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Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

ABSTRACT: LTQ Orbitrap data analyzed with ProteinPilot can be further improved by MaxQuant raw data processing, which utilizes precursor-level high mass accuracy data for peak processing and MGF creation. In particular, ProteinPilot results from MaxQuant-processed peaklists for Orbitrap data sets resulted in improved spectral utilization due to an improved peaklist quality with higher precision and high precursor mass accuracy (HPMA). The output and postsearch analysis tools of both workflows were utilized for previously unexplored features of a three-dimensional fractionated and hexapeptide library (ProteoMiner) treated whole saliva data set comprising 200 fractions. ProteinPilot's ability to simultaneously predict multiple modifications showed an advantage from ProteoMiner treatment for modified peptide identification. We demonstrate that complementary approaches in the analysis pipeline provide comprehensive results for the whole saliva data set acquired on an LTQ Orbitrap. Overall our results establish a workflow for improved protein identification from high mass accuracy data.

SUBMITTER: Jagtap P

PROVIDER: S-EPMC3618284 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

Jagtap Pratik P Bandhakavi Sricharan S Higgins LeeAnn L McGowan Thomas T Sa Rongxiao R Stone Matthew D MD Chilton John J Arriaga Edgar A EA Seymour Sean L SL Griffin Timothy J TJ

Proteomics 20120601 11

LTQ Orbitrap data analyzed with ProteinPilot can be further improved by MaxQuant raw data processing, which utilizes precursor-level high mass accuracy data for peak processing and MGF creation. In particular, ProteinPilot results from MaxQuant-processed peaklists for Orbitrap data sets resulted in improved spectral utilization due to an improved peaklist quality with higher precision and high precursor mass accuracy (HPMA). The output and postsearch analysis tools of both workflows were utilize ...[more]

PMID: 22623410

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Project description:High-frequency data streams of vital signs may be used to generate individualized hemodynamic targets for critically ill patients. Central to this precision medicine approach to resuscitation is our ability to screen these data streams for errors and artifacts. However, there is no consensus on the best method for data cleaning. Our goal was to determine whether an error-checking algorithm developed for intraoperative use could be applied to high volumes of arterial line data in an ICU population.DesignMulticenter observational study.SettingICUs across Ontario, Canada.PatientsNested cohort of ICU patients with shock and/or respiratory failure requiring invasive mechanical ventilation.InterventionsHigh-frequency blood pressure data was analyzed. Systolic, diastolic, and mean arterial pressure minute averages were calculated. For manual analysis, a trained researcher retrospectively reviewed mean arterial pressure data, removing values that were deemed nonphysiological. The algorithm was implemented and identified artifactual data.Measurements and main resultsArterial line data was extracted from 15 patients. A trained researcher manually reviewed 40,798 minute-by-minute data points, then subsequently analyzed them with the algorithm. Manual review resulted in the identification of 119 artifacts (0.29%). The optimized algorithm identified 116 (97%) of these artifacts. Five hundred thirty-seven data points were erroneously removed or modified. Compared with manual review, the modified algorithm incorporating absolute thresholds of greater than 30 and less than 200 mm Hg had 97.5% sensitivity, 98.7% specificity, and a Matthew correlation coefficient of 0.41.ConclusionsThe error-checking algorithm had high sensitivity and specificity in detecting arterial line blood pressure artifacts compared with manual data cleaning. Given the growing use of large datasets and machine learning in critical care research, methods to validate the quality of high-frequency data is important to optimize algorithm performance and prevent spurious associations based on artifactual data.

Dataset Information

Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

Publications

Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

Similar Datasets

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