scholarly journals RGEN-seq For Highly Sensitive Amplification-free Screen Of Off-target Sites Of Gene Editors

2021 ◽  
Author(s):  
Alexander Kuzin ◽  
Brendan Redler ◽  
Jaya Onuska ◽  
Alexei Slesarev
Keyword(s):  
Target Detection ◽  
Dna Cleavage ◽  
Affinity Purification ◽  
Pcr Amplification ◽  
Detailed Comparison ◽  
Detection Methods ◽  
Guide Rnas ◽  
Coverage Bias ◽  
Biochemical Assays ◽  
Target Sites

Abstract 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, significant technical and methodological issues still remain.. Of note, existing methods rely on PCR amplification, tagging, and affinity purification which can introduce bias, contaminants, sample loss through handling, etc. 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. Through use of novel sequencing adapters, the RGEN-Seq method 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.

scholarly journals RGEN-seq for highly sensitive amplification-free screen of off-target sites of gene editors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander Kuzin ◽  
Brendan Redler ◽  
Jaya Onuska ◽  
Alexei Slesarev
Keyword(s):  
Target Detection ◽  
Dna Cleavage ◽  
Affinity Purification ◽  
Pcr Amplification ◽  
Detailed Comparison ◽  
Detection Methods ◽  
Guide Rnas ◽  
Coverage Bias ◽  
Biochemical Assays ◽  
Target Sites

AbstractSensitive 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, significant technical and methodological issues still remain. Of note, existing methods rely on PCR amplification, tagging, and affinity purification which can introduce bias, contaminants, sample loss through handling, etc. 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. Through use of novel sequencing adapters, the RGEN-Seq method 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.

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

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.

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

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.

scholarly journals Optimizing de novo genome assembly from PCR-amplified metagenomes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6902 ◽  
Author(s):  
Simon Roux ◽  
Gareth Trubl ◽  
Danielle Goudeau ◽  
Nandita Nath ◽  
Estelle Couradeau ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Pcr Amplification ◽  
Error Rates ◽  
De Novo Genome Assembly ◽  
Low Input ◽  
Assembly Algorithm ◽  
Coverage Bias ◽  
Size Number ◽  
Assembly Pipeline

Background Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes. Methods Here we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes. Results Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes. Conclusions PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

scholarly journals Optimizing de novo genome assembly from PCR-amplified metagenomes

2018 ◽  
Author(s):  
Simon Roux ◽  
Gareth Trubl ◽  
Danielle Goudeau ◽  
Nandita Nath ◽  
Estelle Couradeau ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo Assembly ◽  
De Novo ◽  
Pcr Amplification ◽  
Error Rates ◽  
De Novo Genome Assembly ◽  
Low Input ◽  
Assembly Algorithm ◽  
Coverage Bias ◽  
Assembly Pipeline

Background. Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes. Methods. Here we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes. Results. Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥ 10kb by 10 to 100-fold for low input metagenomes. Conclusions. PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Improving the Precision of Base Editing by Bubble Hairpin Single Guide RNA

mBio ◽  
2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Zhiwei Hu ◽  
Yannan Wang ◽  
Qian Liu ◽  
Yan Qiu ◽  
Zhiyu Zhong ◽  
...  
Keyword(s):  
Genome Editing ◽  
Dna Breaks ◽  
Single Nucleotide ◽  
Base Editing ◽  
Guide Rna ◽  
Guide Rnas ◽  
Double Stranded Dna ◽  
Target Sites ◽  
Guide Sequence ◽  
Sgrna Design

ABSTRACT Base editing is a powerful genome editing approach that enables single-nucleotide changes without double-stranded DNA breaks (DSBs). However, off-target effects as well as other undesired editings at on-target sites remain obstacles for its application. Here, we report that bubble hairpin single guide RNAs (BH-sgRNAs), which contain a hairpin structure with a bubble region on the 5′ end of the guide sequence, can be efficiently applied to both cytosine base editor (CBE) and adenine base editor (ABE) and significantly decrease off-target editing without sacrificing on-target editing efficiency. Meanwhile, such a design also improves the purity of C-to-T conversions induced by base editor 3 (BE3) at on-target sites. Our results present a distinctive and effective strategy to improve the specificity of base editing. IMPORTANCE Base editors are DSB-free genome editing tools and have been widely used in diverse living systems. However, it is reported that these tools can cause substantial off-target editings. To meet this challenge, we developed a new approach to improve the specificity of base editors by using hairpin sgRNAs with a bubble. Furthermore, our sgRNA design also dramatically reduced indels and unwanted base substitutions at on-target sites. We believe that the BH-sgRNA design is a significant improvement over existing sgRNAs of base editors, and our design promises to be adaptable to various base editors. We expect that it will make contributions to improving the safety of gene therapy.

scholarly journals CRISPRitz: rapid, high-throughput and variant-aware in silico off-target site identification for CRISPR genome editing

2019 ◽  
Vol 36 (7) ◽  
pp. 2001-2008 ◽  
Author(s):  
Samuele Cancellieri ◽  
Matthew C Canver ◽  
Nicola Bombieri ◽  
Rosalba Giugno ◽  
Luca Pinello
Keyword(s):  
In Silico ◽  
Supplementary Information ◽  
Guide Rnas ◽  
Computational Performance ◽  
Search Mechanism ◽  
Genomic Annotation ◽  
Target Sites ◽  
Dna Elements ◽  
Efficient Data ◽  
A Site

ABSTRACT Motivation Clustered regularly interspaced short palindromic repeats (CRISPR) technologies allow for facile genomic modification in a site-specific manner. A key step in this process is the in silico design of single guide RNAs to efficiently and specifically target a site of interest. To this end, it is necessary to enumerate all potential off-target sites within a given genome that could be inadvertently altered by nuclease-mediated cleavage. Currently available software for this task is limited by computational efficiency, variant support or annotation, and assessment of the functional impact of potential off-target effects. Results To overcome these limitations, we have developed CRISPRitz, a suite of software tools to support the design and analysis of CRISPR/CRISPR-associated (Cas) experiments. Using efficient data structures combined with parallel computation, we offer a rapid, reliable, and exhaustive search mechanism to enumerate a comprehensive list of putative off-target sites. As proof-of-principle, we performed a head-to-head comparison with other available tools on several datasets. This analysis highlighted the unique features and superior computational performance of CRISPRitz including support for genomic searching with DNA/RNA bulges and mismatches of arbitrary size as specified by the user as well as consideration of genetic variants (variant-aware). In addition, graphical reports are offered for coding and non-coding regions that annotate the potential impact of putative off-target sites that lie within regions of functional genomic annotation (e.g. insulator and chromatin accessible sites from the ENCyclopedia Of DNA Elements [ENCODE] project). Availability and implementation The software is freely available at: https://github.com/pinellolab/CRISPRitzhttps://github.com/InfOmics/CRISPRitz. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Detection and Quantification of Influenza Virus Defective Viral Genomes from NGS Datasets Obtained after RT or RT-PCR Product Sequencing

Proceedings ◽  
2020 ◽  
Vol 50 (1) ◽  
pp. 84
Author(s):  
Jeremy Boussier ◽  
Sandie Munier ◽  
Bernadette Crescenzo-Chaigne ◽  
Sylvie Behillil ◽  
Vincent Enouf ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Molecular Mechanisms ◽  
Pcr Amplification ◽  
Influenza Viruses ◽  
Detection Methods ◽  
Rt Pcr ◽  
Bioinformatics Pipeline ◽  
Genomic Rnas ◽  
Viral Genomes ◽  
Detection And Quantification

Like most RNA viruses, influenza viruses (IAV) generate defective viral genomes (DVGs) during viral replication. Although there is accumulating evidence of a biological impact of DVGs, the molecular mechanisms leading to their production remain to be unveiled. Various next-generation sequencing (NGS) technologies and detection methods can be used to characterize DVGs. Here, we developed a bioinformatics pipeline called DG-seq to quickly identify and quantify DVGs in influenza viral stocks and compared two processing methods for NGS, with or without PCR amplification. To evaluate the performance of the DG-seq pipeline, we used either synthetic in-vitro-transcribed DVGs mixed with the full set of synthetic full-length genomic RNAs, or biological RNA samples extracted in duplicate from three IAV stocks: mutant viruses with a K635A or a R638A mutation in the PA subunit of the polymerase that impairs viral transcription, and their wild-type (WT) counterpart. Viral genomic RNAs were reverse-transcribed and either directly subjected to Illumina sequencing (RT-seq) or PCR-amplified prior to sequencing (RT-PCR-seq). Both methods displayed a good reproducibility between batches, with a lower detection rate but a more accurate quantification of DVGs in RT-seq samples. The PA mutants produced more DVGs than the WT virus, derived mostly from the polymerase gene segments, but also from the NA and HA segments, suggesting that an imbalance between transcription and replication can promote DVG production. Breakpoints occurred near the segment extremities, with no hotspot identified. Interestingly, we observed short direct A/T-rich repeats adjacent to the breakpoint ends at a significantly higher frequency than in the random case. This work provides the first comparison of DVG detection and quantification from NGS data obtained in the presence or absence of PCR amplification and gives novel insight into the mechanisms of influenza virus DVG production.

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