scholarly journals SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

2019 ◽  
Vol 5 (11) ◽  
pp. eaax9249 ◽  
Author(s):  
Hui Kwon Kim ◽  
Younggwang Kim ◽  
Sungtae Lee ◽  
Seonwoo Min ◽  
Jung Yoon Bae ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Human Cell ◽  
Target Sequence ◽  
Activity Prediction ◽  
Large Dataset ◽  
Predicting Model ◽  
Generalization Performance ◽  
Guide Rna ◽  
Cell Library

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.

scholarly journals SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with unparalleled generalization performance

2019 ◽  
Author(s):  
Hui Kwon Kim ◽  
Younggwang Kim ◽  
Sungtae Lee ◽  
Seonwoo Min ◽  
Jung Yoon Bae ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Human Cell ◽  
Large Data ◽  
Data Sets ◽  
Target Sequence ◽  
Activity Prediction ◽  
Data Set ◽  
Generalization Performance ◽  
Cell Library

AbstractWe evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing sgRNA-encoding and target sequence pairs. Deep learning-based training on this large data set of SpCas9-induced indel frequencies led to the development of a SpCas9-activity predicting model named DeepSpCas9. When tested against independently generated data sets (our own and those published by other groups), DeepSpCas9 showed unprecedentedly high generalization performance. DeepSpCas9 is available athttp://deepcrispr.info/DeepCas9.

scholarly journals On-target activity predictions enable improved CRISPR–dCas9 screens in bacteria

2020 ◽  
Vol 48 (11) ◽  
pp. e64-e64 ◽  
Author(s):  
Alicia Calvo-Villamañán ◽  
Jérome Wong Ng ◽  
Rémi Planel ◽  
Hervé Ménager ◽  
Arthur Chen ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Cost Effective ◽  
Target Sequence ◽  
Guide Rna ◽  
E Coli ◽  
Genome Wide ◽  
High Throughput Screens ◽  
Target Activity ◽  
Target Effects

Abstract The ability to block gene expression in bacteria with the catalytically inactive mutant of Cas9, known as dCas9, is quickly becoming a standard methodology to probe gene function, perform high-throughput screens, and engineer cells for desired purposes. Yet, we still lack a good understanding of the design rules that determine on-target activity for dCas9. Taking advantage of high-throughput screening data, we fit a model to predict the ability of dCas9 to block the RNA polymerase based on the target sequence, and validate its performance on independently generated datasets. We further design a novel genome wide guide RNA library for E. coli MG1655, EcoWG1, using our model to choose guides with high activity while avoiding guides which might be toxic or have off-target effects. A screen performed using the EcoWG1 library during growth in rich medium improved upon previously published screens, demonstrating that very good performances can be attained using only a small number of well designed guides. Being able to design effective, smaller libraries will help make CRISPRi screens even easier to perform and more cost-effective. Our model and materials are available to the community through crispr.pasteur.fr and Addgene.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

ASSESSMENT OF EFFICIENCY AND OFF-TARGET ACTIVITY OF CRISPR/CAS RIBONUCLEOPROTEIN COMPLEXES

2020 ◽  
Author(s):  
Y.V. Mikhaylova ◽  
◽  
M.A. Tyumentseva ◽  
A.A. Shelenkov ◽  
Y.G. Yanushevich ◽  
...  
Keyword(s):  
High Sensitivity ◽  
Correct Choice ◽  
Target Sequence ◽  
Dna Breaks ◽  
Gene Encoding ◽  
Guide Rna ◽  
Guide Rnas ◽  
Ribonucleoprotein Complexes ◽  
Chemokine Receptor Ccr5 ◽  
Target Activity

In this study, we assessed the efficiency and off-target activity of the CRISPR/CAS complex with one of the selected guide RNAs using the CIRCLE-seq technology. The gene encoding the human chemokine receptor CCR5 was used as a target sequence for genome editing. The results of this experiment indicate the correct choice of the guide RNA and efficient work of the CRISPR- CAS ribonucleoprotein complex used. CIRCLE-seq technology has shown high sensitivity compared to bioinformatic methods for predicting off-target activity of CRISPR/CAS complexes. We plan to evaluate the efficiency and off-target activity of CRISPR/CAS ribonucleoprotein complexes with other guide RNAs by slightly adjusting the CIRCLE-seq-technology protocol in order to reduce nonspecific DNA breaks and increase the number of reliable reads.

A deep learning-based concept for high throughput image flow cytometry

2021 ◽  
Vol 118 (12) ◽  
pp. 123701
Author(s):  
Julie Martin-Wortham ◽  
Steffen M. Recktenwald ◽  
Marcelle G. M. Lopes ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
High Throughput ◽  
Image Flow

Development of a mito-CRISPR system for generating mitochondrial DNA-deleted strain in Saccharomyces cerevisiae

2020 ◽  
Vol 85 (4) ◽  
pp. 895-901
Author(s):  
Takamitsu Amai ◽  
Tomoka Tsuji ◽  
Mitsuyoshi Ueda ◽  
Kouichi Kuroda
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Mitochondrial Dna ◽  
Ethidium Bromide ◽  
Nuclear Genome ◽  
Target Sequence ◽  
Guide Rna ◽  
Crispr System ◽  
Mitochondrial Target ◽  
Gene Treatment ◽  
Mtdna Deletion

ABSTRACT Mitochondrial dysfunction can occur in a variety of ways, most often due to the deletion or mutation of mitochondrial DNA (mtDNA). The easy generation of yeasts with mtDNA deletion is attractive for analyzing the functions of the mtDNA gene. Treatment of yeasts with ethidium bromide is a well-known method for generating ρ° cells with complete deletion of mtDNA from Saccharomyces cerevisiae. However, the mutagenic effects of ethidium bromide on the nuclear genome cannot be excluded. In this study, we developed a “mito-CRISPR system” that specifically generates ρ° cells of yeasts. This system enabled the specific cleavage of mtDNA by introducing Cas9 fused with the mitochondrial target sequence at the N-terminus and guide RNA into mitochondria, resulting in the specific generation of ρ° cells in yeasts. The mito-CRISPR system provides a concise technology for deleting mtDNA in yeasts.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid

2021 ◽  
pp. 104490
Author(s):  
Xuesheng Bian ◽  
Gang Li ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Model ◽  
Detection And Tracking ◽  
Deep Learning Model

scholarly journals Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 575
Author(s):  
Jelena Ochs ◽  
Ferdinand Biermann ◽  
Tobias Piotrowski ◽  
Frederik Erkens ◽  
Bastian Nießing ◽  
...  
Keyword(s):  
Stem Cells ◽  
Deep Learning ◽  
High Throughput ◽  
High Speed ◽  
New Technologies ◽  
Human Mesenchymal Stem Cells ◽  
Simulation Software ◽  
System Capacity ◽  
Small Scale ◽  
Production Environment

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

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