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.
Competing Interest Statement
Carl Kingsford is a co-founder of Ocean Genomics, Inc.
Footnotes
Experimental results updated, new results reported in the manuscript.