NucleaRDB: information system for nuclear receptors

Vroling, Bas; Thorne, D. J.; McDermott, Philip; Joosten, Henk Jan; Attwood, Teresa K.; Pettifer, Steve; Vriend, Gert

doi:10.1093/nar/gkr960

Cited by 20 publications

(20 citation statements)

References 17 publications

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“…It is developed to provide researchers a convenient way to compare experimental and predicted binding sites for various NRs, along with genomic annotations. Other existing NR databases are devoted to the sequences, structures and functions (Martinez et al, 1998), mutations (Van Durme et al, 2003;Vroling et al, 2012) and phylogenies (Ruau et al, 2004) of NRs, not their binding sites. Recently, Ochsner et al (2012) set-up a database, Transcriptomine, which is focused on the expression and function of NRs-related genes.…”

Section: Resultsmentioning

confidence: 99%

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse

et al. 2012

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Summary: Nuclear receptors (NRs) are a class of transcription factors playing important roles in various biological processes. An NR often impacts numerous genes and different NRs share overlapped target networks. To fulfil the need for a database incorporating binding sites of different NRs at various conditions for easy comparison and visualization to improve our understanding of NR binding mechanisms, we have developed NURBS, a database for experimental and predicted nuclear receptor binding sites of mouse (NURBS). NURBS currently contains binding sites across the whole-mouse genome of 8 NRs identified in 40 chromatin immunoprecipitation with massively parallel DNA sequencing experiments. All datasets are processed using a widely used procedure and same statistical criteria to ensure the binding sites derived from different datasets are comparable. NURBS also provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model. Availability: The GBrowse-based user interface of NURBS is freely accessible at http://shark.abl.ku.edu/nurbs/. NR-HMM and all results can be downloaded for free at the website.

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Section: Resultsmentioning

confidence: 99%

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse

et al. 2012

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“…C , NHRs required a simple workflow. Russ et al, 2005 and NucleaRDB [6] provided input. One gene mapped to neither the NCBI gene nor synonyms list.…”

Section: Resultsmentioning

confidence: 99%

“…For a given class, results from several sources were reconciled through the NCBI Gene List and entries unique to a single source were confirmed against databases like UniProt or the primary literature. Nuclear hormone receptors (NHR) illustrate a straightforward case with well curated sources [6] requiring little additional scrutiny (Fig. 1c).…”

Section: Resultsmentioning

confidence: 99%

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Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

et al. 2013

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A major goal of cancer genome sequencing is to identify mutations or other somatic alterations that can be targeted by selective and specific drugs. dGene is an annotation tool designed to rapidly identify genes belonging to one of ten druggable classes that are frequently targeted in cancer drug development. These classes were comprehensively populated by combining and manually curating data from multiple specialized and general databases. dGene was used by The Cancer Genome Atlas squamous cell lung cancer project, and here we further demonstrate its utility using recently released breast cancer genome sequencing data. dGene is designed to be usable by any cancer researcher without the need for support from a bioinformatics specialist. A full description of dGene and options for its implementation are provided here.

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“…Specific databases about a single protein family can bring researchers great convenience in using all data needed for their research, while relieving them of the onerous tasks to retrieve many data from different sources [5]. As a professional database for NRs, NucleaRDB holds many different data types in a well-organized form, what's more, the data are validated, internally consistent and updated regularly [6,7].…”

Section: Introductionmentioning

confidence: 99%

NRPred-FS: A Feature Selection based Two-level Predictor for Nuclear Receptors

Xiao¹

2014

J Proteomics Bioinform

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Citation: Wang P, Xiao X (2014) NRPred-FS: A Feature Selection based Twolevel Predictor for Nuclear Receptors. J Proteomics Bioinform S9: 002.All the aforementioned methods each have their own merits and did play a role in stimulating the development of this area, but they all have the following main shortcomings. (1) The datasets constructed to train the predictors were derived from the old version of NucleaRDB, which has been much updated recently. (2) Various feature extraction Abstract Motivation: Nuclear receptors (NRs) play a role in all developmental and physiological processes and are important drug targets in a wide variety of disease and healthy states. In the past years, to identify NRs and their subfamilies with high throughput and low-cost, many machine learning methods have been introduced. However, these predictors are all developed based on old dataset in the NucleaRDB, what's more, no feature selection technique is employed, so that the performances are very limited.Result: In this study, a feature selection based two-level predictor, called NRPred-FS, is developed that can be used to identify a query protein as a nuclear receptor or not based on its sequence information alone, if it is, the prediction will be automatically continued to further identify it among the following eight subfamilies: (1) Thyroid hormone like (NR1), (2) HNF4-like (NR2), (3) Estrogen like, (4) Nerve growth factor IB-like (NR4), (5) Fushi tarazu-F1 like (NR5), (6) Germ cell nuclear factor like (NR6), (7) knirps like (NR0A), and (8) DAX like (NR0B). The nuclear receptor sequences are encoded as sequence-derived feature vectors formed by incorporating various physicochemical and statistical features. Furthermore, the features set are optimized by forward feature selection algorithm for reducing the feature dimensions and for getting higher classifying accuracy. As a demonstration, this method gone through rigorous testing on a benchmark datasets derived from the latest version of NucleaRDB and UniProt. The overall prediction accuracies of leave-one-out cross-validation were about 97% and 93% in the first and second level respectively. As a convenience to the users, the powerful predictor, NRPred-FS, is freely accessible at http://www.jci-bioinfo.cn/NRPred-FS. Hopefully it will be a useful vehicle for identifying NRs and their subfamilies. Journal of Proteomics & BioinformaticsJ ou rnal of P ro te om ics & B io in fo rmatic s ISSN: 0974-276X Citation: Wang P, Xiao X (2014) NRPred-FS: A Feature Selection based Two-level Predictor for Nuclear Receptors. J Proteomics Bioinform S9: 002.

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NucleaRDB: information system for nuclear receptors

Cited by 20 publications

References 17 publications

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse

Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

NRPred-FS: A Feature Selection based Two-level Predictor for Nuclear Receptors

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