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Lists2Networks: Integrated analysis of gene/protein lists
BACKGROUND: Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it i...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2843617/ https://www.ncbi.nlm.nih.gov/pubmed/20152038 http://dx.doi.org/10.1186/1471-2105-11-87 |
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author | Lachmann, Alexander Ma'ayan, Avi |
author_facet | Lachmann, Alexander Ma'ayan, Avi |
author_sort | Lachmann, Alexander |
collection | PubMed |
description | BACKGROUND: Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. RESULTS: We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. CONCLUSIONS: Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org. |
format | Text |
id | pubmed-2843617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28436172010-03-23 Lists2Networks: Integrated analysis of gene/protein lists Lachmann, Alexander Ma'ayan, Avi BMC Bioinformatics Software BACKGROUND: Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. RESULTS: We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. CONCLUSIONS: Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org. BioMed Central 2010-02-12 /pmc/articles/PMC2843617/ /pubmed/20152038 http://dx.doi.org/10.1186/1471-2105-11-87 Text en Copyright ©2010 Lachmann and Ma'ayan; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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. |
spellingShingle | Software Lachmann, Alexander Ma'ayan, Avi Lists2Networks: Integrated analysis of gene/protein lists |
title | Lists2Networks: Integrated analysis of gene/protein lists |
title_full | Lists2Networks: Integrated analysis of gene/protein lists |
title_fullStr | Lists2Networks: Integrated analysis of gene/protein lists |
title_full_unstemmed | Lists2Networks: Integrated analysis of gene/protein lists |
title_short | Lists2Networks: Integrated analysis of gene/protein lists |
title_sort | lists2networks: integrated analysis of gene/protein lists |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2843617/ https://www.ncbi.nlm.nih.gov/pubmed/20152038 http://dx.doi.org/10.1186/1471-2105-11-87 |
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