A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations

• Published in: BMC Bioinformatics Vol. 22; no. Suppl 6; pp. 1 - 12
• Main Authors: Jia, Hao, Park, Sung-Joon, Nakai, Kenta
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 02.06.2021; BioMed Central; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accessibility; Algorithms; Binding sites; Bioinformatics; Biology (General); Biomedical and Life Sciences; Cell lines; Chromatin; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Computer applications to medicine. Medical informatics; Conformation; Data mining; Datasets; Deep learning; Deoxyribonucleic acid; DNA; Epigenetics; Epigenome; Gene expression; Gene regulation; Genetic research; Genetic variation; Genomes; Genomics; Histone Code; Histones; Humans; Labels; Life Sciences; Machine learning; Methods; Microarrays; Mutation; Neural networks; Non-coding variants; Nucleotide sequence; Performance prediction; Pseudo label; QH301-705.5; R858-859.7; Semi-supervised learning; Software; Supervised Machine Learning; Support vector machines; Japan; Deep learning; Non-coding variants; Epigenome; Semi-supervised learning; Pseudo label
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-03999-8

Background Understanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data. Results We prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to the dataset demonstrated its outstanding performance, compared with that of existing tools. Our results also indicated that the semi-supervised model with pseudo labels achieves higher predictive performance than the supervised model without pseudo labels. Interestingly, a model trained with the data in a certain cell line is unlikely to succeed in other cell lines, which implies the cell-type-specific nature of the non-coding variants. Remarkably, we found that DNA accessibility significantly contributes to the functional consequence of variants, which suggests the importance of open chromatin conformation prior to establishing the interaction of non-coding variants with gene regulation. Conclusions The semi-supervised deep learning model coupled with pseudo labeling has advantages in studying with limited datasets, which is not unusual in biology. Our study provides an effective approach in finding non-coding mutations potentially associated with various biological phenomena, including human diseases.