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Implements methods for calibration and filtering of masses. mscalib is a BioConductor package that purposes two novel mass spectrometry (MS) calibration methods. The first method utilises the local similarity of peptide maps generated after separation of complex protein samples by two-dimensional gel electrophoresis. It computes a multiple peak-list alignment of the data set using a modified Minimum Spanning Tree (MST) algorithm. The second method exploits the idea that hundreds of MS samples are measured in parallel on one sample support. It improves the calibration coefficients by applying a two-dimensional Thin Plate Splines (TPS) smoothing algorithm.
Reduces mass measurement errors for parent ions of tandem MS/MS data by modeling systematic errors based on putative peptide identifications. This information is used to subtract out errors from parent ion protonated masses. DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses.
Performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. It is similar to a sequential window acquisition of all theoretical fragment-ion spectra-mass spectrometry (MS) processing workflow. The tool could be adapted to solve the missing value problem in data-independent acquisition analysis, e.g. by propagating identities of peptide fragments instead of precursors.
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