Chemical-genetic Datasets
Chemical-genetic Datasets - Related Videos
12:13
Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae
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Cited by 4 •
2015
Here we present a cost-effective method for defining chemical-genetic interactions in budding yeast. The approach is built on fundamental techniques in yeast molecular biology and is well suited for the mechanistic interrogation of small to medium collections of chemicals and other media environments.
10:56
A User-friendly and Powerful R Analysis of Large-scale Datasets
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2025
This report describes a method involving an R script in the open-source software RStudio to analyze large-scale datasets obtained from time series experiments.
10:16
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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2025
This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.
09:03
Forward Genetic Approaches in Chlamydia trachomatis
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Cited by 21 •
2013
We describe a methodology to perform genetic analysis in Chlamydia based on chemical mutagenesis and whole genome sequencing. In addition, a system for DNA exchange within infected cells is described that can be used for genetic mapping. This method may be broadly applicable to microbial systems lacking transformation systems and molecular genetic tools.
08:50
Genetic Screens
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2023
Genetic screens are critical tools for defining gene function and understanding gene interactions. Screens typically involve mutating genes and then assessing the affected organisms for phenotypes of interest. The process can be “forward”, where mutations are generated randomly to identify unknown genes responsible for the phenotypes, or it can be “reverse”, where specific genes are targeted for mutation to observe what phenotypes are produced.Here, JoVE reviews various types of genetic...
FAQs
The key signal comes from comparing how different genetic backgrounds respond to the same chemical perturbation. Similar response patterns can point to shared gene functions or pathways, whereas selective sensitivity may identify genetic factors that influence a compound’s activity. These comparisons help researchers infer mechanisms rather than relying on a single treatment outcome.
Defined conditions make measurements comparable across compounds, genotypes, and experiments. Without consistent conditions, differences in growth, viability, or molecular changes may be difficult to attribute to the chemical perturbation or genetic factor being studied. Careful comparison under specified conditions therefore strengthens conclusions about sensitivity, resistance, and gene-function relationships.
Sensitivity is indicated when a genetic background shows a stronger adverse response to a compound, while resistance reflects a reduced response under comparison conditions. Examining these patterns across treatments and genotypes can reveal variants or pathways that modify chemical effects. This distinction supports the identification of biological factors associated with either vulnerability or protection.
Measurements such as growth, viability, and molecular changes provide complementary views of a perturbation’s effect. Growth or viability can capture broader biological consequences, while molecular measurements may reveal associated changes within the system. Linking these outcomes to chemical treatments and genetic profiles helps connect observable phenotypes with gene functions and pathways.
A typical workflow combines chemical exposure with genetic alteration or gene profiling, followed by measurement of defined biological outcomes. Researchers then organize the results by treatment and genotype and compare response patterns across conditions. This process produces a structured basis for identifying relationships among compounds, genetic factors, and phenotypes.
Genes can be functionally annotated by examining which chemical response patterns are associated with them or with related genetic backgrounds. Groups of genes showing coordinated responses may suggest shared pathway involvement. These analyses help organize gene-function relationships and connect measured phenotypes with broader biological processes in genetics.
Chemical-genetic datasets can highlight genes or pathways linked to compound sensitivity, resistance, or distinctive molecular responses, making them useful for therapeutic target discovery. Their structured treatment-response relationships also provide input for predictive models that estimate biological responses. In genetics, such models can help relate variation to expected outcomes under chemical perturbation.