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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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Updated: Mar 22, 2026

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Using Synthetic Mouse Spike-In Transcripts to Evaluate RNA-Seq Analysis Tools.

Dena Leshkowitz¹, Ester Feldmesser¹, Gilgi Friedlander²

¹Biological Services Department, Weizmann Institute of Science, Rehovot, 76100, Israel.

|April 22, 2016

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) RNA-Seq analysis for gene expression is adequate, but transcript-isoform level analysis using bioinformatics tools shows significant accuracy and precision limitations. Further development is needed for reliable isoform-level RNA-Seq data interpretation.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Next-generation sequencing (NGS) RNA-Seq is crucial for genome-wide transcriptome analysis.
While gene-level RNA-Seq tools are well-benchmarked, transcript-isoform level analysis remains underexplored.
Alternative splicing significantly increases proteomic diversity in eukaryotes.

Purpose of the Study:

To evaluate and compare bioinformatics tools for assembling, quantifying, and detecting differential expression of transcripts using RNA-Seq data.
To assess the accuracy and precision of these tools specifically at the transcript-isoform level.
To establish a controlled experimental approach for benchmarking RNA-Seq analysis tools.

Main Methods:

Utilized in vitro synthesized mouse spike-in control transcripts added to differentiating mouse embryonic bodies' total RNA.
Measured expression patterns of spike-in transcripts to create a controlled dataset.
Compared observed RNA-Seq analysis results against expected outcomes from controlled spike-in transcripts.

Main Results:

Gene-level differential expression detection was found to be adequate across tested tools.
All evaluated bioinformatics tools demonstrated a lack of accuracy and precision for transcript-isoform level analysis.
The controlled spike-in approach provided a reliable method for assessing tool performance.

Conclusions:

Current bioinformatics tools are insufficient for accurate and precise transcript-isoform level quantification and differential expression analysis via RNA-Seq.
The study highlights a critical gap in the analysis of alternative splicing events using RNA-Seq data.
Further research and development of specialized tools are necessary to improve the reliability of isoform-level transcriptome analysis.