Historically, label-free samples were measured using data-dependent acquisition (DDA). Due to their simplicity and cost-effectiveness, label-free approaches have been used for decades. To identify such proteins, modern mass spectrometry-based proteomics techniques offer many ways to quantify and compare proteins between samples 3.
Many studies, e.g., in the clinical context, focus on the detection of differentially abundant proteins, preferably on a proteome-wide scale 1, 2. Often, the term ‘proteomics’ is specifically used to refer to large-scale studies of the proteome employing liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS). While the genome of an organism is geared towards remaining static for almost every cell, the dynamics introduced by the proteome, including differential expression, altered activity, and modifications of proteins, allow cells, tissues and even the whole organism to undergo dramatic changes and to carry out a plethora of different functions. Proteomics denotes the study of the entire set of proteins produced by an organism under defined conditions. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists.
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We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins.
We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking.
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