PEDRo: A database for storing, searching and disseminating experimental proteomics data

Garwood, Kevin; McLaughlin, Thomas J.; Garwood, Chris; Joens, Scott; Morrison, Norman; Taylor, Christopher F; Carroll, Kathleen M.; Evans, Caroline A.; Whetton, Anthony D.; Hart, Sarah R.; Stead, David; Yin, Zhikang; Brown, Alistair J. P.; Hesketh, Andrew; Chater, Keith F.; Hansson, Lena; Mewissen, Muriel; Ghazal, Peter; Howard, Julie A.; Lilley, Kathryn S.; Gaskell, Simon J.; Brass, Andy; Hubbard, Simon J.; Oliver, Stephen G.; Paton, Norman W.

doi:10.1186/1471-2164-5-68

Cited by 61 publications

(30 citation statements)

References 21 publications

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“…However, there are many other pertinent areas that need to be stressed, including the need to integrate SBML models into post-genomic databases with schemas such as those for genomics (GIMS [64]), proteomics (PEDRo [65]) and metabolomics (ArMet [66]). Only then can we have a truly integrative Systems Biology.…”

Section: Other Areas and Concluding Remarksmentioning

confidence: 99%

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

Kell

2005

Biochemical Society Transactions

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In answering the question 'Systems Biology--will it work?' (which it self-evidently has already), it is appropriate to highlight advances in philosophy, in new technique development and in novel findings. In terms of philosophy, we see that systems biology involves an iterative interplay between linked activities--instance, between theory and experiment, between induction and deduction and between measurements of parameters and variables--with more emphasis than has perhaps been common now being focused on the first in each of these pairs. In technique development, we highlight closed loop machine learning and its use in the optimization of scientific instrumentation, and the ability to effect high-quality and quasi-continuous optical images of cells. This leads to many important and novel findings. In the first case, these may involve new biomarkers for disease, whereas in the second case, we have determined that many biological signals may be frequency-rather than amplitude-encoded. This leads to a very different view of how signalling 'works' (equations such as that of Michaelis and Menten which use only amplitudes, i.e. concentrations, are inadequate descriptors), lays emphasis on the signal processing network elements that lie 'downstream' of what are traditionally considered the signals, and allows one simply to understand how cross-talk may be avoided between pathways which nevertheless use common signalling elements. The language of cells is much richer than we had supposed, and we are now well placed to decode it.

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Section: Other Areas and Concluding Remarksmentioning

confidence: 99%

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

Kell

2005

Biochemical Society Transactions

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“…These data, which were kindly provided by Andrew Hesketh (John Innes Centre), are deposited in the public repository, PEDRoDB (13), and are available for download from pedrodb. man.ac.uk:8080/pedrodb/pages/Browse.jsp.…”

Section: Methodsmentioning

confidence: 99%

Universal Metrics for Quality Assessment of Protein Identifications by Mass Spectrometry

Stead

Preece

Brown

2006

Molecular & Cellular Proteomics

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Increasing numbers of large proteomic datasets are becoming available. As attempts are made to interpret these datasets and integrate them with other forms of genomic data, researchers are becoming more aware of the importance of data quality with respect to protein identification. We present three simple and universal metrics that describe different aspects of the quality of protein identifications by peptide mass fingerprinting. Hit ratio gives an indication of the signal-to-noise ratio in a mass spectrum, mass coverage measures the amount of protein sequence matched, and excess of limit-digested peptides reflects the completeness of the digestion that precedes the peptide mass fingerprinting. Receiver-operating characteristic plots show that the novel metric, excess of limit-digested peptides, can discriminate between correct and random matches more accurately than search score when validating the results from a state-of-the-art protein identification software system (Mascot) especially when combined with the two other metrics, hit ratio and mass coverage. Recommendations are made regarding the use of the metrics when reporting protein identification experiments.

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“…El escenario de aplicación elegido es el área de la Biomedicina, en la cual existen diversos sistemas de gestión de datos, como [9], capaces de generar grandes cantidades de datos anotados semánticamente. Para guiar el proceso de anotación, estos sistemas de gestión de datos adoptan ontologías de aplicación específicas que se apoyan en una o varias ontologías de dominio comúnmente aceptadas.…”

Section: Escenario De Aplicaciónunclassified

Populating Data Warehouses with Semantic Data

Nebot

Berlanga

2010

IEEE Latin Am. Trans.

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Resumen-Debido al auge de la Web Semántica cada vez existen más repositorios con grandes cantidades de datos semánticos (datos anotados con elementos de ontologías), con una estructura compleja, semi-estructurada y heterogénea. En este artículo se presenta un método para la construcción automática de tablas de hechos a partir de datos enriquecidos semánticamente y almacenados en forma de instancias RDF/OWL. Como punto de partida, el método toma un esquema multidimensional en forma de estrella (el tema de análisis, las dimensiones y medidas) diseñado por el analista a partir de los conceptos y propiedades de la ontología que describe el almacén de instancias. El método explota la semántica encapsulada en los axiomas de la ontología para derivar "combinaciones válidas" de instancias y literales y así construir hechos. Además, tanto la ontología como las instancias se indexan con unos índices específicos que proporcionan escalabilidad a la propuesta. El método ha sido evaluado con un conjunto de instancias OWL generadas de forma sintética.

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PEDRo: A database for storing, searching and disseminating experimental proteomics data

Cited by 61 publications

References 21 publications

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

Metabolomics, machine learning and modelling: towards an understanding of the language of cells

Universal Metrics for Quality Assessment of Protein Identifications by Mass Spectrometry

Populating Data Warehouses with Semantic Data

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