scholarly journals MetaBCC-LR: metagenomics binning by coverage and composition for long reads

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i3-i11
Author(s):  
Anuradha Wickramarachchi ◽  
Vijini Mallawaarachchi ◽  
Vaibhav Rajan ◽  
Yu Lin
Keyword(s):  
Error Rates ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
Sequencing Technologies ◽  
Input Size ◽  
Long Reads ◽  
Wide Range ◽  
Long Read ◽  
Oligonucleotide Composition ◽  
Species Specific

Abstract Motivation Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyse metagenomic data, binning is considered a crucial step to characterize the different species of micro-organisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this article, we present MetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition. Results We evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving ∼13% improvement in F1-score and ∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long-read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read-based metagenomics analyses to support a wide range of applications. Availability and implementation The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR. Supplementary information Supplementary data are available at Bioinformatics online.

NextPolish: a fast and efficient genome polishing tool for long-read assembly

2019 ◽  
Vol 36 (7) ◽  
pp. 2253-2255 ◽  
Author(s):  
Jiang Hu ◽  
Junpeng Fan ◽  
Zongyi Sun ◽  
Shanlin Liu
Keyword(s):  
Error Rates ◽  
Supplementary Information ◽  
Sequencing Technologies ◽  
Large Numbers ◽  
Long Reads ◽  
Long Read ◽  
Genome Assemblies ◽  
Polishing Tool ◽  
Sequence Errors ◽  
Plant Arabidopsis Thaliana

Abstract Motivation Although long-read sequencing technologies can produce genomes with long contiguity, they suffer from high error rates. Thus, we developed NextPolish, a tool that efficiently corrects sequence errors in genomes assembled with long reads. This new tool consists of two interlinked modules that are designed to score and count K-mers from high quality short reads, and to polish genome assemblies containing large numbers of base errors. Results When evaluated for the speed and efficiency using human and a plant (Arabidopsis thaliana) genomes, NextPolish outperformed Pilon by correcting sequence errors faster, and with a higher correction accuracy. Availability and implementation NextPolish is implemented in C and Python. The source code is available from https://github.com/Nextomics/NextPolish. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BleTIES: Annotation of natural genome editing in ciliates using long read sequencing

2021 ◽  
Author(s):  
Brandon K. B. Seah ◽  
Estienne C. Swart
Keyword(s):  
Dna Sequences ◽  
Sequence Data ◽  
Low Complexity ◽  
Supplementary Information ◽  
Neighboring Element ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Element Elimination

Ciliates are single-celled eukaryotes that eliminate specific, interspersed DNA sequences (internally eliminated sequences, IESs) from their genomes during development. These are challenging to annotate and assemble because IES-containing sequences are much less abundant in the cell than those without, and IES sequences themselves often contain repetitive and low-complexity sequences. Long read sequencing technologies from Pacific Biosciences and Oxford Nanopore have the potential to reconstruct longer IESs than has been possible with short reads, and also the ability to detect correlations of neighboring element elimination. Here we present BleTIES, a software toolkit for detecting, assembling, and analyzing IESs using mapped long reads. Availability and implementation: BleTIES is implemented in Python 3. Source code is available at https://github.com/Swart-lab/bleties (MIT license), and also distributed via Bioconda. Contact: [email protected] Supplementary information: Benchmarking of BleTIES with published sequence data.

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 rMETL: sensitive mobile element insertion detection with long read realignment

2019 ◽  
Vol 35 (18) ◽  
pp. 3484-3486 ◽  
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.

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

2019 ◽  
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.

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

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.

scholarly journals Near-complete Lokiarchaeota genomes from complex environmental samples using long and short read metagenomic analyses

2019 ◽  
Author(s):  
Eva F. Caceres ◽  
William H. Lewis ◽  
Felix Homa ◽  
Tom Martin ◽  
Andreas Schramm ◽  
...  
Keyword(s):  
Large Scale ◽  
Phylogenetic Analyses ◽  
Metagenomic Data ◽  
Endosomal Sorting ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Complete Genomes ◽  
Culture Independent ◽  
Long Read

AbstractAsgard archaea is a recently proposed superphylum currently comprised of five recognised phyla: Lokiarchaeota, Thorarchaeota, Odinarchaeota, Heimdallarchaeota and Helarchaeota. Members of this group have been identified based on culture-independent approaches with several metagenome-assembled genomes (MAGs) reconstructed to date. However, most of these genomes consist of several relatively small contigs, and, until recently, no complete Asgard archaea genome is yet available. Large scale phylogenetic analyses suggest that Asgard archaea represent the closest archaeal relatives of eukaryotes. In addition, members of this superphylum encode proteins that were originally thought to be specific to eukaryotes, including components of the trafficking machinery, cytoskeleton and endosomal sorting complexes required for transport (ESCRT). Yet, these findings have been questioned on the basis that the genome sequences that underpin them were assembled from metagenomic data, and could have been subjected to contamination and other assembly artefacts. Even though several lines of evidence indicate that the previously reported findings were not affected by these issues, having access to high-quality and preferentially fully closed Asgard archaea genomes is needed to definitively close this debate. Current long-read sequencing technologies such as Oxford Nanopore allow the generation of long reads in a high-throughput manner making them suitable for their use in metagenomics. Although the use of long reads is still limited in this field, recent analyses have shown that it is feasible to obtain complete or near-complete genomes of abundant members of mock communities and metagenomes of various level of complexity. Here, we show that long read metagenomics can be successfully applied to obtain near-complete genomes of low-abundant members of complex communities from sediment samples. We were able to reconstruct six MAGs from different Lokiarchaeota lineages that show high completeness and low fragmentation, with one of them being a near-complete genome only consisting of three contigs. Our analyses confirm that the eukaryote-like features previously associated with Lokiarchaeota are not the result of contamination or assembly artefacts, and can indeed be found in the newly reconstructed genomes.

scholarly journals Long-read amplicon denoising

2018 ◽  
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.

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

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.

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

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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