# BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance

## TL;DR

BOAssembler is a Bayesian Optimization framework that automates hyper-parameter tuning for RNA-Seq assembly tools, significantly enhancing their performance and reliability across diverse datasets.

## Contribution

It introduces an end-to-end automatic tuning method for RNA-Seq assemblers using Bayesian Optimization, addressing the challenge of manual hyper-parameter tuning.

## Key findings

- Improved assembly performance across multiple datasets
- Automated tuning reduces time and effort for users
- Potential to enhance downstream biological analyses

## Abstract

High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets. Here we propose BOAssembler (https://github.com/olivomao/boassembler), a framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies.