scholarly journals HypercubeME: two hundred million combinatorially complete datasets from a single experiment

2019 ◽  
Author(s):  
Laura Avino Esteban ◽  
Lyubov R. Lonishin ◽  
Daniil Bobrovskiy ◽  
Gregory Leleytner ◽  
Natalya S. Bogatyreva ◽  
...  
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Recursive Algorithm ◽  
Random Mutagenesis ◽  
Higher Order ◽  
Single Experiment ◽  
Manual Curation ◽  
Complete Dataset ◽  
Genotype Space ◽  
High Throughput Manner

AbstractMotivationEpistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a “combinatorially complete dataset”. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets.ResultsWe present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data.Availabilityhttps://github.com/ivankovlab/HypercubeME.git.

scholarly journals HypercubeME: two hundred million combinatorially complete datasets from a single experiment

2019 ◽  
Author(s):  
Laura Avino Esteban ◽  
Lyubov R Lonishin ◽  
Daniil Bobrovskiy ◽  
Gregory Leleytner ◽  
Natalya S Bogatyreva ◽  
...  
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Recursive Algorithm ◽  
Random Mutagenesis ◽  
Higher Order ◽  
Supplementary Information ◽  
Single Experiment ◽  
Manual Curation ◽  
Complete Dataset ◽  
Genotype Space

Abstract Motivation Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a “combinatorially complete dataset”. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. Results We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. Availability https://github.com/ivankovlab/HypercubeME.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals High Throughput pMHC-I Tetramer Library Production Using Chaperone Mediated Peptide Exchange

2019 ◽  
Author(s):  
Sarah A. Overall ◽  
Jugmohit S. Toor ◽  
Stephanie Hao ◽  
Mark Yarmarkovich ◽  
Son Nguyen ◽  
...  
Keyword(s):  
T Cell ◽  
High Throughput ◽  
Molecular Chaperone ◽  
Robust Method ◽  
Single Experiment ◽  
Mhc I ◽  
Combined Analysis ◽  
Transcription Profiles ◽  
Peptide Exchange ◽  
High Throughput Manner

ABSTRACTPeptide exchange technologies are essential for the generation of pMHC-multimer libraries, used to probe highly diverse, polyclonal TCR repertoires. Using the molecular chaperone TAPBPR, we present a robust method for the capture of stable, empty MHC-I molecules which can be readily tetramerized and loaded with peptides of choice in a high-throughput manner. Combined with tetramer barcoding using multi-modal cellular indexing technology (ECCITE-seq), our approach allows a combined analysis of TCR repertoires and other T-cell transcription profiles together with their cognate pMHC-I specificities in a single experiment.

scholarly journals Multiplex generation, tracking, and functional screening of substitution mutants using a CRISPR/retron system

2020 ◽  
Author(s):  
Hyeonseob Lim ◽  
Soyeong Jun ◽  
Minjeong Park ◽  
Junghak Lim ◽  
Jaehwan Jeong ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
High Throughput ◽  
Functional Screening ◽  
Single Experiment ◽  
Substitution Mutations ◽  
Genome Scale ◽  
High Throughput Manner ◽  
Substitution Mutation

ABSTRACTWe developed a clustered regularly interspaced short palindromic repeats (CRISPR)/retron system for multiplexed generation of substitution mutations by co-utilization of a retron system that continuously expresses donor DNA and a CRISPR/Cas9 cassette that induces cleavage at target genomic loci. Our system efficiently introduces substitution mutation in the Escherichia coli genome in a high-throughput manner. These substitution mutations can be tracked by analysis of retron plasmid sequences without laborious amplification of individual edited loci. We demonstrated that our CRISPR/retron system can introduce thousands of mutations in a single experiment and be used for screening phenotypes related to chemical responses or fitness changes. We expect that our system could facilitate genome-scale substitution screenings.

Kinetic Solvent Effects in Organic Reactions

2017 ◽  
Author(s):  
Belinda Slakman ◽  
Richard West
Keyword(s):  
Solvent Effect ◽  
Reaction Kinetics ◽  
Computational Methods ◽  
High Throughput ◽  
Kinetic Modeling ◽  
Solvent Effects ◽  
Reaction Rates ◽  
Prior Work ◽  
Solvent Phase ◽  
High Throughput Manner

<div> <div> <div> <p>This article reviews prior work studying reaction kinetics in solution, with the goal of using this information to improve detailed kinetic modeling in the solvent phase. Both experimental and computational methods for calculating reaction rates in liquids are reviewed. Previous studies, which used such methods to determine solvent effects, are then analyzed based on reaction family. Many of these studies correlate kinetic solvent effect with one or more solvent parameters or properties of reacting species, but it is not always possible, and investigations are usually done on too few reactions and solvents to truly generalize. From these studies, we present suggestions on how best to use data to generalize solvent effects for many different reaction types in a high throughput manner. </p> </div> </div> </div>

Directed Evolution of P450 Fatty Acid Decarboxylases via High-Throughput Screening Towards Improved Catalytic Activity

2019 ◽  
Author(s):  
Huifang Xu ◽  
Weinan Liang ◽  
Linlin Ning ◽  
Yuanyuan Jiang ◽  
Wenxia Yang ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Fatty Acid ◽  
Directed Evolution ◽  
High Throughput ◽  
High Throughput Screening ◽  
Colorimetric Detection ◽  
Screening Method ◽  
Random Mutagenesis ◽  
Direct Production ◽  
Consumption Activity

P450 fatty acid decarboxylases (FADCs) have recently been attracting considerable attention owing to their one-step direct production of industrially important 1-alkenes from biologically abundant feedstock free fatty acids under mild conditions. However, attempts to improve the catalytic activity of FADCs have met with little success. Protein engineering has been limited to selected residues and small mutant libraries due to lack of an effective high-throughput screening (HTS) method. Here, we devise a catalase-deficient <i>Escherichia coli</i> host strain and report an HTS approach based on colorimetric detection of H<sub>2</sub>O<sub>2</sub>-consumption activity of FADCs. Directed evolution enabled by this method has led to effective identification for the first time of improved FADC variants for medium-chain 1-alkene production from both DNA shuffling and random mutagenesis libraries. Advantageously, this screening method can be extended to other enzymes that stoichiometrically utilize H<sub>2</sub>O<sub>2</sub> as co-substrate.

A centrifuge-based stepwise chemical loading disc for the production of multiplex anisotropic metallic nanoparticles

RSC Advances ◽  
2015 ◽  
Vol 5 (3) ◽  
pp. 1846-1851 ◽  
Author(s):  
Byung Hyun Park ◽  
Ji Hyun Lee ◽  
Jae Hwan Jung ◽  
Seung Jun Oh ◽  
Doh C. Lee ◽  
...  
Keyword(s):  
High Throughput ◽  
Metallic Nanoparticles ◽  
Au Nps ◽  
High Throughput Manner ◽  
Chemical Loading

We have proposed a novel rotary microdevice in which multiplex anisotropic Au NPs could be synthesized under diverse conditions in a high-throughput manner.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

Biclustering of DNA Microarray Data

2013 ◽  
pp. 513-551 ◽  
Author(s):  
Alain B. Tchagang ◽  
Youlian Pan ◽  
Fazel Famili ◽  
Ahmed H. Tewfik ◽  
Panayiotis V. Benos
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Dna Microarray ◽  
High Throughput ◽  
Microarray Data ◽  
Evaluation Methods ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Daunting Task

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.

scholarly journals Hydrogen overproducing nitrogenases obtained by random mutagenesis and high-throughput screening

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Emma Barahona ◽  
Emilio Jiménez-Vicente ◽  
Luis M. Rubio
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Rhodobacter Capsulatus ◽  
Random Mutagenesis ◽  
Fluorescence Emission ◽  
Inducible Promoter ◽  
Hydrogen Gas ◽  
Photosynthetic Organisms ◽  
Fe Protein ◽  
Protein Variants

Abstract When produced biologically, especially by photosynthetic organisms, hydrogen gas (H2) is arguably the cleanest fuel available. An important limitation to the discovery or synthesis of better H2-producing enzymes is the absence of methods for the high-throughput screening of H2 production in biological systems. Here, we re-engineered the natural H2 sensing system of Rhodobacter capsulatus to direct the emission of LacZ-dependent fluorescence in response to nitrogenase-produced H2. A lacZ gene was placed under the control of the hupA H2-inducible promoter in a strain lacking the uptake hydrogenase and the nifH nitrogenase gene. This system was then used in combination with fluorescence-activated cell sorting flow cytometry to screen large libraries of nitrogenase Fe protein variants generated by random mutagenesis. Exact correlation between fluorescence emission and H2 production levels was found for all automatically selected strains. One of the selected H2-overproducing Fe protein variants lacked 40% of the wild-type amino acid sequence, a surprising finding for a protein that is highly conserved in nature. We propose that this method has great potential to improve microbial H2 production by allowing powerful approaches such as the directed evolution of nitrogenases and hydrogenases.

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