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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...

Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

Ribosome Profiling

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...

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Related Experiment Video

Updated: Nov 9, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

A benchmark for RNA-seq deconvolution analysis under dynamic testing environments.

Haijing Jin¹, Zhandong Liu^2,3

¹Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA.

|April 13, 2021

Summary

This summary is machine-generated.

This study benchmarks deconvolution methods for gene expression data, revealing challenges in selecting optimal tools. Findings offer insights for improving cell type deconvolution analysis and RNA-seq data interpretation.

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Last Updated: Nov 9, 2025

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Published on: March 7, 2018

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Deconvolution analysis is crucial for understanding cell type composition in gene expression data.
Existing deconvolution methods lack clear guidelines for optimal selection due to unknown parameter effects.
Researchers face challenges in applying deconvolution tools to specific biological contexts.

Purpose of the Study:

To systematically evaluate the performance and limitations of various deconvolution methods.
To identify the impact of technical and biological factors on deconvolution accuracy.
To provide guidance for selecting appropriate deconvolution tools for RNA-seq data.

Main Methods:

Developed three benchmarking frameworks to assess deconvolution methods.
Compared 11 popular deconvolution algorithms across 1766 diverse conditions.
Investigated factors including simulation model, quantification unit, and parameter settings.

Main Results:

Identified critical factors influencing deconvolution accuracy and reliability.
Highlighted specific pitfalls associated with different deconvolution approaches.
Quantified the performance variations among 11 deconvolution methods.

Conclusions:

Offers crucial insights for researchers applying deconvolution techniques.
Provides a basis for standardization and development of more robust deconvolution tools.
Aims to enhance the utility of deconvolution analysis in RNA-seq studies.