5/18/2023 0 Comments Tmt label searchguiAmong several quantitation techniques, isobaric labeling-based quantitative proteomics has gained increasing attention because of its multiplexed capability, allowing for absolute and relative protein quantitation in multiple samples within a single run 2, 3. Mass spectrometry-based proteomics has become a dominating approach for identification and quantitation of proteins from complex biological samples 1. Multi-Q 2 executable files, sample data sets, and user manual are freely available at. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies.
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