There exist some biomolecules in a vacuum whose unique nature can only be determined by some special techniques like mass spectrometry. Charged molecules are normally analyzed with their masses playing a major role in determining the nature of biomolecules present in a vacuum. Complete mass spectrometry consists of the source, drift tubes, and analyzers (Eng et al., 1994). It has been observed that the results from the mass spectrometry experiments differ because of the different spectra, sources and analyzers used.The difference in results also occurs as a result of the type of mass spectrometry components used in the experiment. Biological samples are analyzed using the large-scale mass spectroscopic proteomic analysis that normally results in large amounts of data that is sometimes difficult to manage especially in a downstream flow. Data processing and analyzing tools are needed in processing the hundreds of spectra produced in a single analysis. The data processing and analyzing tools are used to extract and interpret the masses of the charged molecules from the data output of that particular mass spectrometry (Eng et al., 1994). Database searches are then performed simultaneously to identify the peptides about the mass criteria of the detected masses from the mass spectrometry data output. The mass spectrometry technique heavily relies on these tools in processing and analyzing data. This paper will discuss why it is important to understand the manual analysis of the mass spectrometry data for one to correctly use the automated mass spectrometric database.
The process of identifying proteins relies on the algorithms specially developed to identify the type of protein in question. The algorithms are designed by correlating the measured peptide masses with the experimentally calculated protein masses. These proteins exist in sequence databases that are often used in the manual analysis of the mass spectrometry data (Ekman, 2009). Computational or automated data analysis can only be applied to interpret known proteins. The manual analysis technique needs to be thoroughly understood for proper application of the computational technique especially in the case of mixed proteins. The mass spectrometry data is determined using the protein sequence databases (Gentzel et al., 2003). The searches of translations of expressed sequence tags are somehow difficult to understand in case the computational data analysis technique is used. The mass spectrometry data is first of all processed out of the spectrometer followed by a thorough analysis of the parameters. The parameter is essential in the process of identifying the real identity of the protein in question. The manual data analysis technique is very much needed in this process and simplifies the analysis of peptides using the computational data search technique. The multiple criteria provided in the mass spectrometry tag tool play a major in narrowing down the search results (Mann & Pandey, 2001). Manual data analysis minimizes the probability of errors occurring from the computational data analysis technique. After manual analysis of data, it becomes very simple to set the necessary parameters for computational analysis (Pennington & Dunn, 2001).
There are quite a number of reasons why manual data analysis techniques should be thoroughly understood for the correct use of the automated mass spectrometric database. To begin with, the peaks that have similar charges are identified and processed together manually before the data is aggregated for computational analysis. Post-translational modifications which form an important parameter are traced from the peptides (Sodygov et al., 2004). This parameter is very important when analyzing data through the designed algorithm. Before processing the data through the automated search databases, it is necessary to determine the values of common contaminants manually. There is always a high possibility of errors when the data is analyzed without deleting the values of common contaminants. Manual data analysis identifies all possible contaminants before automatic processing. Before being processed through the automatic data analyzers, the data is manually interpreted for common splice variants for proper understanding of the peptide homolog (Williams & Burinsky, 2001). The splice variants are normally present in the stratum of the organism. Manual data analysis is essential in cases where the designed algorithms rank values according to the results in high or low peaks. The manual technique of data analysis helps a great deal in the accurate identification of proteins.
In conclusion, the manually developed datasheets from the mass spectrometer are very essential in setting parameters for the automated systems. These data sheets provide the necessary basic information necessary for the success of automatic searches. The type of charges present on the peptides and the type of mass spectrometry used are some of the basic information from mass spectrometry datasheets (Ekman, 2009). Scores of contaminants are normally deleted manually in cases where the exact values are known. All the parameters are very important in the process of determining the sequence of the peptide under investigation. Manual data analysis is very essential because it helps in reducing the automatically generated results. Automated protein search databases require pre-automation manual analysis for precise prediction of the sequence of proteins (Ekman, 2009). The parameters obtained from the experimental data are used in deciding whether the theoretical data fit with experimental data. Automated search databases rely heavily on these parameters to produce precise results. Manual data analysis is very essential in minimizing the number of errors present in the final results produced by the automatic method. It is thus necessary to have a proper understanding of the manual method of data analysis to achieve precise results using the automated mass spectrometry database.
References
Ekman, R. (2009). Mass spectrometry: Instrumentation, interpretation, and application. New York, NY: John, Wiley and Sons.
Eng, J.K. et al. (1994). An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom, 5, 976-989.
Gentzel, M. et al. (2003). Preprocessing of tandem mass spectrometric data to support automatic protein identification. Proteomics, 3, 1597-1610.
Mann, M. & Pandey, A. (2001). Use of mass spectrometry-derived data to annotate nucleotide and protein sequence databases. TRENDS in Biochemical Science, 26, (1), 54-61.
Pennington, S.R. & Dunn, M.J. (2001). Proteomics from protein sequence to function. New York, NY: BIOS Scientific Publishers.
Sodygov, R.G. et al. (2004). Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book. Nature Methods, 1, (3), 195-202.
Williams, J.D. & Burinsky, D.J. (2001). Mass spectrometric analysis of complex mixtures then and now: the impact of linking liquid chromatography and mass spectrometry. International Journal of Mass Spectrometry, 201, 111-133.