ArticleBioinformatics (Oxford, England)2014
EXTREME: an online EM algorithm for motif discovery.
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 21 papers.
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21 citing papers in PubMed, 49 citations in OpenAlex.
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- MicrosatNavigator: exploring nonrandom distribution and lineage-specificity of microsatellite repeat motifs on vertebrate sex chromosomes across 186 whole genomes.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2023Article
- Freezing firefly algorithm for efficient planted (ℓ, d) motif search.Medical & biological engineering & computing · 2022Article
- BML: a versatile web server for bipartite motif discovery.Briefings in bioinformatics · 2022Article
- MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.Bioinformatics (Oxford, England) · 2020Article
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- A Clustering Approach for Motif Discovery in ChIP-Seq Dataset.Entropy (Basel, Switzerland) · 2019Article
- FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.Methods (San Diego, Calif.) · 2019Article
- YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU.Bioinformatics (Oxford, England) · 2018Article
- Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture.BMC bioinformatics · 2018Article
- Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets.Nucleic acids research · 2018Article
- A reporter system coupled with high-throughput sequencing unveils key bacterial transcription and translation determinants.Nature communications · 2017Article
- Dynamic Gene Regulatory Networks of Human Myeloid Differentiation.Cell systems · 2017Article
- RefSelect: a reference sequence selection algorithm for planted (l, d) motif search.BMC bioinformatics · 2016Article
- DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.Nucleic acids research · 2016Article
- Article
- An Affinity Propagation-Based DNA Motif Discovery Algorithm.BioMed research international · 2015Article
- Motif signatures in stretch enhancers are enriched for disease-associated genetic variants.Epigenetics & chromatin · 2015Article
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2 authors at 1 institution in 1 country.
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Abstract
motivationIdentifying regulatory elements is a fundamental problem in the field of gene transcription. Motif discovery-the task of identifying the sequence preference of transcription factor proteins, which bind to these elements-is an important step in this challenge. MEME is a popular motif discovery algorithm. Unfortunately, MEME's running time scales poorly with the size of the dataset. Experiments such as ChIP-Seq and DNase-Seq are providing a rich amount of information on the binding preference of transcription factors. MEME cannot discover motifs in data from these experiments in a practical amount of time without a compromising strategy such as discarding a majority of the sequences.
resultsWe present EXTREME, a motif discovery algorithm designed to find DNA-binding motifs in ChIP-Seq and DNase-Seq data. Unlike MEME, which uses the expectation-maximization algorithm for motif discovery, EXTREME uses the online expectation-maximization algorithm to discover motifs. EXTREME can discover motifs in large datasets in a practical amount of time without discarding any sequences. Using EXTREME on ChIP-Seq and DNase-Seq data, we discover many motifs, including some novel and infrequent motifs that can only be discovered by using the entire dataset. Conservation analysis of one of these novel infrequent motifs confirms that it is evolutionarily conserved and possibly functional. AVAILABILITY AND IMPLEMENTATION: All source code is available at the Github repository http://github.com/uci-cbcl/EXTREME.
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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.