ArticlePLoS computational biology2009
Discovery of regulatory elements is improved by a discriminatory approach.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 27 citations in OpenAlex.
- Transcription factor expression defines subclasses of developing projection neurons highly similar to single-cell RNA-seq subtypes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.Bioinformatics (Oxford, England) · 2019Article
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- A common class of transcripts with 5'-intron depletion, distinct early coding sequence features, andRNA (New York, N.Y.) · 2017Article
- Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.Nucleic acids research · 2014Article
- STEME: a robust, accurate motif finder for large data sets.PloS one · 2014Article
- The enhancer landscape during early neocortical development reveals patterns of dense regulation and co-option.PLoS genetics · 2013Article
- TherMos: Estimating protein-DNA binding energies from in vivo binding profiles.Nucleic acids research · 2013Article
- Article
- MotifClick: prediction of cis-regulatory binding sites via merging cliques.BMC bioinformatics · 2011Article
- GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge.Algorithms for molecular biology : AMB · 2011Article
- CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments.Bioinformatics (Oxford, England) · 2011Article
- AMD, an automated motif discovery tool using stepwise refinement of gapped consensuses.PloS one · 2011Article
- NanoCAGE: a high-resolution technique to discover and interrogate cell transcriptomes.Cold Spring Harbor protocols · 2011Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to "pattern drowning". We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.
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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.