scholarly journals A generic strategy for CRISPR-Cas9-mediated gene tagging

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Daniel H. Lackner ◽  
Alexia Carré ◽  
Paloma M. Guzzardo ◽  
Carina Banning ◽  
Ramu Mangena ◽  
...  
Keyword(s):  
Gene Tagging ◽  
Generic Strategy

scholarly journals Ultrasensitive nano-optomechanical force sensor operated at dilution temperatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Fogliano ◽  
Benjamin Besga ◽  
Antoine Reigue ◽  
Laure Mercier de Lépinay ◽  
Philip Heringlake ◽  
...  
Keyword(s):  
Thermal Conduction ◽  
Photon Counting ◽  
Force Sensor ◽  
Lateral Force ◽  
Oscillation Period ◽  
The Road ◽  
Field Gradients ◽  
Concomitant Reduction ◽  
Generic Strategy ◽  
Conduction Properties

AbstractCooling down nanomechanical force probes is a generic strategy to enhance their sensitivities through the concomitant reduction of their thermal noise and mechanical damping rates. However, heat conduction becomes less efficient at low temperatures, which renders difficult to ensure and verify their proper thermalization. Here we implement optomechanical readout techniques operating in the photon counting regime to probe the dynamics of suspended silicon carbide nanowires in a dilution refrigerator. Readout of their vibrations is realized with sub-picowatt optical powers, in a situation where less than one photon is collected per oscillation period. We demonstrate their thermalization down to 32 ± 2 mK, reaching very large sensitivities for scanning probe force sensors, 40 zN Hz−1/2, with a sensitivity to lateral force field gradients in the fN m−1 range. This opens the road toward explorations of the mechanical and thermal conduction properties of nanoresonators at minimal excitation level, and to nanomechanical vectorial imaging of faint forces at dilution temperatures.

scholarly journals Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
The Tien Mai ◽  
Paul Turner ◽  
Jukka Corander
Keyword(s):  
Random Effect ◽  
Simulated Data ◽  
Fixed Effect Model ◽  
Accurate Estimate ◽  
Limited Attention ◽  
Effect Model ◽  
Generic Strategy ◽  
Multiple Sample ◽  
Inference Strategy ◽  
Resistance Data

Abstract Background Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. Results In this paper, we propose a generic strategy for heritability inference, termed as “boosting heritability”, by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model. Boosting heritability uses in particular a multiple sample splitting strategy which leads in general to a stable and accurate estimate. We use both simulated data and real antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the attractive features of our inference strategy. Conclusions Boosting is shown to offer a reliable and practically useful tool for inference about heritability.

scholarly journals Seamless Gene Tagging by Endonuclease-Driven Homologous Recombination

PLoS ONE ◽  
2011 ◽  
Vol 6 (8) ◽  
pp. e23794 ◽  
Author(s):  
Anton Khmelinskii ◽  
Matthias Meurer ◽  
Nurlanbek Duishoev ◽  
Nicolas Delhomme ◽  
Michael Knop
Keyword(s):  
Homologous Recombination ◽  
Gene Tagging

The Use of Mutator for Gene-Tagging: Cross-Referencing between Transposable Element Systems

1988 ◽  
pp. 137-147 ◽  
Author(s):  
N. S. Shepherd ◽  
W. F. Sheridan ◽  
M. G. Mattes ◽  
G. Deno
Keyword(s):  
Transposable Element ◽  
Gene Tagging

scholarly journals A toolkit enabling efficient, scalable and reproducible gene tagging in trypanosomatids

Open Biology ◽  
2015 ◽  
Vol 5 (1) ◽  
pp. 140197 ◽  
Author(s):  
Samuel Dean ◽  
Jack Sunter ◽  
Richard J. Wheeler ◽  
Ian Hodkinson ◽  
Eva Gluenz ◽  
...  
Keyword(s):  
Trypanosoma Brucei ◽  
Large Datasets ◽  
Gene Tagging ◽  
Deletion Mutants ◽  
Whole Genome ◽  
Leishmania Mexicana ◽  
Protein Coding ◽  
Achievable Goal ◽  
Endogenous Loci ◽  
Pcr Conditions

One of the first steps in understanding a protein's function is to determine its localization; however, the methods for localizing proteins in some systems have not kept pace with the developments in other fields, creating a bottleneck in the analysis of the large datasets that are generated in the post-genomic era. To address this, we developed tools for tagging proteins in trypanosomatids. We made a plasmid that, when coupled with long primer PCR, can be used to produce transgenes at their endogenous loci encoding proteins tagged at either terminus or within the protein coding sequence. This system can also be used to generate deletion mutants to investigate the function of different protein domains. We show that the length of homology required for successful integration precluded long primer PCR tagging in Leishmania mexicana . Hence, we developed plasmids and a fusion PCR approach to create gene tagging amplicons with sufficiently long homologous regions for targeted integration, suitable for use in trypanosomatids with less efficient homologous recombination than Trypanosoma brucei . Importantly, we have automated the primer design, developed universal PCR conditions and optimized the workflow to make this system reliable, efficient and scalable such that whole genome tagging is now an achievable goal.

scholarly journals Mind the gap: preventing circularity in missense variant prediction

2020 ◽  
Author(s):  
Stephan Heijl ◽  
Bas Vroling ◽  
Tom van den Bergh ◽  
Henk-Jan Joosten
Keyword(s):  
Real World ◽  
Performance Metrics ◽  
Model Performance ◽  
Predictive Performance ◽  
Missense Variant ◽  
Training Methods ◽  
The Real ◽  
Evaluation Scores ◽  
Evaluation Strategies ◽  
Generic Strategy

AbstractDespite advances in the field of missense variant effect prediction, the real clinical utility of current computational approaches remains rather limited. There is a large difference in performance metrics reported by developers and those observed in the real world. Most currently available predictors suffer from one or more types of circularity in their training and evaluation strategies that lead to overestimation of predictive performance. We present a generic strategy that is independent of dataset properties and algorithms used, to deal with circularity in the training phase. This results in more robust predictors and evaluation scores that accurately reflect the real-world performance of predictive models. Additionally, we show that commonly used training methods can have an adverse impact on model performance and lead to gross overestimation of true predictive performance.

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