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10:12
Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
Published on: October 28, 2021
An improvement of shotgun proteomics analysis by adding next-generation sequencing transcriptome data in orange
Jiaping Song1, Renjie Sun, Dazhi Li
1The Shenzhen Proteome Engineering Laboratory, BGI Shenzhen, Shenzhen, PR China.
Plos One
|July 7, 2012
Summary
This study introduces an improved shotgun proteomics workflow using an integrated database. This method enhances protein identification by 18.5%, overcoming limitations in species-specific protein sequence data.
Area of Science:
- Proteomics
- Bioinformatics
- Genomics
Background:
- Shotgun proteomics analysis typically relies on database searching.
- Existing protein sequence databases often lack comprehensive information for many species.
- This limitation poses a significant challenge for shotgun proteomics, particularly for non-sequenced genomes.
Purpose of the Study:
- To develop an improved shotgun proteomics workflow.
- To address the challenge of insufficient protein information in standard databases.
- To enhance protein identification rates, especially for species without sequenced genomes.
Main Methods:
- A workflow integrating a homologous species database with a custom transcriptome-derived protein database was developed.
- Transcriptome data was used to create a species-specific protein sequence database.
- This custom database was combined with a homologous species database for comprehensive searching.
Main Results:
- The integrated database approach significantly improved shotgun proteomics data analysis.
- Demonstrated on orange leaves samples, the workflow showed a marked advantage over using only homologous databases.
- An 18.5% increase in protein identification was achieved using the integrated database.
Conclusions:
- The developed workflow and integrated database effectively enhance protein identification in shotgun proteomics.
- This approach offers a valuable solution for studying protein sequence profiles in species with limited genomic data.
- The findings highlight the utility of combining transcriptome data with homologous databases for robust proteomic analysis.
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