However, there are no quantitative tools which combine quantification and functional analysis for complex proteomes and support label-free algorithms compatible with multiple MS spectral features. In conclusion, the abovementioned tools have gained recognition due to their distinctive accuracy and utility. It uses correlation analysis and graph theory detects peaks, isotope clusters, and stable amino acid isotope-labeled peptide pairs as three-dimensional objects in, elution time, and signal intensity space to quantify for proteomics. MaxQuant supporting labeling technique as well as label-free quantification is a quantitative proteomics software package designed for analyzing large mass-spectrometric data. uses a modified spectral counting method which utilizes machine learning technique to arrive at protein abundance values with improved accuracy over traditional spectral counting techniques. Besides, the APEX Tool described by Lu et al. Census supports multiple input formats and is extensively applied in quantitative proteomics. Census is compatible with many labeling strategies as well as with label-free analysis, single-stage mass spectrometry and tandem mass spectrometry (MS/MS) scans, and high- and low-resolution mass spectrometry data. John Yates’ group build a quantitative analysis tool for mass spectrometry-based proteomics called Census. Nowadays, plenty of tools have paved the road to a new era in quantitative proteomics. To propel this brand new analytic mode in proteomics science, innovative design of software tool is desiderated. On the other hand, an increasingly request of studying the complex proteome is to combine quantification and functional analysis together to reveal the unexplored mechanism in the cells. In this sense, selecting appropriate and valid MS spectral features is the key issue for MS-based quantification of complex proteome. Besides, the use of only spectral counts is not able to differentiate the MS spectra with different ion intensity, which leads to the systematic errors of quantification, especially for the low-abundant peptides. However, the NSAF method did not consider the shared peptides generated during the MS experiment.
This method uses protein length to normalize spectral count (SC) for improving the accuracy. One of the representative methods of label-free quantification is Normalized Spectral Abundance Factor (NSAF) which was firstly proposed by Florens et al. Specially, label-free quantitative approach based on spectral count has been widely used because of its ability to quantify large-scale proteomes. Over the past decade, mass spectrometry (MS) has been recognized as one of the most important techniques for proteomics science. Since the complex proteomes consist of a number of proteins with diversified functions, they pose a challenge for quantitative proteomics analysis. In the cell, the development of disease is expressed by the changes of protein abundance thus quantitative analysis for complex proteomes has increasingly become a critical way to investigate the mechanism of disease. Study of complex proteomes has been widely applied in biomarker discovery, signaling pathway, and drug design. The evaluation showed that the quantitative algorithms implemented in freeQuant can improve accuracy of quantification with better dynamic range. Mitochondrial proteomes from the mouse heart, the mouse liver, and the human heart were used to evaluate the usability and performance of freeQuant. Furthermore, freeQuant supports the large-scale functional annotations for complex proteomes. For proteins with low abundance, MS/MS total ion count coupled with spectral count is included to ensure accurate protein quantification.
It adopts spectral count for quantitative analysis and builds a new method for shared peptides to accurately evaluate abundance of isoforms.
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freeQuant supports label-free quantitative analysis which makes full use of tandem mass spectrometry (MS/MS) spectral count, protein sequence length, shared peptides, and ion intensity. freeQuant consists of two well-integrated modules: label-free quantification and functional analysis with biomedical knowledge. In this paper, we present a mass spectrometry label-free quantification tool for complex proteomes, called freeQuant, which integrated quantification with functional analysis effectively. Study of complex proteome brings forward higher request for the quantification method using mass spectrometry technology.