Alternative splicing enables a gene spliced into different isoforms and protein variants. Identifying individual functions of isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few have been moved toward isoform function prediction, mainly due to the unavailable functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with tissue specificity. TS-Isofun firstly constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by integrating them with weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.
|Original language||English (US)|
|Number of pages||1|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|State||Published - 2021|