scholarly journals DeepTetrad: high-throughput analysis of meiotic tetrads by deep learning in plants

2019 ◽  
Author(s):  
Eun-Cheon Lim ◽  
Jaeil Kim ◽  
Jihye Park ◽  
Eun-Jung Kim ◽  
Juhyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chromosome Segregation ◽  
High Throughput ◽  
Fluorescent Protein ◽  
Genetic Dissection ◽  
Crossover Frequency ◽  
Tetrad Analysis ◽  
High Throughput Analysis ◽  
Crossover Interference ◽  
Pollen Tetrad

AbstractMeiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be measured by observing the inheritance of linked transgenes expressing different colors of fluorescent protein in Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image recognition package for pollen tetrad analysis that enables high-throughput measurements of crossover frequency and interference in individual plants. DeepTetrad will accelerate genetic dissection of mechanisms that control meiotic recombination.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

scholarly journals High-Throughput Analysis of Protein with Tandem Fluorescent Protein Timers

2022 ◽  
pp. 85-100
Author(s):  
Jia Jun Fung ◽  
Karla Blöcher-Juárez ◽  
Anton Khmelinskii
Keyword(s):  
High Throughput ◽  
Protein Turnover ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Growth Conditions ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Instrument Choice ◽  
Fluorescence Measurements ◽  
Genome Wide

AbstractTandem fluorescent protein timers (tFTs) are versatile reporters of protein dynamics. A tFT consists of two fluorescent proteins with different maturation kinetics and provides a ratiometric readout of protein age, which can be exploited to follow intracellular trafficking, inheritance and turnover of tFT-tagged proteins. Here, we detail a protocol for high-throughput analysis of protein turnover with tFTs in yeast using fluorescence measurements of ordered colony arrays. We describe guidelines on optimization of experimental design with regard to the layout of colony arrays, growth conditions, and instrument choice. Combined with semi-automated genetic crossing using synthetic genetic array (SGA) methodology and high-throughput protein tagging with SWAp-Tag (SWAT) libraries, this approach can be used to compare protein turnover across the proteome and to identify regulators of protein turnover genome-wide.

scholarly journals Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography

ACS Photonics ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 294-301 ◽  
Author(s):  
Yichen Wu ◽  
Aniruddha Ray ◽  
Qingshan Wei ◽  
Alborz Feizi ◽  
Xin Tong ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Particle Aggregation ◽  
High Throughput Analysis ◽  
Throughput Analysis

scholarly journals Deep mutational scanning by FACS-sorting of encapsulated E. coli micro-colonies

2018 ◽  
Author(s):  
Lars Behrendt ◽  
Amelie Stein ◽  
Shiraz Ali Shah ◽  
Karsten Zengler ◽  
Søren J. Sørensen ◽  
...  
Keyword(s):  
Amino Acid ◽  
High Throughput ◽  
High Throughput Screening ◽  
Fluorescent Protein ◽  
Single Cells ◽  
Fluorescence Signal ◽  
Amino Acid Substitutions ◽  
Nucleotide Exchange ◽  
High Throughput Analysis ◽  
Wide Range

AbstractWe present a method for high-throughput screening of protein variants where the signal is enhanced by micro-encapsulation of single cells into 20-30 μm agarose beads. Cells inside beads are propagated using standard agitation in liquid media and grow clonally into micro-colonies harboring several hundred bacteria. We have, as a proof-of-concept, analyzed random amino acid substitutions in the five C-terminal β-strands of the Green Fluorescent Protein (GFP). Starting from libraries of variants, each bead represents a clonal line of cells that can be separated by Fluorescence Activated Cell Sorting (FACS). Pools representing collections of individual variants with desired properties are subsequently analyzed by deep sequencing. Notably, the encapsulation approach described holds the potential for high-throughput analysis of systems where the fluorescence signal from a single cell is insufficient for detection. Fusion to GFP, or use of fluorogenic substrates, allows coupling protein levels or activity to sequence for a wide range of proteins. Here we analyzed more than 10,000 individual variants to gauge the effect of mutations on GFP-fluorescence. In the mutated region, we observed virtually all amino acid substitutions that are accessible by single nucleotide exchange. Lastly, we assessed the performance of biophysical protein stability predictors, FoldX and Rosetta, in predicting the outcome of the experiment. Both tools display good performance on average, suggesting that loss of thermodynamic stability is a key mechanism for the observed variation of the mutants. This, in turn, suggests that deep mutational scanning datasets may be used to more efficiently fine-tune such predictors, especially for mutations poorly covered by current biophysical data.

High-throughput analysis of meiotic crossover frequency and interference via flow cytometry of fluorescent pollen in Arabidopsis thaliana

2013 ◽  
Vol 8 (11) ◽  
pp. 2119-2134 ◽  
Author(s):  
Nataliya E Yelina ◽  
Piotr A Ziolkowski ◽  
Nigel Miller ◽  
Xiaohui Zhao ◽  
Krystyna A Kelly ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Arabidopsis Thaliana ◽  
High Throughput ◽  
Crossover Frequency ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Meiotic Crossover

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.

Combination of Free Software, Light Microscopy and Non-specific Dyes for a High-throughput Analysis in Live/Dead Cell Count

2015 ◽  
Vol 11 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Luciano Cardoso ◽  
Suellen Cordeiro ◽  
Marcio Fronza ◽  
Denise Endringer ◽  
Tadeu de Andrade ◽  
...  
Keyword(s):  
Light Microscopy ◽  
Cell Count ◽  
High Throughput ◽  
Free Software ◽  
Dead Cell ◽  
High Throughput Analysis ◽  
Throughput Analysis
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