SDEAP: a splice graph based differential transcript expression analysis tool for population data

Yang, Ei-Wen; Jiang, Tao

doi:10.1093/bioinformatics/btw513

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…For each GO term, we check the consistency between the presence or absence of the corresponding sequence feature in associated isoforms and the functional predictions concerning this GO term. To quantify the consistency for the three GO terms separately, the Jaccard indices are calculated as in the literature (Yang et al , 2016). The same computational experiment is repeated for the three other methods as well to compare.…”

Section: Results and Validationmentioning

confidence: 99%

DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning

et al. 2019

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Motivation Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc. Results In this study, we present a novel approach, DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), to predict isoform functions. To integrate various types of data, our approach adopts a hybrid framework by first using a deep neural network (DNN) to predict the functions of isoforms from their genomic sequences and then refining the prediction using a conditional random field (CRF) based on co-expression relationship. To overcome the lack of isoform-level ground truth labels, we further propose an iterative semi-supervised learning algorithm to train both the DNN and CRF together. Our extensive computational experiments demonstrate that DIFFUSE could effectively predict the functions of isoforms and genes. It achieves an average area under the receiver operating characteristics curve of 0.840 and area under the precision–recall curve of 0.581 over 4184 GO functional categories, which are significantly higher than the state-of-the-art methods. We further validate the prediction results by analyzing the correlation between functional similarity, sequence similarity, expression similarity and structural similarity, as well as the consistency between the predicted functions and some well-studied functional features of isoform sequences. Availability and implementation https://github.com/haochenucr/DIFFUSE. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Results and Validationmentioning

confidence: 99%