Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb
| Autor: | Marco Osterwalder, Tyler H. Garvin, Elizabeth Lee, Catherine S. Pickle, Momoe Kato, Iros Barozzi, Axel Visel, Ingrid Plajzer-Frick, Jennifer A. Akiyama, Remo Monti, Niko Beerenwinkel, Len A. Pennacchio, Veena Afzal, Diane E. Dickel |
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| Přispěvatelé: | Ioshikhes, Ilya, Ioshikhes, I |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Embryology Hydrolases Gene Expression computer.software_genre Biochemistry Mathematical Sciences Computer Architecture Machine Learning Mice 0302 clinical medicine Feature (machine learning) Medicine and Health Sciences Biology (General) Musculoskeletal System Epigenomics Interpretability Genome Deoxyribonucleases Mammalian Genomics Ecology Chromosome Biology Genomics Biological Sciences Chromatin Enzymes Enhancer Elements Genetic Computational Theory and Mathematics Modeling and Simulation Legs Epigenetics Data mining Growth and Development Anatomy Research Article Computer and Information Sciences Enhancer Elements Bioinformatics Nucleases QH301-705.5 1.1 Normal biological development and functioning Computational biology Biology 03 medical and health sciences Cellular and Molecular Neuroscience Genetic Underpinning research Artificial Intelligence Information and Computing Sciences DNA-binding proteins Genetics Animals Gene Regulation Enhancer Molecular Biology 01 Mathematical Sciences Ecology Evolution Behavior and Systematics Biology and life sciences Human Genome Limbs (Anatomy) Embryos Proteins Statistical model Extremities Cell Biology 06 Biological Sciences DNA binding site 030104 developmental biology Animal Genomics Enzymology 08 Information and Computing Sciences Generic health relevance computer 030217 neurology & neurosurgery Software Developmental Biology User Interfaces |
| Zdroj: | PLoS Computational Biology, Vol 13, Iss 8, p e1005720 (2017) Monti, R; Barozzi, I; Osterwalder, M; Lee, E; Kato, M; Garvin, TH; et al.(2017). Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb. PLoS computational biology, 13(8), e1005720. doi: 10.1371/journal.pcbi.1005720. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/5f99f1p8 PLoS Computational Biology PLoS Computational Biology, 13 (8) PLoS computational biology, vol 13, iss 8 |
| ISSN: | 1553-7358 1553-734X |
| DOI: | 10.1371/journal.pcbi.1005720. |
| Popis: | Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of >50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage of the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations. PLoS Computational Biology, 13 (8) ISSN:1553-734X ISSN:1553-7358 |
| Databáze: | OpenAIRE |
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