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  • Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence
    Author: Click: Mar 08, 19
     
      
    PNAS
    DOI:10.1073/pnas.1814551116
    published online:March 6, 2019

          Significance

    Machine learning methodologies can be applied readily to biological problems, but standard training and testing methods are not designed to control for evolutionary relatedness or other biological phenomena. In this article, we propose, implement, and test two methods to control for and utilize evolutionary relatedness within a predictive deep learning framework. The methods are tested and applied within the context of predicting mRNA expression levels from whole-genome DNA sequence data and are applicable across biological organisms. Potential use cases for the methods include plant and animal breeding, disease research, gene editing, and others.

          Abstract

    Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family–guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3′ UTR is more important for fine-tuning mRNA abundance levels while the 5′ UTR is more important for large-scale changes.

    
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