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

Abstract

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.