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bioRxiv · 10.1101/2024.01.25.577290

Adaptive, sample-specific parameter selection for more accurate transcript assembly

Shen, Y.·Yan, Z.·Kingsford, C.
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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.

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Shen, Y., Yan, Z., Kingsford, C.. 2024-01-30. Adaptive, sample-specific parameter selection for more accurate transcript assembly. https://doi.org/10.1101/2024.01.25.577290

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