XCMS Online

Lipidomics and Metabolomics Data Processing

Description

XCMS Online (https://xcmsonline.scripps.edu/) is an extensively used cloud-based platform for LC-MS data processing. XCMS Online is derived from the original XCMS technology, which is also still widely used and actively maintained. XCMS Online extends XCMS capabilities to a full suite of tools for metabolomic and lipidomic data analysis. Hosted on high performance and dedicated servers at Scripps Research, XCMS Online offers high processing speeds that is easily accessed by thousands of users. Together, with a freely available R package and a cloud-based data processing, XCMS facilitates data analysis and result sharing across all vendor platforms. XCMS Online is also linked to the METLIN (http://metlin.scripps.edu) database to facilitate lipid and metabolite ID.

Technical Information

Publications:

PMID:16448051,

PMID:22533540

Training datasets:

Documentation and user guide:

XCMS

Source code repository:

Programming languages:

Python,

PHP,

HTML,

CSS,

Javascript,

JQuery

Platforms:

Windows,

Linux,

MacOS

Output formats:

Excel,

PDF,

HTML (incl. images)

Input formats:

mzXML,

mzData,

mzData.XML,

netCDF,

CDF,

.wiff(SCIEX),

.wiff.scan(SCIEX)

Web platform:

Yes

Desktop client:

CLI:

GUI:

Yes

License:

GPL & MIT (Academic)

Download / Web-service link:

Yes

Tasks

6) Analysis and visualization of lipidomics data

Direct connection to identification results:

Yes

Classification and feature selection:

Clustering and correlation analysis:

Feature identification:

Supervised multivariate statistical analysis methods:

Unsupervised multivariate statistical analysis methods:

PCA

Univariate statistical analysis methods:

ANOVA,

Fold-change analysis

Missing data handling:

Yes

4.1) Full MS (HRAM LC-MS)

Other features:

Fold change analysis,

FDR control (q-values),

Principle component analysis plot (PCA),

Heatmaps,

Pathway cloud plot

Batch processing:

Yes

Lipid quantification:

Yes

Lipid identification:

Build-in matching to METLIN DB

Correction for in source fragments:

Support multiple adducts:

Yes

Adjustable feature detection:

S/N, mass tolerance

Rt alignment and correction:

Yes

Algorithms:

Filtering and peak identification, matching peaks across samples, filling in missing peak data. Build-in matching to METLIN DB

LC-MS:

Yes

Shotgun MS/MS:

5) Lipid quantification from untargeted lipidomics datasets (HRAM MS, DDA, DIA)

Calibration curve:

Semi absolute quantification using internal standards:

Relative quantification:

Yes

Normalization methods:

Peak integration:

Peak area

Peak processings:

Peak picking,

Peak alignment,

Deconvolution