Network‐assisted protein identification and data interpretation in shotgun proteomics
Abstract Protein assembly and biological interpretation of the assembled protein lists are critical steps in shotgun proteomics data analysis. Although most biological functions arise from interactions among proteins, current protein assembly pipelines treat proteins as independent entities. Usually...
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| Format: | Article |
| Language: | English |
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Springer Nature
2009-08-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/msb.2009.54 |
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| _version_ | 1849225738963648512 |
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| author | Jing Li Lisa J Zimmerman Byung‐Hoon Park David L Tabb Daniel C Liebler Bing Zhang |
| author_facet | Jing Li Lisa J Zimmerman Byung‐Hoon Park David L Tabb Daniel C Liebler Bing Zhang |
| author_sort | Jing Li |
| collection | DOAJ |
| description | Abstract Protein assembly and biological interpretation of the assembled protein lists are critical steps in shotgun proteomics data analysis. Although most biological functions arise from interactions among proteins, current protein assembly pipelines treat proteins as independent entities. Usually, only individual proteins with strong experimental evidence, that is, confident proteins, are reported, whereas many possible proteins of biological interest are eliminated. We have developed a clique‐enrichment approach (CEA) to rescue eliminated proteins by incorporating the relationship among proteins as embedded in a protein interaction network. In several data sets tested, CEA increased protein identification by 8–23% with an estimated accuracy of 85%. Rescued proteins were supported by existing literature or transcriptome profiling studies at similar levels as confident proteins and at a significantly higher level than abandoned ones. Applying CEA on a breast cancer data set, rescued proteins coded by well‐known breast cancer genes. In addition, CEA generated a network view of the proteins and helped show the modular organization of proteins that may underpin the molecular mechanisms of the disease. |
| format | Article |
| id | doaj-art-005d5917d4eb49008cfa75f73fb68213 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2009-08-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-005d5917d4eb49008cfa75f73fb682132025-08-24T11:59:11ZengSpringer NatureMolecular Systems Biology1744-42922009-08-015111110.1038/msb.2009.54Network‐assisted protein identification and data interpretation in shotgun proteomicsJing Li0Lisa J Zimmerman1Byung‐Hoon Park2David L Tabb3Daniel C Liebler4Bing Zhang5Department of Biomedical Informatics, Vanderbilt University School of MedicineDepartment of Biochemistry, Vanderbilt University School of MedicineOak Ridge National LaboratoryDepartment of Biomedical Informatics, Vanderbilt University School of MedicineDepartment of Biomedical Informatics, Vanderbilt University School of MedicineDepartment of Biomedical Informatics, Vanderbilt University School of MedicineAbstract Protein assembly and biological interpretation of the assembled protein lists are critical steps in shotgun proteomics data analysis. Although most biological functions arise from interactions among proteins, current protein assembly pipelines treat proteins as independent entities. Usually, only individual proteins with strong experimental evidence, that is, confident proteins, are reported, whereas many possible proteins of biological interest are eliminated. We have developed a clique‐enrichment approach (CEA) to rescue eliminated proteins by incorporating the relationship among proteins as embedded in a protein interaction network. In several data sets tested, CEA increased protein identification by 8–23% with an estimated accuracy of 85%. Rescued proteins were supported by existing literature or transcriptome profiling studies at similar levels as confident proteins and at a significantly higher level than abandoned ones. Applying CEA on a breast cancer data set, rescued proteins coded by well‐known breast cancer genes. In addition, CEA generated a network view of the proteins and helped show the modular organization of proteins that may underpin the molecular mechanisms of the disease.https://doi.org/10.1038/msb.2009.54cliquedata interpretationprotein identificationprotein interaction networkshotgun proteomics |
| spellingShingle | Jing Li Lisa J Zimmerman Byung‐Hoon Park David L Tabb Daniel C Liebler Bing Zhang Network‐assisted protein identification and data interpretation in shotgun proteomics Molecular Systems Biology clique data interpretation protein identification protein interaction network shotgun proteomics |
| title | Network‐assisted protein identification and data interpretation in shotgun proteomics |
| title_full | Network‐assisted protein identification and data interpretation in shotgun proteomics |
| title_fullStr | Network‐assisted protein identification and data interpretation in shotgun proteomics |
| title_full_unstemmed | Network‐assisted protein identification and data interpretation in shotgun proteomics |
| title_short | Network‐assisted protein identification and data interpretation in shotgun proteomics |
| title_sort | network assisted protein identification and data interpretation in shotgun proteomics |
| topic | clique data interpretation protein identification protein interaction network shotgun proteomics |
| url | https://doi.org/10.1038/msb.2009.54 |
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