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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 22, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
07:29

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Identifying differentially expressed transcripts from RNA-seq data with biological variation.

Peter Glaus1, Antti Honkela, Magnus Rattray

  • 1School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. glaus@cs.man.ac.uk

Bioinformatics (Oxford, England)
|May 8, 2012
PubMed
Summary
This summary is machine-generated.

BitSeq offers a Bayesian approach for estimating transcript expression levels from RNA-sequencing (RNA-seq) data. This method accurately analyzes differential gene expression by accounting for technical and biological variability.

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Related Experiment Videos

Last Updated: May 22, 2026

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing allows for detailed transcriptomic expression analysis.
  • Accurate estimation of transcript expression and differential expression (DE) requires probabilistic methods to address ambiguities from shared exons, finite read sampling, and biological variance.

Purpose of the Study:

  • To introduce BitSeq, a Bayesian method for estimating transcript expression levels from RNA-sequencing (RNA-seq) data.
  • To develop a novel method for differential expression analysis across replicates that accounts for uncertainty and biological variance.

Main Methods:

  • BitSeq employs a Bayesian approach using Markov chain Monte Carlo samples from the posterior probability distribution of a generative model.
  • A novel DE analysis method is proposed that propagates sample-level uncertainty and models biological variance with an expression-level-dependent prior.

Main Results:

  • The study presents BitSeq for transcript expression level estimation from RNA-seq data.
  • The proposed DE analysis method demonstrates advantages on both simulated and real RNA-seq datasets with replication.

Conclusions:

  • BitSeq provides a robust Bayesian framework for transcript expression and differential expression analysis in RNA-seq.
  • The software implementation in C++ and Python is publicly available, facilitating its application in transcriptomic research.