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Related Experiment Video

Updated: Apr 7, 2026

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

Leveraging transcript quantification for fast computation of alternative splicing profiles.

Gael P Alamancos¹, Amadís Pagès², Juan L Trincado¹

¹Universitat Pompeu Fabra, E08003 Barcelona, Spain.

RNA (New York, N.Y.)

|July 17, 2015

Summary

This summary is machine-generated.

SUPPA is a fast computational tool for analyzing alternative splicing events from RNA sequencing data. It offers comparable accuracy to existing methods while being over 1000 times faster, aiding large-scale splicing analysis.

Keywords:

RNA-seq splicing splicing event

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Alternative splicing is crucial for cellular processes and disease, including cancer.
High-throughput RNA sequencing enables genome-wide splicing analysis.
Large datasets pose computational and storage challenges for splicing analysis.

Purpose of the Study:

To introduce SUPPA, a computational tool for calculating relative inclusion values of alternative splicing events.
To assess SUPPA's accuracy, speed, and performance compared to existing methods.
To provide evidence on optimal annotation strategies for splicing analysis.

Main Methods:

SUPPA utilizes fast transcript quantification to determine alternative splicing inclusion values.
The tool was validated using simulated and real RNA-sequencing data against experimentally validated events.
Annotation strategies, including complete transcripts versus multiple transcripts per gene, were evaluated.

Main Results:

SUPPA demonstrates accuracy comparable or superior to standard methods.
Using complete transcripts improves estimation accuracy.
SUPPA is over 1000 times faster than conventional methods, with minimal accuracy loss when coupled with de novo transcript reconstruction.

Conclusions:

SUPPA significantly accelerates the systematic analysis of alternative splicing events in large datasets.
The tool facilitates splicing analysis with limited computational resources without compromising accuracy.
SUPPA is an efficient solution for researchers studying splicing variations in various biological contexts, including disease.