scholarly journals ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning

2019 ◽  
Author(s):  
Angel Ruiz-Reche ◽  
Joel A. Indi ◽  
Ivan de la Rubia ◽  
Eduardo Eyras
Keyword(s):  
De Novo ◽  
Functional Characterization ◽  
Cost Effective ◽  
Error Rates ◽  
Cdna Libraries ◽  
Rna Molecules ◽  
Sequencing Technologies ◽  
Long Reads ◽  
A Genome ◽  
Long Read

Long-read sequencing technologies allow the systematic interrogation of transcriptomes from any species. However, functional characterization requires the determination of the correct 5’-to-3’ orientation of reads. Oxford Nanopore Technologies (ONT) allows the direct measurement of RNA molecules in the native orientation (Garalde et al. 2018), but sequencing of complementary-DNA (cDNA) libraries yields generally a larger number of reads (Workman et al. 2018). Although strand-specific adapters can be used, error rates hinder their detection. Current methods rely on the comparison to a genome or transcriptome reference (Wyman and Mortazavi 2018; Workman et al. 2018) or on the use of additional technologies (Fu et al. 2018), which limits the applicability of rapid and cost-effective long-read sequencing for transcriptomics beyond model species. To facilitate the interrogation of transcriptomes de-novo in species or samples for which a genome or transcriptome reference is not available, we have developed ReorientExpress (https://github.com/comprna/reorientexpress), a new tool to perform reference-free orientation of ONT reads from a cDNA library, with our without stranded adapters. ReorientExpress uses a deep neural network (DNN) to predict the orientation of cDNA long-reads independently of adapters and without using a reference.

scholarly journals I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads

2020 ◽  
Author(s):  
Sina Baharlouei ◽  
Meisam Razaviyayn ◽  
Elizabeth Tseng ◽  
David Tse
Keyword(s):  
Computational Complexity ◽  
De Novo ◽  
Error Rates ◽  
De Novo Transcriptome ◽  
Transcript Isoforms ◽  
Significant Challenge ◽  
Sequencing Technologies ◽  
Alignment Free ◽  
Long Reads ◽  
Long Read

Long-read sequencing technologies demonstrate high potential for de novo discovery of complex transcript isoforms, but high error rates pose a significant challenge. Existing error correction methods rely on clustering reads based on isoform-level alignment and cannot be efficiently scaled. We propose a new method, I-CONVEX, that performs fast, alignment-free isoform clustering with almost linear computational complexity, and leads to better consensus accuracy on simulated, synthetic, and real datasets.

scholarly journals Impact of Chimera-less Long Reads on Metagenomics of Human Gut Viromes Treated With Multiple Displacement Amplification

2020 ◽  
Author(s):  
Yuya Kiguchi ◽  
Suguru Nishijima ◽  
Naveen Kumar ◽  
Masahira Hattori ◽  
Wataru Suda
Keyword(s):  
De Novo ◽  
Multiple Displacement Amplification ◽  
Read Length ◽  
Human Gut ◽  
Average Read Length ◽  
Sequencing Technologies ◽  
Genomic Complexity ◽  
Long Reads ◽  
Long Read ◽  
The Impact

Abstract Background: The ecological and biological features of the indigenous phage community (virome) in the human gut microbiome are poorly understood, possibly due to many fragmented contigs and fewer complete genomes based on conventional short-read metagenomics. Long-read sequencing technologies have attracted attention as an alternative approach to reconstruct long and accurate contigs from microbial communities. However, the impact of long-read metagenomics on human gut virome analysis has not been well evaluated. Results: Here we present chimera-less PacBio long-read metagenomics of multiple displacement amplification (MDA)-treated human gut virome DNA. The method included the development of a novel bioinformatics tool, SACRA (Split Amplified Chimeric Read Algorithm), which efficiently detects and splits numerous chimeric reads in PacBio reads from the MDA-treated virome samples. SACRA treatment of PacBio reads from five samples markedly reduced the average chimera ratio from 72 to 1.5%, generating chimera-less PacBio reads with an average read-length of 1.8 kb. De novo assembly of the chimera-less long reads generated contigs with an average N50 length of 11.1 kb, whereas those of MiSeq short reads from the same samples were 0.7 kb, dramatically improving contig extension. Alignment of both contig sets generated 378 high-quality merged contigs (MCs) composed of the minimum scaffolds of 434 MiSeq and 637 PacBio contigs, respectively, and also identified numerous MiSeq short fragmented contigs ≤500 bp additionally aligned to MCs, which possibly originated from a small fraction of MiSeq chimeric reads. The alignment also revealed that fragmentations of the scaffolded MiSeq contigs were caused primarily by genomic complexity of the community, including local repeats, hypervariable regions, and highly conserved sequences in and between the phage genomes. We identified 142 complete and near-complete phage genomes including 108 novel genomes, varying from 5 to 185 kb in length, the majority of which were predicted to be Microviridae phages including several variants with homologous but distinct genomes, which were fragmented in MiSeq contigs. Conclusions: Long-read metagenomics coupled with SACRA provides an improved method to reconstruct accurate and extended phage genomes from MDA-treated virome samples of the human gut, and potentially from other environmental virome samples.

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

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).

scholarly journals Ultra-low input single tube linked-read library method enables short-read NGS systems to generate highly accurate and economical long-range sequencing information for de novo genome assembly and haplotype phasing

2019 ◽  
Author(s):  
Zhoutao Chen ◽  
Long Pham ◽  
Tsai-Chin Wu ◽  
Guoya Mo ◽  
Yu Xia ◽  
...  
Keyword(s):  
Long Range ◽  
De Novo ◽  
Low Cost ◽  
Cost Effective ◽  
De Novo Genome Assembly ◽  
Short Read ◽  
Single Tube ◽  
Haplotype Phasing ◽  
A Genome ◽  
Long Read

AbstractLong-range sequencing information is required for haplotype phasing, de novo assembly and structural variation detection. Current long-read sequencing technologies can provide valuable long-range information but at a high cost with low accuracy and high DNA input requirement. We have developed a single-tube Transposase Enzyme Linked Long-read Sequencing (TELL-Seq™) technology, which enables a low-cost, high-accuracy and high-throughput short-read next generation sequencer to routinely generate over 100 Kb long-range sequencing information with as little as 0.1 ng input material. In a PCR tube, millions of clonally barcoded beads are used to uniquely barcode long DNA molecules in an open bulk reaction without dilution and compartmentation. The barcode linked reads are used to successfully assemble genomes ranging from microbes to human. These linked-reads also generate mega-base-long phased blocks and provide a cost-effective tool for detecting structural variants in a genome, which are important to identify compound heterozygosity in recessive Mendelian diseases and discover genetic drivers and diagnostic biomarkers in cancers.

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 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 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 Highly-accurate long-read sequencing improves variant detection and assembly of a human genome

2019 ◽  
Author(s):  
Aaron M. Wenger ◽  
Paul Peluso ◽  
William J. Rowell ◽  
Pi-Chuan Chang ◽  
Richard J. Hall ◽  
...  
Keyword(s):  
Single Molecule ◽  
De Novo ◽  
Structural Variants ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Variant Detection ◽  
High Quality Genome ◽  
Circular Consensus Sequencing

AbstractThe major DNA sequencing technologies in use today produce either highly-accurate short reads or noisy long reads. We developed a protocol based on single-molecule, circular consensus sequencing (CCS) to generate highly-accurate (99.8%) long reads averaging 13.5 kb and applied it to sequence the well-characterized human HG002/NA24385. We optimized existing tools to comprehensively detect variants, achieving precision and recall above 99.91% for SNVs, 95.98% for indels, and 95.99% for structural variants. We estimate that 2,434 discordances are correctable mistakes in the high-quality Genome in a Bottle benchmark. Nearly all (99.64%) variants are phased into haplotypes, which further improves variant detection. De novo assembly produces a highly contiguous and accurate genome with contig N50 above 15 Mb and concordance of 99.998%. CCS reads match short reads for small variant detection, while enabling structural variant detection and de novo assembly at similar contiguity and markedly higher concordance than noisy long reads.

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.

Sign in / Sign up

Export Citation Format

Share Document