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.

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 WhatsHap: fast and accurate read-based phasing

2016 ◽  
Author(s):  
Marcel Martin ◽  
Murray Patterson ◽  
Shilpa Garg ◽  
Sarah O Fischer ◽  
Nadia Pisanti ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Third Generation ◽  
Compound Heterozygosity ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Improve Accuracy ◽  
Answering Questions ◽  
Generation Sequencing

AbstractRead-based phasing allows to reconstruct the haplotypes of a sample purely from sequencing reads. While phasing is an important step for answering questions about population genetics, compound heterozygosity, and to aid in clinical decision making, there has been a lack of accurate, usable and standards-based software.WhatsHap is a production-ready tool for highly accurate read-based phasing. It was designed from the beginning to leverage third-generation sequencing technologies, whose long reads can span many variants and are therefore ideal for phasing. WhatsHap works also well with second-generation data, is easy to use and will phase not only SNVs, but also indels and other variants. It is unique in its ability to combine read-based with pedigree-based phasing, allowing to further improve accuracy if multiple related samples are provided.

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.

Quality of Third Generation Sequencing

2020 ◽  
Vol 17 (12) ◽  
pp. 5205-5209
Author(s):  
Ali Elbialy ◽  
M. A. El-Dosuky ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gc Content ◽  
Error Rates ◽  
Third Generation ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing

Third generation sequencing (TGS) relates to long reads but with relatively high error rates. Quality of TGS is a hot topic, dealing with errors. This paper combines and investigates three quality related metrics. They are basecalling accuracy, Phred Quality Scores, and GC content. For basecalling accuracy, a deep neural network is adopted. The measured loss does not exceed 5.42.

scholarly journals A Sequence-Based Novel Approach for Quality Evaluation of Third-Generation Sequencing Reads

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 44 ◽  
Author(s):  
Wenjing Zhang ◽  
Neng Huang ◽  
Jiantao Zheng ◽  
Xingyu Liao ◽  
Jianxin Wang ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Training Data ◽  
Third Generation ◽  
Contig Assembly ◽  
High Quality ◽  
Promising Alternative ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing

The advent of third-generation sequencing (TGS) technologies, such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines, provides new possibilities for contig assembly, scaffolding, and high-performance computing in bioinformatics due to its long reads. However, the high error rate and poor quality of TGS reads provide new challenges for accurate genome assembly and long-read alignment. Efficient processing methods are in need to prioritize high-quality reads for improving the results of error correction and assembly. In this study, we proposed a novel Read Quality Evaluation and Selection Tool (REQUEST) for evaluating the quality of third-generation long reads. REQUEST generates training data of high-quality and low-quality reads which are characterized by their nucleotide combinations. A linear regression model was built to score the quality of reads. The method was tested on three datasets of different species. The results showed that the top-scored reads prioritized by REQUEST achieved higher alignment accuracies. The contig assembly results based on the top-scored reads also outperformed conventional approaches that use all reads. REQUEST is able to distinguish high-quality reads from low-quality ones without using reference genomes, making it a promising alternative sequence-quality evaluation method to alignment-based algorithms.

scholarly journals Nanopore base-calling from a perspective of instance segmentation

2019 ◽  
Author(s):  
Yao-zhong Zhang ◽  
Arda Akdemir ◽  
Georg Tremmel ◽  
Seiya Imoto ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Sequencing Technology ◽  
One Dimensional ◽  
Sequential Dependencies ◽  
Base Calling ◽  
Third Generation Sequencing ◽  
Pass Through ◽  
Segmentation Task ◽  
Generation Sequencing ◽  
Instance Segmentation

AbstractBackgroundNanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real time. Through detecting the change of ion currency signals during a DNA/RNA fragment’s pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore base-calling has a higher error rate than short-read base-calling. Through utilizing deep neural networks, the-state-of-the art nanopore base-callers achieve base-calling accuracy in a range from 85% to 95%.ResultIn this work, we proposed a novel base-calling approach from a perspective of instance segmentation. Different from the previous sequence labeling approaches, we formulated the base-calling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed base-caller URnano achieves competitive results compared to recently proposed CTC-featured base-caller Chiron, on the same amount of training and test data for in-domain evaluation. Our results show that formulating the base-calling problem as a one-dimensional segmentation task is a promising approach.AvailabilityThe source code and data are available at https://github.com/yaozhong/[email protected] informationSupplementary data are available at attachment online.

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