Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

QC mix; quality control mix of 6 compounds trial3

ABSTRACT: This is another test of the quality control text mix. This is not really useful for anybody but me.

INSTRUMENT(S): impact II

ORGANISM(S): No Species

SUBMITTER: Chambers Hughes

PROVIDER: MSV000092593 | MassIVE | Fri Aug 04 11:17:00 BST 2023

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

QC mix; quality control mix of 6 compounds trial2

Project description:This is another test QC mix to learn about molecular networking.

2023-08-03 | MSV000092587 | MassIVE

QC mix; quality control mix of 6 compounds trial4

Project description:This is a test, this is only a test, of a 6 mix. This is not useful data. Please don't publish.

2023-08-04 | MSV000092594 | MassIVE

QC mix; quality control mix of 6 compounds

Project description:This is the standard QC 6-six mix as a test. This is just a learning tool.

2023-08-03 | MSV000092586 | MassIVE

Hela digest test as quality control on QE and QE-HF

Project description:Hela test samples prepared by FASP digestion were run on QE and QE-HF mass spectrometry as quality controls. We tried to compare the protein and peptide identification results between the two machines.

2022-09-18 | PXD028625 | JPOST Repository

Editorial: Demonstrating quality control (QC) procedures in fMRI.

Project description: Not available

| S-EPMC10264898 | biostudies-literature

estuary mix

Project description:estuary mix Raw sequence reads

| PRJNA957238 | ENA

Test case data for ChIC - ChIP-seq quality control pipeline

Project description:Test case data for ChIC - ChIP-seq quality control pipeline

| PRJNA490734 | ENA

iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control

Project description:Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metric exhibit higher sensitivity compared to DDA-based QC metric in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.

2024-11-08 | PXD052871 | Pride

ID QC - Peptide identification quality control

Project description:Mitochondria sample replicate 1

2011-10-10 | PRD000373 | Pride

Pisolithus sp. 'mix'

Project description:Pisolithus sp mix smRNA - Indirect_FLM_Redo transcriptome

| PRJNA442580 | ENA