ResourceLife Sciences
Analysis of Labeled and Non-Labeled Proteomic Data
19 Feb 2015Proteomics experiments can readily generate large, complex data sets, making the analysis and interpretation of results, rate-determining steps. This application note demonstrates the ability of Progenesis QI for proteomics to achieve qualitative and quantitative results for both labeled and label-free quantitation workflows. Data acquired by means of data-dependent acquisition and data-independent acquisition strategies show consistency and precision in their reported quantitation values, independent of the quantitation methodology adopted.
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ProteomicsProteomics is the systemic bioinformatics study of proteins and amino acids, including their structure, size, function and identification. Tools used in proteomics include chromatography, blotting and gels, protein arrays, mass spectrometry and ELISA and associated analysis software. Analyzers and proteomic systems should be sensitive, high resolution, fast and may be automated for high-throughput.Data AnalysisData analysis hardware and software is available to make data processing straight-forward yet powerful. Data software can be used for math and stats, technical graphing and image analysis. In addition, software is available for specific data analysis of electrophoresis, densitometry, ELISA and DNA sequencing.Data AnalysisThe analysis of data is the process of transforming, modeling and evaluating data to discover useful information from experimental results. Big DataBig data is described as data sets that are extremely large and complex, and can be challenging to process.