Some excellent reviews on existing software are available, such as. Both commercial and nonprofit solutions exist. The label-free proteomic software workflows can be divided into two categories based on their structure: modular workflows and non-modular complete workflows. Label-free proteomics software workflows typically consist of multiple steps, including peptide peak picking, peptide identification, feature finding, matching of the features with peptide identities, alignment of the features between different samples and possibly aggregation of the identified and quantified peptides into protein quantifications. After the actual MS measurements, the raw data need to be processed appropriately for accurate results. Therefore, in this study, we concentrate on relative quantification using peptide peak intensities. It has been shown that relative quantification by peptide peak intensities provides more accurate relative quantities than spectral counting if the protein concentrations are high or if the sample complexity changes drastically between samples. Two main strategies for relative quantification by label-free methods exist: peak intensity-based methods and spectral counting. In recent years, data-independent acquisition (DIA) shotgun proteomics has emerged as a possible solution to the low reproducibility of the traditional data-dependent acquisition (DDA) shotgun proteomics. So far, the shotgun method has been more popular than the targeted method. Label-free methods can be used both in shotgun (discovery analysis of the whole proteome) and in targeted (analysis of a specified set of proteins) proteomics experiments and can be applied even when the metabolic labeling of samples is not possible. For example, SILAC requires the use of live cell cultures, and compared with label-free methods, most of the label-based methods require more steps in sample preparation and higher sample concentration, are more expensive and can only be performed for a limited number of samples. Although label-based quantification methods, such as SILAC (Stable isotope labeling with amino acids in cell culture), provide undisputable accuracy and robustness, they also have their limitations when compared with the more simple label-free methods. MS technologies can be coarsely divided into two categories: label-based and label-free quantification methods. biology, biochemistry and medicine) and is expected to further evolve with regard to resolution, speed and cost-efficiency. MS-powered quantitative proteomics has emerged into an important tool applied in various biosciences (e.g. High-resolution MS enables modern-day proteomics to identify and quantify tens of thousands of peptides and thousands of proteins in a single run. Mass spectrometry (MS)-based proteomics has developed rapidly during the recent decades. Proteomics, software workflow, differential expression, logarithmic fold change, imputation, evaluation Introduction Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences.
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