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Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells
We have recently developed a novel method for the affinity purification of the complete suite of translating mRNA from genetically labeled cell populations. This method permits comprehensive quantitative comparisons of the genes employed by each specific cell type. We provide a detailed description...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910036/ https://www.ncbi.nlm.nih.gov/pubmed/20308160 http://dx.doi.org/10.1093/nar/gkq130 |
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author | Dougherty, Joseph D. Schmidt, Eric F. Nakajima, Miho Heintz, Nathaniel |
author_facet | Dougherty, Joseph D. Schmidt, Eric F. Nakajima, Miho Heintz, Nathaniel |
author_sort | Dougherty, Joseph D. |
collection | PubMed |
description | We have recently developed a novel method for the affinity purification of the complete suite of translating mRNA from genetically labeled cell populations. This method permits comprehensive quantitative comparisons of the genes employed by each specific cell type. We provide a detailed description of tools for analysis of data generated with this and related methodologies. An essential question that arises from these data is how to identify those genes that are enriched in each cell type relative to all others. Genes relatively specifically employed by a cell type may contribute to the unique functions of that cell, and thus may become useful targets for development of pharmacological tools for cell-specific manipulations. We describe here a novel statistic, the specificity index, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles. This measure correctly predicts in situ hybridization patterns for many cell types. We apply this measure to a large survey of CNS cell-specific microarray data to identify those genes that are significantly enriched in each population Data and algorithms are available online (www.bactrap.org). |
format | Text |
id | pubmed-2910036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-29100362010-07-27 Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells Dougherty, Joseph D. Schmidt, Eric F. Nakajima, Miho Heintz, Nathaniel Nucleic Acids Res Computational Biology We have recently developed a novel method for the affinity purification of the complete suite of translating mRNA from genetically labeled cell populations. This method permits comprehensive quantitative comparisons of the genes employed by each specific cell type. We provide a detailed description of tools for analysis of data generated with this and related methodologies. An essential question that arises from these data is how to identify those genes that are enriched in each cell type relative to all others. Genes relatively specifically employed by a cell type may contribute to the unique functions of that cell, and thus may become useful targets for development of pharmacological tools for cell-specific manipulations. We describe here a novel statistic, the specificity index, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles. This measure correctly predicts in situ hybridization patterns for many cell types. We apply this measure to a large survey of CNS cell-specific microarray data to identify those genes that are significantly enriched in each population Data and algorithms are available online (www.bactrap.org). Oxford University Press 2010-07 2010-03-22 /pmc/articles/PMC2910036/ /pubmed/20308160 http://dx.doi.org/10.1093/nar/gkq130 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Dougherty, Joseph D. Schmidt, Eric F. Nakajima, Miho Heintz, Nathaniel Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title | Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title_full | Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title_fullStr | Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title_full_unstemmed | Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title_short | Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells |
title_sort | analytical approaches to rna profiling data for the identification of genes enriched in specific cells |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910036/ https://www.ncbi.nlm.nih.gov/pubmed/20308160 http://dx.doi.org/10.1093/nar/gkq130 |
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