A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

Corradi, Luca; Fato, Marco; Porro, Ivan; Scaglione, Silvia; Torterolo, Livia

doi:10.1186/1471-2105-9-480

Cited by 4 publications

(4 citation statements)

References 11 publications

(7 reference statements)

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“…Images are then encoded into numerical data by using proprietary tools that extract regions corresponding to probes and convert their pixel intensities into a numerical value. These files usually use a proprietary format defined by the microarray vendors and are not automatically readable by existing analysis platforms, thus the analysis of microarray data requires a preliminary preprocessing activity before the further analysis [ 3 - 7 ]. The preprocessing involves several phases, among those: denoising, background correction, normalization and summarization, i.e.…”

Section: Introductionmentioning

confidence: 99%

μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

Guzzi

Cannataro

2010

BMC Bioinformatics

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BackgroundA main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.ResultsThe paper presents μ-CS (Microarray Cel file Summarizer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix binary data. μ-CS is based on a client-server architecture. The μ-CS client is provided both as a plug-in of the TIGR M4 platform and as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data, avoiding the manual invocation of external tools (e.g. the Affymetrix Power Tools), the manual loading of preprocessing libraries, and the management of intermediate files. The μ-CS server automatically updates the references to the summarization and annotation libraries that are provided to the μ-CS client before the preprocessing. The μ-CS server is based on the web services technology and can be easily extended to support more microarray vendors (e.g. Illumina).ConclusionsThus μ-CS users can directly manage binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the μ-CS plugin for TM4 can manage Affymetrix binary files without using external tools, such as APT (Affymetrix Power Tools) and related libraries. Consequently, μ-CS offers four main advantages: (i) it avoids to waste time for searching the correct libraries, (ii) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or the use of old libraries, (iii) it implements the annotation of preprocessed data, and finally, (iv) it may enhance the quality of further analysis since it provides the most updated annotation libraries. The μ-CS client is freely available as a plugin of the TM4 platform as well as a standalone application at the project web site (http://bioingegneria.unicz.it/M-CS).

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Section: Introductionmentioning

confidence: 99%

μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

Guzzi

Cannataro

2010

BMC Bioinformatics

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“…Details of the Microarray analysis workflow are shown in Figure 5 . We store the raw data as .CEL files, and we performed two different microarray normalizations (MAS5 or RMA [ 39 , 40 ]) depending on needs. We also store reports about a set of outcome and prognostic feature predictors, developed at IGG, and based on two machine learning classifiers: (I) a multilayer perceptron neural network [ 41 ] and (II) a Logic Learning Machine (LLM) algorithm implemented with the RULEX software [ 42 ].…”

Section: Resultsmentioning

confidence: 99%

A digital repository with an extensible data model for biobanking and genomic analysis management

et al. 2014

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MotivationMolecular biology laboratories require extensive metadata to improve data collection and analysis. The heterogeneity of the collected metadata grows as research is evolving in to international multi-disciplinary collaborations and increasing data sharing among institutions. Single standardization is not feasible and it becomes crucial to develop digital repositories with flexible and extensible data models, as in the case of modern integrated biobanks management.ResultsWe developed a novel data model in JSON format to describe heterogeneous data in a generic biomedical science scenario. The model is built on two hierarchical entities: processes and events, roughly corresponding to research studies and analysis steps within a single study. A number of sequential events can be grouped in a process building up a hierarchical structure to track patient and sample history. Each event can produce new data. Data is described by a set of user-defined metadata, and may have one or more associated files. We integrated the model in a web based digital repository with a data grid storage to manage large data sets located in geographically distinct areas. We built a graphical interface that allows authorized users to define new data types dynamically, according to their requirements. Operators compose queries on metadata fields using a flexible search interface and run them on the database and on the grid. We applied the digital repository to the integrated management of samples, patients and medical history in the BIT-Gaslini biobank. The platform currently manages 1800 samples of over 900 patients. Microarray data from 150 analyses are stored on the grid storage and replicated on two physical resources for preservation. The system is equipped with data integration capabilities with other biobanks for worldwide information sharing.ConclusionsOur data model enables users to continuously define flexible, ad hoc, and loosely structured metadata, for information sharing in specific research projects and purposes. This approach can improve sensitively interdisciplinary research collaboration and allows to track patients' clinical records, sample management information, and genomic data. The web interface allows the operators to easily manage, query, and annotate the files, without dealing with the technicalities of the data grid.

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“…As the first step, microarray data are preprocessed (using background correction and normalization methods) and gene expression values are obtained by running a parallel version of dChip 7. dChip software is able to run on both cluster environments and distributed Grid infrastructures, like EGEE.…”

Section: Use Casesmentioning

confidence: 99%

Generating web‐based user interfaces for computational science

Hemert

Koetsier

Torterolo

et al. 2010

Concurrency and Computation

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Scientific gateways in the form of web portals are becoming the popular approach to share knowledge and resources around a topic in a community of researchers. Unfortunately, the development of web portals is expensive and requires specialists skills. Commercial and more generic web portals have a much larger user base and can afford this kind of development. Here we present two solutions that address this problem in the area of portals for scientific computing; both take the same approach. The whole process of designing, delivering and maintaining a portal can be made more cost-effective by generating a portal from a description rather than programming in the traditional sense. We show four successful use cases to show how this process works and the results it can deliver.

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A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

Cited by 4 publications

References 11 publications

μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

A digital repository with an extensible data model for biobanking and genomic analysis management

Generating web‐based user interfaces for computational science

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