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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: May 10, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues.

Yi Li1, Xiaohui Xie

  • 1Department of Computer Science, University of California-Irvine, CA, USA.

BMC Bioinformatics
|June 6, 2013
PubMed
Summary
This summary is machine-generated.

Estimating gene expression from mixed tissue samples is challenging. This study introduces Transcript Estimation from Mixed Tissue samples (TEMT), a computational method that accurately deconvolutes RNA-seq data from heterogeneous tissues, improving transcript abundance estimation.

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Last Updated: May 10, 2026

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05:41

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • RNA-sequencing (RNA-seq) is crucial for transcriptome analysis and transcript abundance estimation.
  • Sample purity significantly impacts RNA-seq accuracy.
  • Analyzing heterogeneous tissues, composed of multiple cell types, presents a major challenge in accurately estimating individual cell type transcriptomes.

Purpose of the Study:

  • To develop a computational method for deconvoluting heterogeneous tissue samples in RNA-seq data.
  • To accurately estimate transcript abundances for individual cell types within mixed tissue samples.

Main Methods:

  • Proposed a probabilistic model-based approach named Transcript Estimation from Mixed Tissue samples (TEMT).
  • TEMT incorporates positional and sequence-specific biases.
  • Utilized an online Expectation-Maximization (EM) algorithm with efficient computational requirements.

Main Results:

  • TEMT significantly outperforms existing methods that do not account for tissue heterogeneity.
  • The method was validated on both simulation and ENCODE datasets.
  • Currently supports deconvolution of two cell types, with potential for extension to multiple cell types.

Conclusions:

  • The developed probabilistic model offers a novel approach for RNA-seq data analysis in heterogeneous tissues.
  • Explicitly addressing tissue heterogeneity substantially enhances the accuracy of transcript abundance estimation.
  • TEMT provides a valuable tool for analyzing complex biological samples.