scholarly journals Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes

2021 ◽  
Vol 220 (2) ◽  
Author(s):  
Gil Kanfer ◽  
Shireen A. Sarraf ◽  
Yaakov Maman ◽  
Heather Baldwin ◽  
Eunice Dominguez-Martin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fluorescent Protein ◽  
Network Models ◽  
Neural Network Models ◽  
Guide Rna ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Screening Platform ◽  
Cellular Phenotypes

Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

scholarly journals Image-based pooled whole genome CRISPR screening for Parkin and TFEB subcellular localization

2020 ◽  
Author(s):  
Gil Kanfer ◽  
Shireen A. Sarraf ◽  
Yaakov Maman ◽  
Heather Baldwin ◽  
Kory R. Johnson ◽  
...  
Keyword(s):  
Neural Network ◽  
Subcellular Localization ◽  
Fluorescent Protein ◽  
Network Models ◽  
Neural Network Models ◽  
Guide Rna ◽  
Genome Wide ◽  
A Genome ◽  
Screening Platform ◽  
Cellular Phenotypes

AbstractGenome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9 (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated, isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty of the sixty-four hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-Photoswitchable Screening (AI-PS) offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

scholarly journals Genome-wide Cas9 binding specificity in Saccharomyces cerevisiae

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9442 ◽  
Author(s):  
Zachary J. Waldrip ◽  
Piroon Jenjaroenpun ◽  
Oktawia DeYoung ◽  
Intawat Nookaew ◽  
Sean D. Taverna ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
High Specificity ◽  
Genomic Locus ◽  
Site Specific ◽  
Guide Rna ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Chromosome Iii ◽  
Genomic Editing

The CRISPR system has become heavily utilized in biomedical research as a tool for genomic editing as well as for site-specific chromosomal localization of specific proteins. For example, we developed a CRISPR-based methodology for enriching a specific genomic locus of interest for proteomic analysis in Saccharomyces cerevisiae, which utilized a guide RNA-targeted, catalytically dead Cas9 (dCas9) as an affinity reagent. To more comprehensively evaluate the genomic specificity of using dCas9 as a site-specific tool for chromosomal studies, we performed dCas9-mediated locus enrichment followed by next-generation sequencing on a genome-wide scale. As a test locus, we used the ARS305 origin of replication on chromosome III in S. cerevisiae. We found that enrichment of this site is highly specific, with virtually no off-target enrichment of unique genomic sequences. The high specificity of genomic localization and enrichment suggests that dCas9-mediated technologies have promising potential for site-specific chromosomal studies in organisms with relatively small genomes such as yeasts.

scholarly journals Integrated cross-study datasets of genetic dependencies in cancer

2020 ◽  
Author(s):  
Clare Pacini ◽  
Joshua M. Dempster ◽  
Isabella Boyle ◽  
Emanuel Gonçalves ◽  
Hanna Najgebauer ◽  
...  
Keyword(s):  
Statistical Power ◽  
Batch Effects ◽  
Copy Number Alterations ◽  
Cancer Genetic ◽  
Guide Rna ◽  
Large Panels ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
The Individual

AbstractCRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

scholarly journals Integrated cross-study datasets of genetic dependencies in cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Clare Pacini ◽  
Joshua M. Dempster ◽  
Isabella Boyle ◽  
Emanuel Gonçalves ◽  
Hanna Najgebauer ◽  
...  
Keyword(s):  
Statistical Power ◽  
Batch Effects ◽  
Copy Number Alterations ◽  
Cancer Genetic ◽  
Guide Rna ◽  
Large Panels ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
The Individual

AbstractCRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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