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Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

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STAR Suite: Integrating transcriptomics through AI software engineering in the NIH MorPhiC consortium.

Ling-Hong Hung¹, Ka Yee Yeung¹

¹School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA, USA.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

Summary

This summary is machine-generated.

STAR Suite modernizes bioinformatics pipelines by integrating adapter trimming into the STAR aligner source code. This high-performance solution improves scalability and reduces file processing for transcriptomics data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Bioinformatics pipelines often use discrete binaries and scripts, leading to performance issues due to intermediate file handling.
The standard STAR aligner for transcriptomics requires external scripts for tasks like adapter trimming, causing inefficiencies with large files.
Scalability challenges were identified for uniform data processing within the NIH MorPhiC consortium.

Purpose of the Study:

To develop a modernized, high-performance bioinformatics software solution.
To address the limitations of traditional pipeline architectures, specifically within the STAR aligner.
To improve the scalability and efficiency of transcriptomics data processing.

Main Methods:

Integrated adapter trimming and other functionalities directly into the C++ source code of the STAR aligner.
Developed STAR Suite, comprising four unified modules: STAR-core, STAR-Flex, STAR-Perturb, and STAR-SLAM.
Engineered the software through a combination of human expertise and AI implementation over four months.

Main Results:

Successfully modernized the STAR aligner codebase by adding over 92,000 lines of code.
Created a pre-compiled binary for STAR Suite without introducing new dependencies.
Demonstrated a new paradigm for rapid development of high-performance bioinformatics software.

Conclusions:

STAR Suite offers a scalable and efficient solution for transcriptomics data analysis.
The integrated approach significantly improves performance compared to script-based pipelines.
This work establishes a model for the rapid evolution of complex bioinformatics tools.