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    Protein Mass Spectrometry Sequencing Result Filtering

      Protein mass spectrometry is a vital tool for analyzing protein expression, modification, and interaction. However, protein mass spectrometry data typically contain a large amount of noise, challenging the protein identification accuracy. Hence, filtering mass spectrometry sequencing results is of great significance.

       

      Filtering Strategies

      1. False Positive Rate Control

      In protein mass spectrometry data analysis, the false discovery rate (FDR) is one of the most commonly used filtering criteria. The FDR refers to the proportion of results incorrectly deemed positive among all results considered positive. Generally, we can control the false positive rate by setting the FDR threshold (such as 1% or 5%).

       

      2. Protein Coverage

      Protein coverage refers to the proportion of identified protein fragments in the entire protein sequence. Generally, the higher the coverage, the more credible the identification. Therefore, we can set a filtering threshold based on coverage, such as only accepting proteins with coverage over 20%.

       

      3. Number of Primary Fragment Spectral Peak Matches

      The number of primary fragment spectral peak matches refers to the number of matched primary fragment peaks in protein mass spectrometry. This parameter is also widely used to filter spectrogram results because the more matches, the more credible the identification.

       

      Filtering Tools

      In practice, we can use various protein mass spectrometry data analysis software to filter results, such as MaxQuant, Proteome Discoverer, etc. These tools provide a wealth of filtering options, such as FDR threshold, coverage threshold, and the number of primary fragment spectral peak matches.

       

      In summary, filtering the results of protein mass spectrometry sequencing is a crucial step that can effectively improve the accuracy of protein identification. It should also be noted that the selection of filtering strategies should be based on the experimental purpose and data characteristics to achieve the best results.

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