Dataset Information

Dinosaur: refined open source peptide MS feature detector

ABSTRACT: In order to test the usability of the new MS feature detection algorithm Dinosaur, 8 samples were analyzed. These were selected to represent different proteomics experiments and cover the full range of sample different complexities, from synthetic peptides to whole tissue lysate. This project evolves around the development of a new MS1 feature detection tool, and the files were only used for MS1 feature detection and analysis. Therefore, no identification files exist and there is no scientific interest in generating these.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Streptococcus Pyogenes Serotype M1 (strain Atcc Baa-947 / Mgas5005) Mus Musculus (mouse)

TISSUE(S): Liver

SUBMITTER: Johan Teleman

LAB HEAD: Johan Malmström

PROVIDER: PXD003405 | Pride | 2016-06-08

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	Anahita_S1411_240.features.csv	Csv
	Anahita_S1411_240.mzML.gz	Mzml
	Anahita_S1411_240.qc.zip	Other
	Erik_S1407_238.features.csv	Csv
	Erik_S1407_238.mzML.gz	Mzml

Items per page:

1 - 5 of 24

Publications

Dinosaur: A Refined Open-Source Peptide MS Feature Detector.

Teleman Johan J Chawade Aakash A Sandin Marianne M Levander Fredrik F Malmström Johan J

Journal of proteome research 20160608 7

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detectio ...[more]

PMID: 27224449

Similar Datasets

Project description:Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.