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Related Experiment Video

Updated: Nov 3, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

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

Hao Jia¹, Sung-Joon Park^1,2, Kenta Nakai^3,4

¹Department of Computer Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.

BMC Bioinformatics

|June 3, 2021

Summary

We developed a novel semi-supervised deep learning method using pseudo labels to predict the functional impact of non-coding variants. This approach improves accuracy with limited data, aiding in disease-associated mutation discovery.

Keywords:

Deep learning Epigenome Non-coding variants Pseudo label Semi-supervised learning

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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Published on: August 24, 2013

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Last Updated: Nov 3, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Published on: August 24, 2013

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Non-coding variants significantly impact gene expression and disease development.
Predicting functional effects of non-coding variants is challenging due to data scarcity.
Existing computational tools require improvement for accurate functional impact prediction.

Purpose of the Study:

To develop a novel computational method for predicting the functional impact of non-coding variants.
To leverage semi-supervised deep learning with pseudo labels for enhanced prediction accuracy.
To improve the understanding of non-coding variant roles in biological processes and diseases.

Main Methods:

A semi-supervised deep learning model incorporating pseudo labels was developed.
The model utilized histone marks, DNA accessibility, and sequence context data.
Performance was evaluated using datasets from GM12878, HepG2, and K562 cell lines.

Main Results:

The proposed semi-supervised method demonstrated superior performance compared to existing tools.
Pseudo-labeled semi-supervised learning outperformed supervised learning without pseudo labels.
DNA accessibility was identified as a key factor in determining variant functional consequences, highlighting cell-type specificity.

Conclusions:

Semi-supervised deep learning with pseudo labeling offers an effective strategy for analyzing limited biological datasets.
This approach provides a powerful tool for identifying non-coding mutations linked to human diseases.
The findings underscore the importance of cell-type-specific analysis and DNA accessibility in non-coding variant function.