ArticleNucleic acids research2021
DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.Interdisciplinary sciences, computational life sciences · 2026Article
- The SynMall resource for characterizing the functional impact of synonymous variation.Genome research · 2026Article
- Systematic identification of pathogenic variants of non-small cell lung cancer in the promoters of DNA-damage repair genes.EBioMedicine · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence.Molecular psychiatry · 2024Article
- Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Briefings in bioinformatics · 2024Review
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.Journal of Alzheimer's disease : JAD · 2024Article
- Genetic overlap between Alzheimer's disease and immune-mediated diseases: An atlas of shared genetic determinants and biological convergence.Research square · 2023Article
- m6A-TSHub: Unveiling the Context-specific mGenomics, proteomics & bioinformatics · 2023Article
- DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.International journal of molecular sciences · 2023Article
- deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues.Genomics, proteomics & bioinformatics · 2023Article
- Building integrative functional maps of gene regulation.Human molecular genetics · 2022Article
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
- Designing optimal convolutional neural network architecture using differential evolution algorithm.Patterns (New York, N.Y.) · 2022Article
- A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the functional activity of non-coding genetic variants. This new server is built on a convolutional neural network (CNN) framework that has been extensively evaluated. Specifically, we collected chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles covering 225 tissues or cell types. With such comprehensive epigenomics annotations, DeepFun expands the functionality of existing non-coding variant prioritizing tools to provide a more specific functional assessment on non-coding variants in a tissue- and cell type-specific manner. By using the datasets from various GWAS studies, we conducted independent validations and demonstrated the functions of the DeepFun web server in predicting the effect of a non-coding variant in a specific tissue or cell type, as well as visualizing the potential motifs in the region around variants. We expect our server will be widely used in genetics, functional genomics, and disease studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.