Open AccessImproved prediction of smoking status via isoform-aware RNA-seq deep learning models
Author(s) -
Zifeng Wang,
Aria Masoomi,
Zhonghui Xu,
Adel Boueiz,
Sool Lee,
Tingting Zhao,
Russell P. Bowler,
Michael H. Cho,
Edwin K. Silverman,
Craig P. Hersh,
Jennifer Dy,
Peter J. Castaldi
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009433
Subject(s) - exon , alternative splicing , gene isoform , computational biology , gene , rna splicing , rna seq , leverage (statistics) , biology , rna , genetics , gene expression , artificial intelligence , bioinformatics , computer science , transcriptome
Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC (area under the receiver operating characteristic) of 0.88, which improved to an AUC of 0.94 using exon quantifications from a larger set of genes. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.