Shen Yihang, Yan Zhiwen, Kingsford Carl
Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA.
bioRxiv. 2024 Oct 30:2024.01.25.577290. doi: 10.1101/2024.01.25.577290.
We introduce AutoTuneX, a data-driven, AI system designed to automatically predict optimal parameters for transcript assemblers - tools for reconstructing expressed transcripts from the reads in a given RNA-seq sample. AutoTuneX is built by learning parameter knowledge from existing RNA-seq samples and transferring this knowledge to unseen samples. On 1588 human RNA-seq samples tested with two transcript assemblers, AutoTuneX predicts parameters that resulted in 98% of samples achieving more accurate transcript assembly compared to using default parameter settings, with some samples experiencing up to a 600% improvement in AUC. AutoTuneX offers a new strategy for automatically optimizing use of sequence analysis tools.
我们推出了AutoTuneX，这是一个数据驱动的人工智能系统，旨在自动预测转录本组装工具的最佳参数，转录本组装工具用于从给定RNA测序样本中的 reads 重建表达的转录本。AutoTuneX通过从现有的RNA测序样本中学习参数知识，并将这些知识转移到未见样本中构建而成。在用两种转录本组装工具测试的1588个人类RNA测序样本上，与使用默认参数设置相比，AutoTuneX预测的参数使得98%的样本实现了更准确的转录本组装，一些样本的AUC提高了600%。AutoTuneX为自动优化序列分析工具的使用提供了一种新策略。
