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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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ASimulatoR: splice-aware RNA-Seq data simulation.

Quirin Manz¹, Olga Tsoy¹, Amit Fenn¹

¹Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.

Bioinformatics (Oxford, England)

|March 1, 2021

Summary

This summary is machine-generated.

Researchers developed ASimulatoR, an R package for simulating RNA-Seq data. This tool generates gold-standard datasets to evaluate alternative splicing analysis tools and understand factors like sequencing depth.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Numerous RNA-Seq analysis tools exist for alternative splicing (AS) detection.
A lack of standardized datasets hinders the comparative evaluation of these AS tools.
Understanding the impact of sequencing depth on AS detection performance is crucial.

Purpose of the Study:

To introduce ASimulatoR, an R package designed for simulating RNA-Seq datasets.
To provide a method for generating gold-standard data with controlled alternative splicing events.
To enable the rigorous evaluation and comparison of alternative splicing detection tools.

Main Methods:

ASimulatoR simulates RNA-Seq data with user-defined control over alternative splicing event distributions.
The package allows for the generation of 'gold standard' datasets, crucial for benchmarking.
It facilitates the investigation of how sequencing depth influences the performance of AS detection algorithms.

Main Results:

ASimulatoR provides a flexible framework for creating realistic RNA-Seq simulation data.
The simulated datasets can be used to assess the accuracy and sensitivity of various AS analysis tools.
The tool aids in understanding the performance limitations related to sequencing depth.

Conclusions:

ASimulatoR addresses the need for standardized evaluation data in alternative splicing analysis.
The R package offers a valuable resource for the bioinformatics community to benchmark AS tools.
This simulation approach enhances the reliability and interpretability of RNA-Seq-based alternative splicing studies.