ArticlePLoS computational biology2015
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 76 citations in OpenAlex.
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- ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions.bioRxiv : the preprint server for biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.PLoS computational biology · 2023Article
- BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin.Genome biology · 2022Article
- Dynamic regulatory module networks for inference of cell type-specific transcriptional networks.Genome research · 2022Article
- Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.PLoS computational biology · 2022Article
- Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.Nucleic acids research · 2021Article
- A machine learning-based framework for modeling transcription elongation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome.Biological psychiatry · 2021Review
- High-Resolution Mapping of Multiway Enhancer-Promoter Interactions Regulating Pathogen Detection.Molecular cell · 2020Article
- Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay.Genome research · 2020Article
- Enhancer Predictions and Genome-Wide Regulatory Circuits.Annual review of genomics and human genetics · 2020Review
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
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- Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.Nucleic acids research · 2019Article
- Epigenomic analysis reveals DNA motifs regulating histone modifications in human and mouse.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A De Novo Shape Motif Discovery Algorithm Reveals Preferences of Transcription Factors for DNA Shape Beyond Sequence Motifs.Cell systems · 2019Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.
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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.