scholarly journals Sequencing of Sars-CoV-2 genome using different Nanopore chemistries

2021 ◽  
Author(s):  
Oscar Gonzalez-Recio ◽  
Monica Gutierrez-Rivas ◽  
Ramon Peiro-Pastor ◽  
Pilar Aguilera-Sepulveda ◽  
Cristina Cano-Gomez ◽  
...  
Keyword(s):  
Error Rate ◽  
Consensus Sequence ◽  
Flow Cell ◽  
Epidemiological Surveillance ◽  
Nanopore Sequencing ◽  
Efficient Tool ◽  
Flow Cells ◽  
Lower Error ◽  
Lower Error Rate ◽  
Cost Efficient

Nanopore sequencing has emerged as a rapid and cost-efficient tool for diagnostic and epidemiological surveillance of SARS-CoV-2 during the COVID-19 pandemic. This study compared results from sequencing the SARS-CoV-2 genome using R9 vs R10 flow cells and Rapid Barcoding Kit (RBK) vs Ligation Sequencing Kit (LSK). The R9 chemistry provided a lower error rate (3.5%) than R10 chemistry (7%). The SARS-CoV-2 genome includes few homopolymeric regions. Longest homopolymers were composed of 7 (TTTTTTT) and 6 (AAAAAA) nucleotides. The R10 chemistry resulted in a lower rate of deletions in timine and adenine homopolymeric regions than R9, at expenses of a larger rate (~10%) of mismatches in these regions. The LSK had a larger yield than RBK, and provided longer reads than RBK. It also resulted in a larger percentage of aligned reads (99% vs 93%) and also in a complete consensus genome. The results from this study suggest that the LSK used on a R9 flow cell could maximize the yield and accuracy of the consensus sequence when used in epidemiological surveillance of SARS-CoV-2.

scholarly journals Nanopore Sequencing of a Forensic STR Multiplex Reveals Loci Suitable for Single-Contributor STR Profiling

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 381 ◽  
Author(s):  
Olivier Tytgat ◽  
Yannick Gansemans ◽  
Jana Weymaere ◽  
Kaat Rubben ◽  
Dieter Deforce ◽  
...  
Keyword(s):  
Tandem Repeats ◽  
Massively Parallel Sequencing ◽  
Flow Cell ◽  
Nanopore Sequencing ◽  
Flow Cells ◽  
Pattern Complexity ◽  
Str Loci ◽  
Oxford Nanopore ◽  
Oxford Nanopore Technologies ◽  
Short Tandem

Nanopore sequencing for forensic short tandem repeats (STR) genotyping comes with the advantages associated with massively parallel sequencing (MPS) without the need for a high up-front device cost, but genotyping is inaccurate, partially due to the occurrence of homopolymers in STR loci. The goal of this study was to apply the latest progress in nanopore sequencing by Oxford Nanopore Technologies in the field of STR genotyping. The experiments were performed using the state of the art R9.4 flow cell and the most recent R10 flow cell, which was specifically designed to improve consensus accuracy of homopolymers. Two single-contributor samples and one mixture sample were genotyped using Illumina sequencing, Nanopore R9.4 sequencing, and Nanopore R10 sequencing. The accuracy of genotyping was comparable for both types of flow cells, although the R10 flow cell provided improved data quality for loci characterized by the presence of homopolymers. We identify locus-dependent characteristics hindering accurate STR genotyping, providing insights for the design of a panel of STR loci suited for nanopore sequencing. Repeat number, the number of different reference alleles for the locus, repeat pattern complexity, flanking region complexity, and the presence of homopolymers are identified as unfavorable locus characteristics. For single-contributor samples and for a limited set of the commonly used STR loci, nanopore sequencing could be applied. However, the technology is not mature enough yet for implementation in routine forensic workflows.

scholarly journals NanoReviser: An Error-correction Tool for Nanopore Sequencing Based on a Deep Learning Algorithm

2020 ◽  
Author(s):  
Luotong Wang ◽  
Li Qu ◽  
Longshu Yang ◽  
Yiying Wang ◽  
Huaiqiu Zhu
Keyword(s):  
Deep Learning ◽  
Error Rate ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Consensus Sequence ◽  
Sequencing Error ◽  
Nanopore Sequencing ◽  
Electrical Signals ◽  
E Coli ◽  
Deep Learning Algorithm

AbstractNanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. Since 2014, Oxford Nanopore Technologies (ONT) has developed a series of devices based on nanopore sequencing to produce very long reads, with an expected impact on genomics. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Although several basecalling tools have been developed for nanopore sequencing over the past years, it is still challenging to correct the sequences after applying the basecalling procedure. In this study, we developed an open-source DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers. Our results showed NanoReviser, as a post-basecalling reviser, significantly improving the basecalling quality. After being trained on standard ONT sequencing reads from public E. coli and human NA12878 datasets, NanoReviser reduced the sequencing error rate by over 5% for both the E. coli dataset and the human dataset. The performance of NanoReviser was found to be better than those of all current basecalling tools. Furthermore, we analyzed the modified bases of the E. coli dataset and added the methylation information to train our module. With the methylation annotation, NanoReviser reduced the error rate by 7% for the E. coli dataset and specifically reduced the error rate by over 10% for the regions of the sequence rich in methylated bases. To the best of our knowledge, NanoReviser is the first post-processing tool after basecalling to accurately correct the nanopore sequences without the time-consuming procedure of building the consensus sequence. The NanoReviser package is freely available at https://github.com/pkubioinformatics/NanoReviser.

Situational Awareness in Map Displays

1987 ◽  
Vol 31 (5) ◽  
pp. 533-535 ◽  
Author(s):  
William P. Marshak ◽  
Gilbert Kuperman ◽  
Eric G. Ramsey ◽  
Denise Wilson
Keyword(s):  
Error Rate ◽  
Situational Awareness ◽  
Subjective Ratings ◽  
Subjective Data ◽  
Lower Error ◽  
Lower Error Rate ◽  
Moving Plane ◽  
Simple Paths ◽  
Relationship Of ◽  
The Relationship

The effectiveness of ego-centered (moving map) and earth-centered (moving plane) displays was studied with subjects monitoring an animated aircraft situational awareness display. Other independent variables were subject experience (aircrew vs non-aircrew) and path complexity (straight vs turning). Periodically, the display blanked and probe questions were asked concerning the relationship of the aircraft to the simulated world. Questions included judgements about angles, distances, time and terrain. Simple paths elicited a 28 percent lower error rate than did complex paths. Moving map displays had a 32 percent lower error rate than moving plane displays. No other significant effects were observed. Subjective ratings by subjects after the experiment revealed unanimous preference for the moving plane display and that the moving plane condition was believed to be easier! This contradiction indicates subjective data is limited in determining display effectiveness.

Research of the Flow Sampling Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 2588-2591
Author(s):  
Yan Liu
Keyword(s):  
Error Rate ◽  
Flow Measurement ◽  
Sampling Method ◽  
Sample Space ◽  
Sampling Algorithm ◽  
Fine Grained ◽  
Lower Error ◽  
Lower Error Rate

This article presents a flow measurement sampling method based on the application group, which identify the received message and then trasmit the identified packets to the corresponding application packet sample space, after this sampling each space packet independently. Thus to ensure the network to restore the situation in the application of fine-grained has lower error rate, thereby reducing the distortion of the application distribution.

scholarly journals Detecting Image Splicing Using Merged Features in Chroma Space

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Xu ◽  
Guangjie Liu ◽  
Yuewei Dai
Keyword(s):  
Error Rate ◽  
Previous Literature ◽  
Experimental Results ◽  
Image Editing ◽  
Image Splicing ◽  
Lower Error ◽  
Lower Error Rate ◽  
Image Dataset ◽  
Splicing Detection ◽  
Markov Features

Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature.

scholarly journals Comparative Performance of the MGISEQ-2000 and Illumina X-Ten Sequencing Platforms for Paleogenomics

2021 ◽  
Vol 12 ◽  
Author(s):  
Kongyang Zhu ◽  
Panxin Du ◽  
Jianxue Xiong ◽  
Xiaoying Ren ◽  
Chang Sun ◽  
...  
Keyword(s):  
Error Rate ◽  
Comparative Performance ◽  
Lower Error ◽  
Human Dna ◽  
Duplication Rate ◽  
Lower Error Rate ◽  
Sequencing Bias ◽  
Comparable Performance ◽  
Library Complexity ◽  
Sequencing Platforms

The MGISEQ-2000 sequencer is widely used in various omics studies, but the performance of this platform for paleogenomics has not been evaluated. We here compare the performance of MGISEQ-2000 with the Illumina X-Ten on ancient human DNA using four samples from 1750BCE to 60CE. We found there were only slight differences between the two platforms in most parameters (duplication rate, sequencing bias, θ, δS, and λ). MGISEQ-2000 performed well on endogenous rate and library complexity although X-Ten had a higher average base quality and lower error rate. Our results suggest that MGISEQ-2000 and X-Ten have comparable performance, and MGISEQ-2000 can be an alternative platform for paleogenomics sequencing.

scholarly journals Taking the perspectives of many people: humanization matters

2020 ◽  
Author(s):  
Tian Ye ◽  
Fumikazu Furumi ◽  
Daniel Catarino da Silva ◽  
Antonia Hamilton
Keyword(s):  
Virtual Reality ◽  
Mental Rotation ◽  
Error Rate ◽  
Social Contexts ◽  
Virtual Human ◽  
Human Agent ◽  
Lower Error Rate ◽  
Rate Experiment ◽  
Group Member ◽  
Director Task

In a busy space, people encounter many other people with different viewpoints, but classic studies of VPT examine only one agent at a time. This paper explores the issue of selectivity in VPT when different people are available to interact with. We consider the hypothesis that humanisation impacts on VPT in four studies using virtual reality methods. Experiment 1 & 2 use the Director Task to show that for more humanised agents (an in-group member or a virtual human agent), participants were more likely to use VPT to achieve lower error rate. Experiment 3 & 4 used a two-agent social mental rotation task to show that participants are faster and more accurate to recognise items which are oriented towards a more humanised agent (an in-group member or a naturally-moving agent). All results support the claim that humanisation alters the propensity to engage in VPT in rich social contexts.

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