ArticleBioinformatics (Oxford, England)2017
Denoising genome-wide histone ChIP-seq with convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.Research square · 2025Article
- PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling.bioRxiv : the preprint server for biology · 2025Article
- Summary of ChIP-Seq Methods and Description of an Optimized ChIP-Seq Protocol.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention.Genome biology · 2023Article
- A deep learning framework for characterization of genotype data.G3 (Bethesda, Md.) · 2022Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
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- Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders.BMC bioinformatics · 2021Article
- A self-attention model for inferring cooperativity between regulatory features.Nucleic acids research · 2021Article
- Deep learning-based enhancement of epigenomics data with AtacWorks.Nature communications · 2021Article
- Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.Computational and structural biotechnology journal · 2021Review
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Fully interpretable deep learning model of transcriptional control.Bioinformatics (Oxford, England) · 2020Article
- The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding.Biomolecules · 2020Review
- Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures.Molecular metabolism · 2020Article
- Prediction of sgRNA on-target activity in bacteria by deep learning.BMC bioinformatics · 2019Article
- Denoising of Aligned Genomic Data.Scientific reports · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
motivationChromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging.
resultsWe introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. This overcomes various sources of noise and variability, substantially enhancing and recovering signal when applied to low-quality chromatin profiling datasets across individuals, cell types and species. Our method has the potential to improve data quality at reduced costs. More broadly, this approach-using a high-dimensional discriminative model to encode a generative noise process-is generally applicable to other biological domains where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution. AVAILABILITY AND IMPLEMENTATION: https://github.com/kundajelab/coda . CONTACT: akundaje@stanford.edu.
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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 OpenQuestion graph.