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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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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Published on: September 18, 2021

FDM: a graph-based statistical method to detect differential transcription using RNA-seq data.

Darshan Singh1, Christian F Orellana, Yin Hu

  • 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. darshan@email.unc.edu

Bioinformatics (Oxford, England)
|August 10, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces the Flow Difference Metric (FDM) for detecting differential transcription in RNA sequencing data. The FDM method demonstrates high accuracy and sensitivity, outperforming existing tools in identifying gene expression variations between samples.

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Area of Science:

  • Genomics and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • Alternative splicing in eukaryotes diversifies RNA transcripts and is crucial for tissue differentiation.
  • Dysregulation of alternative splicing is implicated in various diseases.
  • Accurate methods for detecting differential transcription between samples are essential for understanding these processes, particularly with short-read RNA sequencing (RNA-seq) data.

Purpose of the Study:

  • To develop and validate a novel method for detecting differential transcription between samples using RNA-seq data.
  • To characterize differential transcription as the difference in relative abundance of transcript isoforms.
  • To introduce a new statistical test for assessing the significance of differential transcription.

Main Methods:

  • Defined differential transcription as the difference in relative abundance of transcript isoforms.
  • Utilized the square root of Jensen Shannon Divergence (JSD*) to quantify differential transcription magnitude.
  • Developed a weighted splice-graph representation for RNA-seq data.
  • Introduced the Flow Difference Metric (FDM) to identify differential RNA transcript expression between splice graphs without prior gene models or transcript catalogs.
  • Developed a non-parametric statistical test for assessing differential transcription significance, extended for group-wise comparisons with replicates.

Main Results:

  • The FDM showed a high correlation (r=0.82) with JSD* using simulated RNA-seq data with sufficient coverage.
  • FDM identified 90% of genes with differential transcription (JSD* >0.28, coverage >7), surpassing Cufflinks (69%) and rDiff (49%) without annotations.
  • With annotations, Cufflinks identified 86% of differentially transcribed genes.
  • In experimental data from cancer cell lines (MCF7 and SUM102), FDM identified 1425 significantly differentially transcribed genes.
  • Quantitative real-time polymerase chain reaction (qRT-PCR) validated FDM's findings in experimental samples.

Conclusions:

  • The Flow Difference Metric (FDM) is a sensitive and accurate method for detecting differential transcription from RNA-seq data.
  • FDM outperforms existing methods, especially when gene models or transcript catalogs are unavailable.
  • The developed statistical test provides a robust way to assess the significance of differential transcription, applicable to both pairwise and group comparisons.