ArticleBioinformatics (Oxford, England)2014
Discriminative motif analysis of high-throughput dataset.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma.Apoptosis : an international journal on programmed cell death · 2024Article
- Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.BMC genomics · 2023Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
- MEIRLOP: improving score-based motif enrichment by incorporating sequence bias covariates.BMC bioinformatics · 2020Article
- Hallmarks and Determinants of Oncogenic Translation Revealed by Ribosome Profiling in Models of Breast Cancer.Translational oncology · 2020Article
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.Bioinformatics (Oxford, England) · 2019Article
- HOT or not: examining the basis of high-occupancy target regions.Nucleic acids research · 2019Article
- FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Super-lncRNAs: identification of lncRNAs that target super-enhancers via RNA:DNA:DNA triplex formation.RNA (New York, N.Y.) · 2017Article
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- RCAS: an RNA centric annotation system for transcriptome-wide regions of interest.Nucleic acids research · 2017Article
- Article
- Conversion of MyoD to a neurogenic factor: binding site specificity determines lineage.Cell reports · 2015Article
- Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.PloS one · 2015Article
- Article
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6 authors.
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Abstract
motivationHigh-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naïve background distribution but are of questionable biological relevance.
resultsWe describe a simple yet effective algorithm for discovering differential motifs between two sequence datasets that is effective in eliminating systematic biases and scalable to large datasets. Tested on 207 ENCODE ChIP-seq datasets, our method identifies correct motifs in 78% of the datasets with known motifs, demonstrating improvement in both accuracy and efficiency compared with DREME, another state-of-art discriminative motif discovery tool. More interestingly, on the remaining more challenging datasets, we identify common technical or biological factors that compromise the motif search results and use advanced features of our tool to control for these factors. We also present case studies demonstrating the ability of our method to detect single base pair differences in DNA specificity of two similar TFs. Lastly, we demonstrate discovery of key TF motifs involved in tissue specification by examination of high-throughput DNase accessibility data. AVAILABILITY: The motifRG package is publically available via the bioconductor repository. CONTACT: yzizhen@fhcrc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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