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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
Published on: May 22, 2018
Identifier mapping performance for integrating transcriptomics and proteomics experimental results.
Roger S Day1, Kevin K McDade, Uma R Chandran
1Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA. day01@pitt.edu
BMC Bioinformatics
|May 31, 2011
Summary
Comparing proteomic and transcriptomic data requires accurate identifier mapping. Three online tools showed significant discrepancies, highlighting the need for careful selection in omics data integration.
Area of Science:
- Biochemistry and Molecular Biology
- Bioinformatics
- Genomics and Proteomics
Background:
- Integrating transcriptomic and proteomic data offers deeper insights into gene-regulatory relationships.
- Standardization of identifier nomenclature is a significant challenge in multi-omics data integration.
- Accurate mapping of identifiers is crucial for reliable analysis of high-throughput biological data.
Purpose of the Study:
- To compare the performance of three freely available online tools for mapping UniProt accessions to Affymetrix probe set IDs.
- To evaluate the reliability of identifier mapping resources using proteomic and transcriptomic correlation analysis.
- To provide guidance for selecting appropriate identifier mapping strategies in omics data merging.
Main Methods:
- Comparison of DAVID, EnVision, and NetAffx for mapping UniProt accessions to Affymetrix probe set IDs.
- Liquid chromatography-tandem mass spectrometry was used to generate 11,879 distinct UniProt accessions from cancer and non-cancer samples.
- Proteome-transcriptome correlation analysis was performed to assess the quality of identifier mapping.
Main Results:
- Significant discrepancies were observed among the three identifier mapping resources.
- Proteome-transcriptome correlation analysis revealed varying performance levels for the mapping tools.
- The overall performance of the mapping resources remained consistent over a two-year period despite updates.
Conclusions:
- The presented methods aid in selecting optimal identifier mapping strategies for omics data integration.
- Context-specific insights can be gained by critically evaluating different mapping resources.
- Informed decisions regarding data merging are facilitated by understanding the performance of identifier mapping tools.
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