scholarly journals Real-time selective sequencing using nanopores and deep learning

2021 ◽  
Author(s):  
Artem Danilevsky ◽  
Avital Luba Polsky ◽  
Noam Shomron
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Human Cell Line ◽  
Genomic Research ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Total Input ◽  
Unique Method ◽  
Cell Line Sample ◽  
Selective Sequencing

Abstract Nanopore sequencing is an emerging technology that utilizes a unique method of reading nucleic acid sequences and, at the same time, it detects various chemical modifications. Deep learning has increased in popularity as a useful technique to solve many complex computational tasks. Selective sequencing has been widely used in genomic research; although it introduces several caveats to the process of sequencing, its advantages supersede them. In this study we demonstrate an alternative method of software-based selective sequencing that is performed in real time by combining nanopore sequencing and deep learning. Our results show the feasibility of using deep learning for classifying signals from only the first 200 nucleotides in a raw nanopore sequencing signal format. Using custom deep learning models and a script utilizing "Read-Until" framework to target mitochondrial molecules in real time from a human cell line sample, we achieved a significant separation and enrichment ability of more than 2-fold. In a series of very short sequencing runs (10, 30, and 120 minutes), we identified genomic and mitochondrial reads with accuracy above 90%, although mitochondrial DNA comprises only 0.1% of the total input material. We believe that our results will lay the foundation for rapid and selective sequencing using nanopore technology and will pave the way for future clinical applications using nanopore sequencing data.

scholarly journals CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning

iScience ◽  
2020 ◽  
Vol 23 (5) ◽  
pp. 101128 ◽  
Author(s):  
Yifan Zhang ◽  
Chi-Man Liu ◽  
Henry C.M. Leung ◽  
Ruibang Luo ◽  
Tak-Wah Lam
Keyword(s):  
Deep Learning ◽  
Nanopore Sequencing ◽  
Sequencing Data

scholarly journals Real-time demultiplexing Nanopore barcoded sequencing data with npBarcode

2017 ◽  
Author(s):  
Son Hoang Nguyen ◽  
Tania Duarte ◽  
Lachlan J. M. Coin ◽  
Minh Duc Cao
Keyword(s):  
Real Time ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Bioinformatic Tools ◽  
Oxford Nanopore ◽  
Pooled Sequencing ◽  
Cross Platform ◽  
Real Time Applications ◽  
Friendly Graphical User Interface

AbstractMotivationThe recently introduced barcoding protocol to Oxford Nanopore sequencing has increased the versatility of the technology. Several bioinformatic tools have been developed to demultiplex the barcoded reads, but none of them support the streaming analysis. This limits the use of pooled sequencing in real-time applications, which is one of the main advantages of the technology.ResultsWe introduced npBarcode, an open source and cross platform tool for barcode demultiplex in streaming fashion. npBarcode can be seamlessly integrated into a streaming analysis pipeline. The tool also provides a friendly graphical user interface through npReader, allowing the real-time visual monitoring of the sequencing progress of barcoded samples. We show that npBarcode achieves comparable accuracies to the other alternatives.AvailabilitynpBarcode is bundled in Japsa - a Java tools kit for genome analysis, and is freely available at https://github.com/hsnguyen/npBarcode.

scholarly journals MaxDEL: Accurate and Efficient Calling Genomic Deletions From Single Molecular Real-Time Sequencing Using Integrated Method

2021 ◽  
Author(s):  
Yaoxian Lv ◽  
Lei Cai ◽  
Jingyang Gao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Single Molecule ◽  
Research Result ◽  
Sequencing Data ◽  
Smrt Sequencing ◽  
Learning Methods ◽  
Genomic Deletions ◽  
Sequencing Errors

Abstract Background: Single-molecule real-time (SMRT) sequencing data are characterized by long reads and high read depth. Compared with next-generation sequencing (NGS), SMRT sequencing data can present more structural variations (SVs) and has greater advantages in calling variation. However, there are high sequencing errors and noises in SMRT sequencing data, which brings inaccurately on calling SVs from sequencing data. Most existing tools are unable to overcome the sequencing errors and detect genomic deletions. Methods and results: In this investigation, we propose a new method for calling deletions from SMRT sequencing data, called MaxDEL. MaxDEL can effectively overcome the noise of SMRT sequencing data and integrates new machine learning and deep learning technologies. Firstly, it uses machine learning method to calibrate the deletions regions from variant call format (VCF) file. Secondly, MaxDEL develops a novel feature visualization method to convert the variant features to images and uses these images to accurately call the deletions based on convolutional neural network (CNN). The result shows that MaxDEL performs better in terms of accuracy and recall for calling variants when compared with existing methods in both real data and simulative data. Conclusions: We propose a method (MAXDEL) for calling deletion variations, which effectively utilizes both machine learning and deep learning methods. We tested it with different SMRT data and evaluated its effectiveness. The research result shows that the use of machine learning and deep learning methods has great potential in calling deletion variations.

Nano-GLADIATOR: real-time detection of copy number alterations from nanopore sequencing data

2019 ◽  
Vol 35 (21) ◽  
pp. 4213-4221 ◽  
Author(s):  
Alberto Magi ◽  
Davide Bolognini ◽  
Niccoló Bartalucci ◽  
Alessandra Mingrino ◽  
Roberto Semeraro ◽  
...  
Keyword(s):  
Real Time ◽  
Copy Number ◽  
Probabilistic Approach ◽  
Supplementary Information ◽  
Genomic Alteration ◽  
Nanopore Sequencing ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
On Line ◽  
Allelic Fraction

Abstract Motivation The past few years have seen the emergence of nanopore-based sequencing technologies which interrogate single molecule of DNA and generate reads sequentially. Results In this paper, we demonstrate that, thanks to the sequentiality of the nanopore process, the data generated in the first tens of minutes of a typical MinION/GridION run can be exploited to resolve the alterations of a human genome at a karyotype level with a resolution in the order of tens of Mb, while the data produced in the first 6–12 h allow to obtain a resolution comparable to currently available array-based technologies, and thanks to a novel probabilistic approach are capable to predict the allelic fraction of genomic alteration with high accuracy. To exploit the unique characteristics of nanopore sequencing data we developed a novel software tool, Nano-GLADIATOR, that is capable to perform copy number variants/alterations detection and allelic fraction prediction during the sequencing run (‘On-line’ mode) and after experiment completion (‘Off-line’ mode). We tested Nano-GLADIATOR on publicly available (‘Off-line’ mode) and on novel whole genome sequencing dataset generated with MinION device (‘On-line’ mode) showing that our tool is capable to perform real-time copy number alterations detection obtaining good results with respect to other state-of-the-art tools. Availability and implementation Nano-GLADIATOR is freely available at https://sourceforge.net/projects/nanogladiator/. Supplementary information Supplementary data are available at Bioinformatics online.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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