Prediction-based highly sensitive CRISPR off-target validation using target-specific DNA enrichment

AbstractCRISPR effectors, which comprise a CRISPR-Cas protein and a guide (g)RNA derived from the bacterial immune system, are widely used for target-specific genome editing. When the gRNA recognizes genomic loci with sequences that are similar to the target, deleterious mutations can occur. Off-target mutations with a frequency below 0.5% remain mostly undetected by current genome-wide off-target detection techniques. Here we report a method to effectively detect extremely small amounts of mutated DNA based on predicted off-target-specific amplification. In this study, we used various genome editors to induce intracellular genome mutations, and the CRISPR amplification method detected off-target mutations at a significantly higher rate (1.6~984 fold increase) than an existing targeted amplicon sequencing method. In the near future, CRISPR amplification in combination with genome-wide off-target detection methods will allow detection of genome editor-induced off-target mutations with high sensitivity and in a non-biased manner.

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Prediction-based highly sensitive CRISPR off-target validation using target-specific DNA enrichment

10.1101/2019.12.31.889626 ◽

2019 ◽

Author(s):

Seung-Hun Kang ◽

Wi-jae Lee ◽

Ju-Hyun An ◽

Jong-Hee Lee ◽

Young-Hyun Kim ◽

...

Keyword(s):

Target Detection ◽

High Sensitivity ◽

Fold Increase ◽

Amplicon Sequencing ◽

Detection Methods ◽

Repair System ◽

Detection Techniques ◽

Amplification Method ◽

Genome Wide

AbstractCRISPR effectors, which comprise a CRISPR-Cas protein and a guide (g)RNA derived from the bacterial immune system, are widely used to induce double-strand breaks in target DNA and activate the in-vivo DNA repair system for target-specific genome editing. When the gRNA recognizes genomic loci with sequences that are similar to the target, deleterious and often carcinogenic mutations can occur. Off-target mutations with a frequency below 0.5% remain mostly undetected by current genome-wide off-target detection techniques. In this study, we developed a method to effectively detect extremely small amounts of mutated DNA based on predicted off-target-specific amplification. We used various genome editors, including CRISPR-Cpf1, Cas9, and an adenine base editor, to induce intracellular genome mutations. The CRISPR amplification method detected off-target mutations at a significantly higher rate (1.6∼984 fold increase) than did an existing targeted amplicon sequencing method. In the near future, CRISPR amplification in combination with genome-wide off-target detection methods will allow to detect genome editor-induced off-target mutations with high sensitivity and in a non-biased manner.

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RGEN-SEQ FOR HIGHLY SENSITIVE AMPLIFICATION-FREE SCREEN OF OFF-TARGET SITES OF GENE EDITORS

10.1101/2021.07.01.450795 ◽

2021 ◽

Author(s):

Alexander Kuzin ◽

Brendan Redler ◽

Jaya Onuska ◽

Alexei Slesarev

Keyword(s):

Target Detection ◽

Dna Cleavage ◽

Detailed Comparison ◽

Detection Methods ◽

Guide Rnas ◽

Coverage Bias ◽

Biochemical Assays ◽

Sequencing Method ◽

Highly Sensitive ◽

Target Sites

Sensitive detection of off-target sites produced by gene editing nucleases is crucial for developing reliable gene therapy platforms. Although several biochemical assays for the characterization of nuclease off-target effects have been recently published, they still leave plenty of room for improvement. Here we describe a sensitive, PCR-free next-generation sequencing method (RGEN-seq) for unbiased detection of double-stranded breaks generated by RNA-guided CRISPR-Cas9 endonuclease. The method is extremely simple, and it is on a par or even supersedes in sensitivity existing assays without reliance on amplification steps. The latter saves time, simplifies workflow, and removes genomic coverage bias and gaps associated with PCR and/or other enrichment procedures. RGEN-seq is fully compatible with existing off-target detection software; moreover, the unbiased nature of RGEN-seq offers a robust foundation for relating assigned DNA cleavage scores to propensity for off-target mutations in cells. A detailed comparison of RGEN-seq with other off-target detection methods is provided using a previously characterized set of guide RNAs.

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Weighted Background Suppression Target Detection Using Sparse Image Enhancement Technique for Newly Grown Tree Leaves

Remote Sensing ◽

10.3390/rs11091081 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1081 ◽

Cited By ~ 1

Author(s):

Shih-Yu Chen ◽

Chinsu Lin ◽

Shang-Ju Chuang ◽

Zhe-Yuan Kao

Keyword(s):

Target Detection ◽

Operating Characteristic ◽

Characteristic Curve ◽

Detection Methods ◽

Effect Of Temperature ◽

Background Suppression ◽

Multispectral Images ◽

Detection Techniques ◽

Aerial Vehicle ◽

Weighted Correlation

The process from leaf sprouting to senescence is a phenological response, which is caused by the effect of temperature and moisture on the physiological response during the life cycle of trees. Therefore, detecting newly grown leaves could be useful for studying tree growth or even climate change. This study applied several target detection techniques to observe the growth of leaves in unmanned aerial vehicle (UAV) multispectral images. The weighted background suppression (WBS) method was proposed in this paper to reduce the interference of the target of interest through a weighted correlation/covariance matrix. This novel technique could strengthen targets and suppress the background. This study also developed the sparse enhancement (SE) method for newly grown leaves (NGL), as sparsity has features similar to newly grown leaves. The experimental results suggested that using SE-WBS based algorithms could improve the detection performance of NGL for most detectors. For the global target detection methods, the SE-WBS version of adaptive coherence estimator (SE-WBS-ACE) refines the area under the receiver operating characteristic curve (AUC) from 0.9417 to 0.9658 and kappa from 0.3389 to 0.4484. The SE-WBS version of target constrained interference minimized filter (SE-WBS-TCIMF) increased AUC from 0.9573 to 0.9708 and kappa from 0.3472 to 0.4417; the SE-WBS version of constrained energy minimization (SE-WBS-CEM) boosted AUC from 0.9606 to 0.9713 and kappa from 0.3604 to 0.4483. For local target detection methods, the SE-WBS version of adaptive sliding window CEM (ASW SE-WBS-CEM) enhanced AUC from 0.9704 to 0.9796 and kappa from 0.4526 to 0.5121, which outperforms other methods.

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NANOPOROUS MEMBRANE FOR BIOSENSING APPLICATIONS

Nano LIFE ◽

10.1142/s1793984411000323 ◽

2012 ◽

Vol 02 (01) ◽

pp. 1230003 ◽

Cited By ~ 10

Author(s):

YANG MO ◽

TAN FEI

Keyword(s):

Low Cost ◽

High Sensitivity ◽

Detection Methods ◽

Nucleic Acid Detection ◽

Nanoporous Membranes ◽

Inorganic Membranes ◽

Detection Techniques ◽

Nanoporous Membrane ◽

Surface Functionality ◽

Surface Modification Techniques

Synthetic nanoporous membranes have been used in numerous biosensing applications, such as glucose detection, nucleic acid detection, bacteria detection, and cell-based sensing. The increased surface affinity area and enhanced output sensing signals make the nanoporous membranes increasingly attractive as biosensing platforms. Surface modification techniques can be used to improve surface properties for realizable bioanalyte immobilization, conjugation, and detection. Combined with realizable detection techniques such as electrochemical and optical detection methods, nanoporous membrane–based biosensors have advantages, including rapid response, high sensitivity, and low cost. In this paper, an overview of nanoporous membranes for biosensing application is given. Types of nanoporous membranes including polymer membranes, inorganic membranes, membranes with nanopores fabricated using nanolithography, and nanotube-based membranes are introduced. The fabrication techniques of nanoporous membranes are also discussed. The key requirements of nanoporous membranes for biosensing applications include surface functionality for bioanalyte immobilization, biocompatibility, mechanical and chemical stability, and anti-biofouling capability. The recent advances and development of nanoporous membrane–based biosensors are discussed, especially for the sensing mechanism and surface functionalization strategies. Finally, the challenges and future development of nanoporous membrane for biosensing applications are discussed.

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Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3439870 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1-18

Author(s):

Jessamyn Dahmen ◽

Diane J. Cook

Keyword(s):

Anomaly Detection ◽

Time Series Data ◽

Bayesian Optimization ◽

Detection Methods ◽

Series Data ◽

Detection Techniques ◽

Health Events ◽

Warm Start ◽

Health Related ◽

Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

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Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00549-3 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Xiang Li ◽

Jianzheng Liu ◽

Jessica Baron ◽

Khoa Luu ◽

Eric Patterson

Keyword(s):

Focal Length ◽

Ground Truth ◽

Detection Methods ◽

Viewing Angle ◽

Deep Convolutional Neural Networks ◽

Landmark Detection ◽

High Performing ◽

Detection Techniques ◽

And Performance ◽

Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

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Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

Applied Sciences ◽

10.3390/app11125685 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5685

Author(s):

Hosam Aljihani ◽

Fathy Eassa ◽

Khalid Almarhabi ◽

Abdullah Algarni ◽

Abdulaziz Attaallah

Keyword(s):

Attack Detection ◽

Detection Methods ◽

Software Systems ◽

Distributed Software ◽

Detection Techniques ◽

Detection Systems ◽

Novel Approach ◽

Validation Experiment ◽

Critical Issues ◽

Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

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TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

Remote Sensing ◽

10.3390/rs13132558 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2558

Author(s):

Lei Yu ◽

Haoyu Wu ◽

Zhi Zhong ◽

Liying Zheng ◽

Qiuyue Deng ◽

...

Keyword(s):

Target Detection ◽

Detection Methods ◽

Observation System ◽

Single Shot ◽

Small Target ◽

Feature Mapping ◽

Sar Images ◽

Two Stage ◽

Ship Detection ◽

Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

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State of Research on Negative Pressure Techniques Applied to Leak Detection in Liquid Pipelines

2004 International Pipeline Conference, Volumes 1, 2, and 3 ◽

10.1115/ipc2004-0621 ◽

2004 ◽

Cited By ~ 1

Author(s):

Wei Liang ◽

Lai-bin Zhang ◽

Zhao-hui Wang

Keyword(s):

Pressure Wave ◽

Analytical Techniques ◽

Leak Detection ◽

Point Of View ◽

Detection Methods ◽

Propagation Mechanism ◽

Detection Techniques ◽

Wave Detection ◽

State Of Research ◽

Signal Processing Methods

In China, the rarefaction-pressure wave techniques are widely used to diagnose the leakage fault for liquid pipelines. Many leaking propagating assumptions, such as stable single-phased flow hypothesis and none rarefaction wave front hypothesis, are often uncertain in the process of leak detection, which can easily result in some errors. Thus the rarefaction-pressure wave techniques should be integrated with other analytical techniques to compute a more accurate leak location. Additionally, the development trends of rarefaction-pressure wave techniques lie in three aspects. First, rarefaction-pressure wave detection techniques will be integrated with other compatible detection techniques and modern signal processing methods to solve the complex problems encountered in leak detection. Second, studies of rarefaction-pressure wave techniques have advanced to a new stage. The deductions on propagation mechanism of rarefaction-pressure wave have been successfully applied to determine leaks qualitatively. Third, analysis on rarefaction-pressure wave detection techniques will be made from a quantitative point of view. The quantitative data have been used to deduce leak amounts and location. The purpose of this paper is to present the recent achievements in the study of improved rarefaction-pressure wave detection techniques. The rarefaction-pressure wave detection methods, effects of incomplete information conditions, the improvements of rarefaction-pressure wave detection techniques with modified factors and propagation mechanisms are comprehensively investigated. The disfigurements of rarefaction-pressure wave are analyzed. The corresponding methods for resolving such problems as ill diagnostic information and weak amplitude values are put forward. Several methods for stronger small leakage detection ability, higher leakage positioning precision, lower false alarm rates are proposed. The application of rarefaction-pressure wave detection techniques to safety protection of liquid pipelines is also introduced. Finally, the prospect of rarefaction-pressure wave detection techniques is predicted.

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SNP discovery by amplicon sequencing and multiplex SNP genotyping in the allopolyploid species Brassica napusThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽

10.1139/g10-079 ◽

2010 ◽

Vol 53 (11) ◽

pp. 948-956 ◽

Cited By ~ 35

Author(s):

G. Durstewitz ◽

A. Polley ◽

J. Plieske ◽

H. Luerssen ◽

E. M. Graner ◽

...

Keyword(s):

Genetic Analysis ◽

Oilseed Rape ◽

Expressed Sequence Tag ◽

Diploid Species ◽

Amplicon Sequencing ◽

Snp Markers ◽

Snp Analysis ◽

Genome Wide ◽

A Genome ◽

Illumina Goldengate

Oilseed rape ( Brassica napus ) is an allotetraploid species consisting of two genomes, derived from B. rapa (A genome) and B. oleracea (C genome). The presence of these two genomes makes single nucleotide polymorphism (SNP) marker identification and SNP analysis more challenging than in diploid species, as for a given locus usually two versions of a DNA sequence (based on the two ancestral genomes) have to be analyzed simultaneously during SNP identification and analysis. One hundred amplicons derived from expressed sequence tag (ESTs) were analyzed to identify SNPs in a panel of oilseed rape varieties and within two sister species representing the ancestral genomes. A total of 604 SNPs were identified, averaging one SNP in every 42 bp. It was possible to clearly discriminate SNPs that are polymorphic between different plant varieties from SNPs differentiating the two ancestral genomes. To validate the identified SNPs for their use in genetic analysis, we have developed Illumina GoldenGate assays for some of the identified SNPs. Through the analysis of a number of oilseed rape varieties and mapping populations with GoldenGate assays, we were able to identify a number of different segregation patterns in allotetraploid oilseed rape. The majority of the identified SNP markers can be readily used for genetic mapping, showing that amplicon sequencing and Illumina GoldenGate assays can be used to reliably identify SNP markers in tetraploid oilseed rape and to convert them into successful SNP assays that can be used for genetic analysis.

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