ArticleBMC bioinformatics2011
Motif-All: discovering all phosphorylation motifs.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals.Cell reports methods · 2024Article
- A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.Genomics, proteomics & bioinformatics · 2023Review
- Article
- TcdB ofInternational journal of molecular sciences · 2022Article
- Isotopically Dimethyl Labeling-Based Quantitative Proteomic Analysis of Phosphoproteomes of Soybean Cultivars.Biomolecules · 2021Article
- 3D Functional Genomics Screens Identify CREBBP as a Targetable Driver in Aggressive Triple-Negative Breast Cancer.Cancer research · 2021Article
- Phosphorylation Site Motifs in Plant Protein Kinases and Their Substrates.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Molecular dissection of Chagas induced cardiomyopathy reveals central disease associated and druggable signaling pathways.PLoS neglected tropical diseases · 2020Article
- MoMo: discovery of statistically significant post-translational modification motifs.Bioinformatics (Oxford, England) · 2019Article
- Article
- Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria.PloS one · 2018Article
- Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis.BMC bioinformatics · 2015Article
- Identifying novel targets of oncogenic EGF receptor signaling in lung cancer through global phosphoproteomics.Proteomics · 2015Article
- Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures.Cell cycle (Georgetown, Tex.) · 2014Article
- Stable isotope metabolic labeling-based quantitative phosphoproteomic analysis of Arabidopsis mutants reveals ethylene-regulated time-dependent phosphoproteins and putative substrates of constitutive triple response 1 kinase.Molecular & cellular proteomics : MCP · 2013Article
- MMFPh: a maximal motif finder for phosphoproteomics datasets.Bioinformatics (Oxford, England) · 2012Article
- Characterization of Kinase Expression Related to Increased Migration of PC-3M Cells Using Global Comparative Phosphoproteome Analysis.Cancer genomics & proteomicsArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques.
resultsWe describe an algorithm called Motif-All that is able to efficiently identify all statistically significant motifs. The proposed method explores a support constraint to reduce search space and avoid generating random artifacts. As the number of phosphorylated peptides are far less than that of unphosphorylated ones, we divide the mining process into two stages: The first step generates candidates from the set of phosphorylated sequences using only support constraint and the second step tests the statistical significance of each candidate using the odds ratio derived from the whole data set. Experimental results on real data show that Motif-All outperforms current algorithms in terms of both effectiveness and efficiency.
conclusionsMotif-All is a useful tool for discovering statistically significant phosphorylation motifs. Source codes and data sets are available at: http://bioinformatics.ust.hk/MotifAll.rar.
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