A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

MicroGen: a MIAME compliant web system for microarray experiment information and workflow management

BACKGROUND: Improvements of bio-nano-technologies and biomolecular techniques have led to increasing production of high-throughput experimental data. Spotted cDNA microarray is one of the most diffuse technologies, used in single research laboratories and in biotechnology service facilities. Although they are routinely performed, spotted microarray experiments are complex procedures entailing several experimental steps and actors with different technical skills and roles. During an experiment, involved actors, who can also be located in a distance, need to access and share specific experiment information according to their roles. Furthermore, complete information describing all experimental steps must be orderly collected to allow subsequent correct interpretation of experimental results. RESULTS: We developed MicroGen, a web system for managing information and workflow in the production pipeline of spotted microarray experiments. It is constituted of a core multi-database system able to store all data completely characterizing different spotted microarray experiments according to the Minimum Information About Microarray Experiments (MIAME) standard, and of an intuitive and user-friendly web interface able to support the collaborative work required among multidisciplinary actors and roles involved in spotted microarray experiment production. MicroGen supports six types of user roles: the researcher who designs and requests the experiment, the spotting operator, the hybridisation operator, the image processing operator, the system administrator, and the generic public user who can access the unrestricted part of the system to get information about MicroGen services. CONCLUSION: MicroGen represents a MIAME compliant information system that enables managing workflow and supporting collaborative work in spotted microarray experiment production

SaDA: From Sampling to Data Analysis—An Extensible Open Source Infrastructure for Rapid, Robust and Automated Management and Analysis of Modern Ecological High-Throughput Microarray Data

One of the most crucial characteristics of day-to-day laboratory information management is the collection, storage and retrieval of information about research subjects and environmental or biomedical samples. An efficient link between sample data and experimental results is absolutely important for the successful outcome of a collaborative project. Currently available software solutions are largely limited to large scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but most of the times this requires a sufficient investment of money, time and technical efforts. There is a clear need for a light weighted open source system which can easily be managed on local servers and handled by individual researchers. Here we present a software named SaDA for storing, retrieving and analyzing data originated from microorganism monitoring experiments. SaDA is fully integrated in the management of environmental samples, oligonucleotide sequences, microarray data and the subsequent downstream analysis procedures. It is simple and generic software, and can be extended and customized for various environmental and biomedical studies

Using Robust Rank Aggregation for Prioritising Autoimmune Targets on Protein Microarrays

Autoimmuunhaigused on tänapäeva maailmas väga sagedased. Üha enam ja enam haigusi on seotud autoimmuunsete protsessidega. Autoimmuunreaktsioon on protsess, mille käigus immuunsüsteem toodab antikehasid (autoantikehad) organismi enda rakkude vastu. Autoimmuunhaiguste põhjused ja mehhanismid on aga veel selgeks tegemata. Üheks võimaluseks, kuidas autoimmuunhaigusi õppida on välja selgitada, miks kindlad rakud ja iseäranis just valgud on autoantikehade märklauaks. Selle eesmärgi saavutamiseks on välja töötatud mitmesuguseid tehnoloogiaid, kuhu kuuluvad ka valgukiibid. See tehnoloogia võimaldab hinnata autoantikehade kogust patsiendi seerumis 9000 unikaalse inimese valgu vastu. Seega, rakendades andmeanalüüsi meetodeid on bioinformaatikud võimelised tuvastama autoantikehade märklaudvalke. Teades neid valke, saavad bioloogid läbi viia edasisi katseid ning formuleerida uusi hüpoteese autoimmuunhaiguste mehhanismide ja esinemise kohta. Traditsioonilised andmeanalüüsi meetodid keskenduvad ainult selliste valkude leidmisele, mis erinevad kõige kindlamalt tervete ja patsientide grupi vahel. Need meetodid aga jätavad kõrvale fakti, et märklaudvalkude repertuaar võib patsientide vahel oluliselt erineda. Seega võib isegi üksikjuhtum sisaldada olulist informatsiooni haiguse mehhanismide mõistmisel. Käesolevas lõputöös pakume välja, et Robust Rank Aggregation (RRA) algoritmi saab kasutada adaptiivse meetodina leidmaks reaktiivsete valkude (märklaudvalkude) laia repertuaari. Me võrdlesime klassikaliste analüüsimeetodite otstarbekust ja efektiivsust RRA-ga nii sünteetilistel kui ka pärisandmetel. Katsed sünteetilise andmehulgaga ehk andmehulgaga, mille puhul on reaktiivsed valgud teada näitavad, et RRA ületab teisi meetodeid olles samal ajal vähem mõjutatud “mürast”. Rakendades RRA-d pärisandmetel ning viies läbi rikastusanalüüsi iga meetodi kohta saadud reaktiivsete valkude listidega, saime me sarnase arvu valke, mis olid bioloogilise ja immuunvastusega seotud klassides üleesindatud.Autoimmune diseases are very common in the modern world. More and more diseases associated with an autoimmune process. Autoimmune reaction is a process in which the immune system produces antibodies (autoantibodies) that attack organism’s own cells. Causes and mechanisms of autoimmune diseases are yet to be understood. One of the ways to study autoimmunity is to explore reasons why certain cells and particularly proteins were attacked by autoantibodies. To achieve this, many technologies have been developed and one of which is Protein microarray. This technology allows estimating the amount of autoantibodies in patient serum against 9000 unique human proteins. Consequently, applying methods of data analysis on this data, bioinformaticians might be able to identify proteins that attract prevalent amount of autoantibodies. Knowing these proteins, biologists could conduct experiments and formulate new hypotheses about mechanisms of work and appearance of autoimmune diseases. Common data analysis methods focused on how to select only the most reliably differing proteins between healthy and diseased groups. Moreover, ignoring the fact that in the case of an autoimmune disease - the repertoire of the affected proteins can differ greatly between patients. So even single cases of high protein reactivity may carry important information for understanding the mechanisms of disease. In this thesis, we propose to apply Robust Rank Aggregation algorithm as an adaptive method to identify a wide repertoire of reactive proteins. We compared expediency and effectiveness of the classical methods of analysis, method recently applied by biologists and RRA on synthetic and real data. Experiments on synthetic data sets with known reactive proteins show that RRA outperforms these methods while also being more robust to incorporated noise. Applying RRA on real data and conducting an enrichment analysis on lists of reactive proteins for each method, we got comparable numbers of proteins overrepresented in the classes associated with biological and immune responses

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

<p>Abstract</p> <p>Background</p> <p>A 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.</p> <p>Results</p> <p>The paper presents <it>μ</it>-CS (Microarray Cel file Summarizer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix binary data. <it>μ</it>-CS is based on a client-server architecture. The <it>μ</it>-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 <it>μ</it>-CS server automatically updates the references to the summarization and annotation libraries that are provided to the <it>μ</it>-CS client before the preprocessing. The <it>μ</it>-CS server is based on the web services technology and can be easily extended to support more microarray vendors (e.g. Illumina).</p> <p>Conclusions</p> <p>Thus <it>μ</it>-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 <it>μ</it>-CS plugin for TM4 can manage Affymetrix binary files without using external tools, such as APT (Affymetrix Power Tools) and related libraries. Consequently, <it>μ</it>-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 <it>μ</it>-CS client is freely available as a plugin of the TM4 platform as well as a standalone application at the project web site (<url>http://bioingegneria.unicz.it/M-CS</url>).</p