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

License:

GPL & MIT (Academic)

GUI:

Yes

CLI:

Desktop client:

Web platform:

Yes

Input formats:

.wiff.scan(SCIEX),

.wiff(SCIEX),

CDF,

netCDF,

mzData.XML,

mzData,

mzXML

Output formats:

HTML (incl. images),

PDF,

Excel

Platforms:

MacOS,

Linux,

Windows

Programming languages:

JQuery,

Javascript,

CSS,

HTML,

PHP,

Python

Source code repository:

Download / Web-service link:

Yes

Documentation and user guide:

XCMS

Training datasets:

Publications:

PMID:22533540,

PMID:16448051

Tasks

4.1) Full MS (HRAM LC-MS)

Shotgun MS/MS:

LC-MS:

Yes

Algorithms:

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

Rt alignment and correction:

Yes

Adjustable feature detection:

S/N, mass tolerance

Support multiple adducts:

Yes

Correction for in source fragments:

Lipid identification:

Build-in matching to METLIN DB

Lipid quantification:

Yes

Batch processing:

Yes

Other features:

Pathway cloud plot,

Heatmaps,

Principle component analysis plot (PCA),

FDR control (q-values),

Fold change analysis

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

Peak processings:

Deconvolution,

Peak alignment,

Peak picking

Peak integration:

Peak area

Normalization methods:

Relative quantification:

Yes

Semi absolute quantification using internal standards:

Calibration curve:

6) Analysis and visualization of lipidomics data

Missing data handling:

Yes

Univariate statistical analysis methods:

Fold-change analysis,

ANOVA

Unsupervised multivariate statistical analysis methods:

PCA

Supervised multivariate statistical analysis methods:

Feature identification:

Clustering and correlation analysis:

Classification and feature selection:

Direct connection to identification results:

Yes