Pacific Biosciences including both continuous long reads and high-quality consensus reads, Oxford Nanopore Technologies, 10x Genomics, and Illumina sequencing technologies. We found that 10x Genomics and Oxford Nanopore phasing can be significantly improved while retaining a high N50 and completeness of phase blocks. PhaseME generates reports and summary plots to provide insights into phasing performance and correctness. We observed unique phasing issues for each of the sequencing technologies, highlighting the necessity of quality assessments. PhaseME is able to decrease the Hamming error rate significantly by 22.4% on average across all 5 technologies. Additionally, a significant improvement is obtained in the reduction of long switch errors. Especially for high-quality consensus reads, the improvement is 54.6% in return for only a 5% decrease in phase block N50 length. Conclusions PhaseME is a universal method to assess the phasing quality and accuracy and improves the quality of phasing using linkage information. The package is freely available at https://github.com/smajidian/phaseme.

scholarly journals Improving the Chromosome-Level Genome Assembly of the Siamese Fighting Fish (Betta splendens) in a University Master’s Course

2020 ◽  
Vol 10 (7) ◽  
pp. 2179-2183 ◽  
Author(s):  
Stefan Prost ◽  
Malte Petersen ◽  
Martin Grethlein ◽  
Sarah Joy Hahn ◽  
Nina Kuschik-Maczollek ◽  
...  
Keyword(s):  
Genome Assembly ◽  
High Throughput Sequencing ◽  
Siamese Fighting Fish ◽  
Betta Splendens ◽  
High Quality ◽  
Sequencing Platform ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
Long Read ◽  
Chromosome Level

Ever decreasing costs along with advances in sequencing and library preparation technologies enable even small research groups to generate chromosome-level assemblies today. Here we report the generation of an improved chromosome-level assembly for the Siamese fighting fish (Betta splendens) that was carried out during a practical university master’s course. The Siamese fighting fish is a popular aquarium fish and an emerging model species for research on aggressive behavior. We updated the current genome assembly by generating a new long-read nanopore-based assembly with subsequent scaffolding to chromosome-level using previously published Hi-C data. The use of ∼35x nanopore-based long-read data sequenced on a MinION platform (Oxford Nanopore Technologies) allowed us to generate a baseline assembly of only 1,276 contigs with a contig N50 of 2.1 Mbp, and a total length of 441 Mbp. Scaffolding using the Hi-C data resulted in 109 scaffolds with a scaffold N50 of 20.7 Mbp. More than 99% of the assembly is comprised in 21 scaffolds. The assembly showed the presence of 96.1% complete BUSCO genes from the Actinopterygii dataset indicating a high quality of the assembly. We present an improved full chromosome-level assembly of the Siamese fighting fish generated during a university master’s course. The use of ∼35× long-read nanopore data drastically improved the baseline assembly in terms of continuity. We show that relatively in-expensive high-throughput sequencing technologies such as the long-read MinION sequencing platform can be used in educational settings allowing the students to gain practical skills in modern genomics and generate high quality results that benefit downstream research projects.

scholarly journals Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Davide Bolognini ◽  
Alberto Magi
Keyword(s):  
Variant Calling ◽  
Research Report ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Factors Affecting ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Sequencing Studies ◽  
Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

scholarly journals LRez: C ++ API and toolkit for analyzing and managing Linked-Reads data

2021 ◽  
Author(s):  
Pierre Morisse ◽  
Claire Lemaitre ◽  
Fabrice Legeai
Keyword(s):  
Genome Assembly ◽  
Low Cost ◽  
Variant Calling ◽  
Supplementary Information ◽  
Supplementary Data ◽  
High Quality ◽  
Dna Molecule ◽  
Sequencing Technologies ◽  
Wide Range ◽  
Genomic Regions

Abstract Motivation Linked-Reads technologies combine both the high-quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. Results We introduce LRez, a C ++ API and toolkit which allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. Availability and implementation LRez is implemented in C ++, supported on Unix-based platforms, and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary information Supplementary data are available at Bioinformatics Advances

scholarly journals RNA Transcriptome Mapping with GraphMap

2017 ◽  
Author(s):  
Krešimir Križanović ◽  
Ivan Sović ◽  
Ivan Krpelnik ◽  
Mile Šikić
Keyword(s):  
Third Generation ◽  
Sequencing Data ◽  
Mapping Algorithm ◽  
Gene Annotations ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Oxford Nanopore ◽  
Rna Mapping ◽  
Synthetic Datasets ◽  
Generation Sequencing

AbstractNext generation sequencing technologies have made RNA sequencing widely accessible and applicable in many areas of research. In recent years, 3rd generation sequencing technologies have matured and are slowly replacing NGS for DNA sequencing. This paper presents a novel tool for RNA mapping guided by gene annotations. The tool is an adapted version of a previously developed DNA mapper – GraphMap, tailored for third generation sequencing data, such as those produced by Pacific Biosciences or Oxford Nanopore Technologies devices. It uses gene annotations to generate a transcriptome, uses a DNA mapping algorithm to map reads to the transcriptome, and finally transforms the mappings back to genome coordinates. Modified version of GraphMap is compared on several synthetic datasets to the state-of-the-art RNAseq mappers enabled to work with third generation sequencing data. The results show that our tool outperforms other tools in general mapping quality.

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