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Data-driven AI system for learning how to run transcript assemblers.

Yihang Shen¹, Zhiwen Yan¹, Carl Kingsford¹

¹Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA.

Biorxiv : the Preprint Server for Biology

|November 18, 2024

Summary

This summary is machine-generated.

AutoTuneX, an AI system, optimizes transcript assembly parameters for RNA-seq data. It improves accuracy in 98% of samples, significantly enhancing transcript reconstruction from sequencing reads.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-seq) is crucial for understanding gene expression.
Transcript assemblers reconstruct expressed transcripts but require optimal parameter settings.
Manual parameter tuning is time-consuming and may not yield optimal results.

Purpose of the Study:

To introduce AutoTuneX, an AI-driven system for automatic optimization of transcript assembler parameters.
To improve the accuracy and efficiency of transcript assembly from RNA-seq data.
To provide a data-driven strategy for parameter selection in sequence analysis tools.

Main Methods:

Developed AutoTuneX, a machine learning system leveraging knowledge from existing RNA-seq samples.
Trained AutoTuneX to predict optimal parameters for transcript assemblers.
Evaluated AutoTuneX on 1588 human RNA-seq samples using two different transcript assemblers.

Main Results:

AutoTuneX predicted parameters that led to more accurate transcript assembly in 98% of tested samples.
Significant improvements in Area Under the Curve (AUC) were observed, with some cases reaching up to 600% enhancement.
The system demonstrated effective knowledge transfer to optimize parameters for unseen RNA-seq samples.

Conclusions:

AutoTuneX offers a robust, data-driven approach to automatically optimize transcript assembler parameters.
The system substantially enhances the accuracy of transcript assembly compared to default settings.
AutoTuneX represents a novel strategy for optimizing the performance of bioinformatics tools in sequence analysis.