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An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange
BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challeng...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387166/ https://www.ncbi.nlm.nih.gov/pubmed/22768084 http://dx.doi.org/10.1371/journal.pone.0039494 |
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author | Song, Jiaping Sun, Renjie Li, Dazhi Tan, Fengji Li, Xin Jiang, Pingping Huang, Xinjie Lin, Liang Deng, Ziniu Zhang, Yong |
author_facet | Song, Jiaping Sun, Renjie Li, Dazhi Tan, Fengji Li, Xin Jiang, Pingping Huang, Xinjie Lin, Liang Deng, Ziniu Zhang, Yong |
author_sort | Song, Jiaping |
collection | PubMed |
description | BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification. |
format | Online Article Text |
id | pubmed-3387166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33871662012-07-05 An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange Song, Jiaping Sun, Renjie Li, Dazhi Tan, Fengji Li, Xin Jiang, Pingping Huang, Xinjie Lin, Liang Deng, Ziniu Zhang, Yong PLoS One Research Article BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification. Public Library of Science 2012-06-29 /pmc/articles/PMC3387166/ /pubmed/22768084 http://dx.doi.org/10.1371/journal.pone.0039494 Text en Song et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Song, Jiaping Sun, Renjie Li, Dazhi Tan, Fengji Li, Xin Jiang, Pingping Huang, Xinjie Lin, Liang Deng, Ziniu Zhang, Yong An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title | An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title_full | An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title_fullStr | An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title_full_unstemmed | An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title_short | An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange |
title_sort | improvement of shotgun proteomics analysis by adding next-generation sequencing transcriptome data in orange |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387166/ https://www.ncbi.nlm.nih.gov/pubmed/22768084 http://dx.doi.org/10.1371/journal.pone.0039494 |
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