scholarly journals Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Life ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 30
Author(s):  
Konstantina Athanasopoulou ◽  
Michaela A. Boti ◽  
Panagiotis G. Adamopoulos ◽  
Paraskevi C. Skourou ◽  
Andreas Scorilas
Keyword(s):  
De Novo ◽  
Direct Detection ◽  
Transcriptional Profiling ◽  
Third Generation ◽  
De Novo Genome Assembly ◽  
Rna Molecules ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Generation Sequencing

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

AlignGraph2: similar genome-assisted reassembly pipeline for PacBio long reads

2021 ◽  
Author(s):  
Shien Huang ◽  
Xinyu He ◽  
Guohua Wang ◽  
Ergude Bao
Keyword(s):  
De Novo ◽  
Consensus Algorithm ◽  
Alignment Algorithm ◽  
Short Reads ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Stable Performance ◽  
Novel Algorithms ◽  
Generation Sequencing

Abstract Contigs assembled from the third-generation sequencing long reads are usually more complete than the second-generation short reads. However, the current algorithms still have difficulty in assembling the long reads into the ideal complete and accurate genome, or the theoretical best result [1]. To improve the long read contigs and with more and more fully sequenced genomes available, it could still be possible to use the similar genome-assisted reassembly method [2], which was initially proposed for the short reads making use of a closely related genome (similar genome) to the sequencing genome (target genome). The method aligns the contigs and reads to the similar genome, and then extends and refines the aligned contigs with the aligned reads. Here, we introduce AlignGraph2, a similar genome-assisted reassembly pipeline for the PacBio long reads. The AlignGraph2 pipeline is the second version of AlignGraph algorithm proposed by us but completely redesigned, can be inputted with either error-prone or HiFi long reads, and contains four novel algorithms: similarity-aware alignment algorithm and alignment filtration algorithm for alignment of the long reads and preassembled contigs to the similar genome, and reassembly algorithm and weight-adjusted consensus algorithm for extension and refinement of the preassembled contigs. In our performance tests on both error-prone and HiFi long reads, AlignGraph2 can align 5.7–27.2% more long reads and 7.3–56.0% more bases than some current alignment algorithm and is more efficient or comparable to the others. For contigs assembled with various de novo algorithms and aligned to similar genomes (aligned contigs), AlignGraph2 can extend 8.7–94.7% of them (extendable contigs), and obtain contigs of 7.0–249.6% larger N50 value and 5.2–87.7% smaller number of indels per 100 kbp (extended contigs). With genomes of decreased similarities, AlignGraph2 also has relatively stable performance. The AlignGraph2 software can be downloaded for free from this site: https://github.com/huangs001/AlignGraph2.

scholarly journals TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 519
Author(s):  
Danze Chen ◽  
Qianqian Zhao ◽  
Leiming Jiang ◽  
Shuaiyuan Liao ◽  
Zhigang Meng ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
De Novo ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Third Generation ◽  
Sequencing Platform ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Routine Tasks ◽  
Generation Sequencing

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis. It also prioritizes the transcripts with a visualization framework that automatically integrates rich annotation with known genomic features. TGStools is a Python package freely available at Github.

scholarly journals Evaluating approaches to find exon chains based on long reads

2016 ◽  
Author(s):  
Anna Kuosmanen ◽  
Veli Mäkinen
Keyword(s):  
Second Generation ◽  
Simulated Data ◽  
Error Rates ◽  
Third Generation ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Second Generation Sequencing ◽  
Generation Sequencing

AbstractMotivationTranscript prediction can be modelled as a graph problem where exons are modelled as nodes and reads spanning two or more exons are modelled as exon chains. PacBio third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions.ResultsWe survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity / precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy.AvailabilityThe simulated data and in-house scripts used for this article are available at http://cs.helsinki.fi/u/aekuosma/exon_chain_evaluation_publish.tar.gz.

scholarly journals HASLR: Fast Hybrid Assembly of Long Reads

2020 ◽  
Author(s):  
Ehsan Haghshenas ◽  
Hossein Asghari ◽  
Jens Stoye ◽  
Cedric Chauve ◽  
Faraz Hach
Keyword(s):  
Unmet Need ◽  
Effective Length ◽  
Third Generation ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
The One ◽  
Generation Sequencing

AbstractThird generation sequencing technologies from platforms such as Oxford Nanopore Technologies and Pacific Biosciences have paved the way for building more contiguous assemblies and complete reconstruction of genomes. The larger effective length of the reads generated with these technologies has provided a mean to overcome the challenges of short to mid-range repeats. Currently, accurate long read assemblers are computationally expensive while faster methods are not as accurate. Therefore, there is still an unmet need for tools that are both fast and accurate for reconstructing small and large genomes. Despite the recent advances in third generation sequencing, researchers tend to generate second generation reads for many of the analysis tasks. Here, we present HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples.AvailabilityHASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

scholarly journals Overcoming uncollapsed haplotypes in long-read assemblies of non-model organisms

2020 ◽  
Author(s):  
Nadège Guiglielmoni ◽  
Antoine Houtain ◽  
Alessandro Derzelle ◽  
Karine van Doninck ◽  
Jean-François Flot
Keyword(s):  
Genome Assembly ◽  
Model Organism ◽  
Model Organisms ◽  
Third Generation ◽  
Post Processing ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Generation Sequencing ◽  
Chromosome Level

Third-generation sequencing, also called long-read sequencing, is revolutionizing genome assembly: as PacBio and Nanopore technologies become more accessible in technicity and in cost, long-read assemblers flourish and are starting to deliver chromosome-level assemblies. However, these long reads are also error-prone, making the generation of a haploid reference out of a diploid genome a difficult enterprise. Although failure to properly collapse haplotypes results in fragmented and/or structurally incorrect assemblies and wreaks havoc on orthology inference pipelines, this serious issue is rarely acknowledged and dealt with in genomic projects, and an independent, comparative benchmark of the capacity of assemblers and post-processing tools to properly collapse or purge haplotypes is still lacking. To fill this gap, we tested different assembly strategies on the genome of the rotifer Adineta vaga, a non-model organism for which high coverages of both PacBio and Nanopore reads were available. The assemblers we tested (Canu, Flye, NextDenovo, Ra, Raven, Shasta and wtdbg2) exhibited strikingly different behaviors when dealing with highly heterozygous regions, resulting in variable amounts of uncollapsed haplotypes. Filtering out shorter reads generally improved haploid assemblies, and we also benchmarked three post-processing tools aimed at detecting and purging uncollapsed haplotypes in long-read assemblies: HaploMerger2, purge_haplotigs and purge_dups. Testing these strategies separately and in combination revealed several approaches able to generate haploid assemblies with genome sizes, coverage distributions, and completeness close to expectations.

IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Hongdong Li ◽  
Wenjing Zhang ◽  
Yuwen Luo ◽  
Jianxin Wang
Keyword(s):  
Sequence Similarity ◽  
Detection Methods ◽  
Sequence Information ◽  
Third Generation ◽  
Sequencing Data ◽  
Splice Isoforms ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Feature Sequence ◽  
Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

scholarly journals Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

2020 ◽  
Vol 36 (12) ◽  
pp. 3669-3679 ◽  
Author(s):  
Can Firtina ◽  
Jeremie S Kim ◽  
Mohammed Alser ◽  
Damla Senol Cali ◽  
A Ercument Cicek ◽  
...  
Keyword(s):  
Genome Analysis ◽  
Supplementary Information ◽  
Third Generation ◽  
Sequencing Technology ◽  
Base Pairs ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing ◽  
Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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