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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Related Experiment Video

Updated: Jun 6, 2026

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
11:42

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

Evaluating gene expression dynamics using pairwise RNA FISH data.

Matthieu Wyart1, David Botstein, Ned S Wingreen

  • 1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.

Plos Computational Biology
|November 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using RNA Fluorescent In Situ Hybridization (FISH) to reconstruct gene expression dynamics from single-cell snapshots. The approach accurately models gene expression cycles and switches, offering insights into cellular processes.

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Related Experiment Videos

Last Updated: Jun 6, 2026

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
11:42

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Published on: October 27, 2017

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

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Published on: May 28, 2021

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Molecular Biology
  • Systems Biology
  • Biophysics

Background:

  • Single-cell gene expression analysis using RNA Fluorescent In Situ Hybridization (FISH) provides high-resolution snapshots but requires cell fixation.
  • Reconstructing dynamic biological processes from static snapshots is a significant challenge in systems biology.

Purpose of the Study:

  • To develop and validate a computational method for reconstructing real-time gene expression dynamics from static RNA FISH data.
  • To demonstrate the applicability of this method to identify cyclical gene expression patterns and estimate gene expression noise.

Main Methods:

  • Utilized maximum-likelihood parameter estimation on synthetically generated, noisy RNA FISH data for pairs of genes.
  • Developed a binary thresholding approach for reconstructing dynamics under the assumption of short-lived mRNA bursts.
  • Applied the thresholding method to experimental RNA FISH data from unsynchronized Saccharomyces cerevisiae cells.

Main Results:

  • Accurate reconstruction of dynamical gene expression programs, including cycles and switches, from simulated FISH data.
  • Demonstrated that binary thresholding is a robust method for dynamics reconstruction in the bursty mRNA production regime.
  • Identified evidence for metabolic cycles in Saccharomyces cerevisiae and provided an estimate of global gene-expression noise.

Conclusions:

  • RNA FISH data from pairs of genes can be leveraged to reconstruct dynamic gene expression processes.
  • The developed computational framework offers a powerful tool for inferring cellular dynamics from static snapshot data.
  • This approach has broad applicability to various molecular measurements, including protein concentrations from immunofluorescence assays.