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RNA-seq

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...

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing.

Gennady Gorin¹, Shawn Yoshida^1,2, Lior Pachter^3,4

¹Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Bulletin of Mathematical Biology

|October 12, 2023

Summary

This study models RNA dynamics with delays, finding that nuclear export kinetics are consistent with standard waiting times. Our findings simplify models of gene expression and RNA processing.

Keywords:

Chemical master equations Delay processes Markov chain Monte Carlo Model identification Single-cell RNA sequencing Stochastic simulation

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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Area of Science:

Molecular Biology
Systems Biology
Computational Biology

Background:

The RNA life-cycle involves sequential reactions, necessitating models that account for temporal delays in transcriptional dynamics.
Existing models often simplify reaction kinetics, potentially overlooking the impact of delays on RNA production and processing.

Purpose of the Study:

To develop and analyze computational models of transcriptional dynamics incorporating general sets of delayed monomolecular reactions.
To investigate the influence of delays on RNA copy number distributions in systems with splicing, degradation, and catalysis.
To analyze single-cell and single-nucleus RNA sequencing data to infer the nature of waiting times in nuclear export.

Main Methods:

Development of numerical strategies for calculating RNA copy number distributions from delayed reaction models.
Application of these models to simulate systems involving RNA splicing, degradation, and catalytic reactions.
Analysis of experimental single-cell and single-nucleus RNA sequencing data using the developed computational framework.

Main Results:

Successfully calculated RNA copy number distributions for various delayed transcriptional models.
Demonstrated the utility of the models in analyzing complex RNA processing pathways.
Analysis of RNA sequencing data indicated that nuclear export kinetics can be described by Markovian waiting times, without needing non-Markovian models.

Conclusions:

Models incorporating delays are crucial for accurately representing RNA life-cycle dynamics.
The developed numerical strategies provide a robust method for analyzing such models.
Experimental data suggest that nuclear export kinetics are simpler than previously assumed, adhering to standard waiting time distributions.