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      Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This study introduces a novel method for standardized relative quantification of colorimetric in situ hybridization signal that enables a large-scale cross-platform expression level comparison of in situ hybridization with two publicly available microarray brain data sources.

          Abstract

          With the emergence of genome-wide colorimetric in situ hybridization (ISH) data sets such as the Allen Brain Atlas, it is important to understand the relationship between this gene expression modality and those derived from more quantitative based technologies. This study introduces a novel method for standardized relative quantification of colorimetric ISH signal that enables a large-scale cross-platform expression level comparison of ISH with two publicly available microarray brain data sources.

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          A gene expression atlas of the central nervous system based on bacterial artificial chromosomes.

          Shiaoching Gong, Chen Zheng, Martin Doughty … (2003)
          The mammalian central nervous system (CNS) contains a remarkable array of neural cells, each with a complex pattern of connections that together generate perceptions and higher brain functions. Here we describe a large-scale screen to create an atlas of CNS gene expression at the cellular level, and to provide a library of verified bacterial artificial chromosome (BAC) vectors and transgenic mouse lines that offer experimental access to CNS regions, cell classes and pathways. We illustrate the use of this atlas to derive novel insights into gene function in neural cells, and into principal steps of CNS development. The atlas, library of BAC vectors and BAC transgenic mice generated in this screen provide a rich resource that allows a broad array of investigations not previously available to the neuroscience community.
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            Serial analysis of gene expression.

            V. E. Velculescu, L. Zhang, B Vogelstein … (1995)
            The characteristics of an organism are determined by the genes expressed within it. A method was developed, called serial analysis of gene expression (SAGE), that allows the quantitative and simultaneous analysis of a large number of transcripts. To demonstrate this strategy, short diagnostic sequence tags were isolated from pancreas, concatenated, and cloned. Manual sequencing of 1000 tags revealed a gene expression pattern characteristic of pancreatic function. New pancreatic transcripts corresponding to novel tags were identified. SAGE should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states.
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              Multiple-laboratory comparison of microarray platforms.

              Rafael A. Irizarry, Daniel P Warren, Forrest Spencer … (2005)
              Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2008
                Publication date (Electronic): 30 January 2008
                Volume: 9
                Issue: 1
                Page: R23
                Affiliations
                [1 ]Allen Institute for Brain Science, N. 34th Street, Seattle, WA 98103, USA
                Article
                Publisher ID: gb-2008-9-1-r23
                DOI: 10.1186/gb-2008-9-1-r23
                PMC ID: 2395252
                PubMed ID: 18234097
                SO-VID: 3bf3b492-5e24-42d0-945c-6aca2b829acb
                Copyright © Copyright © 2008 Lee et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 12 November 2007
                Date revision received : 21 December 2007
                Date accepted : 30 January 2008
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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