Tables

Overview Table

All tables from the xOmics documentation are listed here:

Table	Description	See Also
t1_omics_fields	Omic fields targeted by xOmics	nan
t2_quantification_methods	Quantification methods used in omic fields	nan
t3_overview_datasets	Omics example datasets	aa.load_dataset
t4_omics_analysis_tools	Analysis tools for omics data	nan
t5_omics_post_analysis_tools	Post-analysis tools for omics data	nan
t6_enrichment_tools	Enrichment analysis tools	nan

Omics fields

All omics field of which data can be analyzed by the xOmics toolkit are summarized in the following table:

Omics Field	Short Description	Associated Quantification Methods
Proteomics	Study of the entire set of proteins, including their abundances, modifications, and interactions, in a cell, tissue, or organism.	Label-Free Quantification (LFQ), Stable Isotope Labeling, TMT/iTRAQ, MRM/PRM
Transcriptomics	Study of the total mRNA content within a cell or tissue, providing insights into gene expression patterns and regulatory networks.	RNA-seq, qRT-PCR, Microarrays
Lipidomics	Analysis of lipids in a sample, which includes the identification and quantification of thousands of underlying lipid species.	Label-Free Quantification (LFQ), Stable Isotope Labeling, Internal Standards, MRM/PRM, Shotgun Lipidomics
Metabolomics	Comprehensive analysis of small molecule metabolites in biological samples, providing insights into metabolic pathways and physiological state.	Label-Free Quantification (LFQ), Stable Isotope Labeling, Internal Standards, MRM/PRM

Quantification methods

The different quantification methods used in these omic fields are described in this overview:

Quantification Method	Description	Omics Fields Utilizing the Method
Label-Free Quantification (LFQ)	Direct comparison of ion intensities or other signal outputs for specific molecules between samples without any labels.	Proteomics, Lipidomics, Metabolomics
Stable Isotope Labeling	Incorporation of heavy isotopes (e.g., ^13C, ^15N, ^2H) into molecules of interest, allowing quantification by comparing signal intensities of labeled versus unlabeled molecules.	Proteomics, Lipidomics, Metabolomics
TMT, iTRAQ	Isobaric labeling methods where chemical tags fragment in a mass spectrometer to produce reporter ions. Intensity of reporter ions is used for quantification.	Proteomics
Internal Standards	Use of specific molecules (often isotopically labeled) added to samples as a reference for quantification, correcting for potential variability in analysis.	Lipidomics, Metabolomics
MRM/PRM	Targeted mass spectrometry techniques. MRM monitors specific transitions between precursor and product ions, while PRM captures all product ions from a chosen precursor.	Proteomics, Lipidomics, Metabolomics
RNA-seq	A sequencing method to quantify RNA molecules. Abundance is determined based on read counts or TPM (transcripts per million).	Transcriptomics
qRT-PCR	A targeted method to measure RNA abundance using quantitative PCR after reverse transcription.	Transcriptomics
Microarrays	Measures gene expression by hybridizing labeled cDNA or cRNA to probes on a slide. Signal intensity from each probe corresponds to transcript abundance.	Transcriptomics
Shotgun Lipidomics	Direct infusion of lipid samples into a mass spectrometer without prior separation.	Lipidomics

Overview of Datasets

Example proteomic datasets are given for different mouse models for neurodegenerative diseases.

Datasets are named according to the assessed disease (e.g., Alzheimer´s disease (AD)) and the name of the mouse models, as described in Alzforum or defined in the respective publication. Datasets were obtained by mass spectrometry (MS)-based proteomics.

Dataset	Data Type	Description	Condition	Quantification	Reference
PROT_DEMYELINATION	Proteomic	Demylination recovery experiment in mouse	4 timepoints in days [‘d00’, ‘d04’, ‘d10’, ‘d14’]	LFQ	Penkert21
LIPID_DEMYELINATION	Lipidomics	Demylination recovery experiment in mouse	4 timepoints in days [‘d00’, ‘d04’, ‘d10’, ‘d14’]	Internal Standards	Penkert21

Omics Analysis Tools

Overview of different omics analysis software tools such as MaxQuant or DIA-NN for proteomics data are given.

Tool	Description	Usability	Open-source	Documentation	Community & Support	Integration	Programming Language/GUI	Advantages	Disadvantages	Publication
MaxQuant	Proteomics data analysis, especially for label-free quantification	Proteomics	No	Excellent	Active	Moderate	Standalone / GUI	Robust algorithms, widely used	Requires high computational resources	[Link to paper]
Spectronaut	Analysis of DIA (data-independent acquisition) mass spectrometry data	Proteomics	No	Good	Managed by Biognosys	Limited	Standalone / GUI	Optimized for DIA, high reproducibility	Proprietary software	[Link to paper]
DIA-NN	Software suite for DIA data analysis	Proteomics	Yes	Good	Growing	Good	Command-line	Open-source, versatile	Command-line based	[Link to paper]
Skyline	Targeted mass spec data analysis	Proteomics	Yes	Excellent	Active	Excellent	Standalone / GUI	Supports multiple instrument vendors, extensible	Mainly for targeted proteomics	[Link to paper]
LipidSearch	Software for lipidomics data processing and identification	Lipidomics	No	Good	Managed by Thermo Fisher	Moderate	Standalone / GUI	Comprehensive lipid databases, integration with mass spec instruments	Proprietary software	[Link to paper]
LipidHunter	Identification of lipids from LC-MS/MS data	Lipidomics	Yes	Good	Active	Good	Python	Open-source, comprehensive output	Requires good understanding of lipidomics	[Link to paper]
MZmine	Framework for processing, visualization, and analysis of mass spectrometry data	Metabolomics	Yes	Good	Active	Good	Java	Modular, supports various data processing tasks	Java-centric, learning curve	[Link to paper]
MetaboAnalyst	Comprehensive web-based tool for metabolomics data analysis	Metabolomics	Yes	Excellent	Active	Good	Web-based	Wide range of statistical methods, user-friendly interface	Web-based, can limit very large analyses	[Link to paper]
XCMS	Processing and analysis of untargeted metabolomics data	Metabolomics	Yes	Good	Active	Good	R	Widely used in the community, high flexibility	Requires R programming knowledge	[Link to paper]
Compound Discoverer	Software for metabolite identification and quantitative analysis	Metabolomics	No	Good	Managed by Thermo Fisher	Moderate	Standalone / GUI	Comprehensive workflow, integration with mass spec instruments	Proprietary software	[Link to paper]

Post-Analysis Tools

Post-analysis tools for omics data are diverse software solutions that facilitate specialized types of data evaluations, like differential gene expression analysis. These tools span from Graphical User Interface (GUI) applications such as Perseus to Python-based packages tailored for specific analyses, such as Scanpy for single-cell RNAseq data analysis.

Tool	Description	Usability	Open-source	Documentation	Community & Support	Integration	Programming Language/GUI	Advantages	Disadvantages	Publication
Perseus	Comprehensive platform for in-depth analysis of proteomics data	Proteomics	No	Good	Moderate	Limited	GUI	Comprehensive analysis for MaxQuant data	Limited to specific datasets	[Link to paper]
PEPPI	Tool for analyzing protein-protein interactions and functional associations	Proteomics	Unknown	Moderate	Unknown	Unknown	Likely GUI	Protein interaction analysis	Unknown support and documentation	Unknown
MSstats	Statistical relative quantification in mass spectrometry-based proteomics	Proteomics	Yes	Good	Active	Moderate	R / GUI	Robust statistical framework	R learning curve for some	[Link to paper]
Pyteomics	Collection of tools for various tasks in proteomics data analysis	Proteomics	Yes	Good	Moderate	Good	Python	Python-based, flexible	Requires Python expertise	[Link to paper]
AlphaPept	Peptide identification and quantification	Proteomics	Yes	Good	Growing	Limited	Python / GUI	Fast and accurate peptide identification	Still maturing	[Link to paper]
Seurat	Toolkit for quality control, analysis, and exploration of single-cell RNA-seq data	scRNA-seq	Yes	Excellent	Very Active	Good	R / GUI	Comprehensive scRNA-seq toolkit	R learning curve for some	[Link to paper]
Scanpy	Analyzing and visualizing single-cell RNA-seq data with emphasis on scalability and speed	scRNA-seq	Yes	Excellent	Very Active	Excellent	Python	Scalable, integration with other tools	Python-centric	[Link to paper]
SCope	Fast, scalable, and user-friendly tool for visualizing and interpreting large datasets from scRNA-seq	scRNA-seq	Yes	Good	Active	Good	Web-based	User-friendly, web-based	Limited to visualization	[Link to paper]
AnnData	Handling matrix data with annotations	scRNA-seq	Yes	Good	Associated with Scanpy	Good	Python	Efficient data structure for large datasets	Primarily a data structure, not a full toolkit	[Link to paper]
MetaboAnalyst	Comprehensive platform for metabolomics data analysis and interpretation	Metabolomics	Yes	Excellent	Active	Good	Web-based	Comprehensive, user-friendly	Web-based might limit large-scale analyses	[Link to paper]
XCMS	LC/MS and GC/MS data preprocessing	Metabolomics	Yes	Excellent	Very Active	Excellent	R / GUI	Industry standard for LC/MS data	R learning curve for some	[Link to paper]
MZmine	MS-based molecular profile data processing and analysis	Metabolomics, Lipidomics	Yes	Good	Active	Good	Java / GUI	Versatile and supports various data formats	Java-based, might be slower on large data	[Link to paper]
LipidSearch	Accurate identification and quantification of lipids from LC-MS/MS data	Lipidomics	No	Good	Managed by Thermo	Limited	GUI	Accurate lipid identification	Proprietary and expensive	[Link to paper]
LipidHunter	Direct annotation of lipid species from LC-MS datasets	Lipidomics	Yes	Moderate	Moderate	Moderate	Python	Direct lipid species annotation	Requires command-line experience	[Link to paper]

Enrichment Tools

Enrichment analysis for omics data (most often genes) is a computational method used to identify which predefined sets of genes are statistically over-represented in a large set of genes. It helps in deciphering the biological significance behind large-scale molecular data by linking genes to known pathways, functions, or other biological categories. While proteins are analyzed based on their gene names using Gene Ontology terms or pathway terms of databases such as Reactome, enrichment analysis tools for lipids are improving with the annotation scope of the Lipid Ontology. See on overview of diverse enrichment tools here:

Tool	Description	Usability	Open-source	Documentation	Community & Support	Integration	Programming Language/GUI	Advantages	Disadvantages	Publication
GSEA	Tool for gene set enrichment analysis	Genomics	Yes	Excellent	Active	Good	Java / GUI	Benchmark for GSEA, widely used	Java-centric, may be slower on huge datasets	[Link to paper]
Enrichr	Web-based tool for gene set enrichment analysis	Genomics	Yes	Excellent	Active	Excellent	Web-based	Comprehensive databases, user-friendly interface	Web-based might limit very large analyses	[Link to paper]
DAVID	Bioinformatics resources for gene functional classification	Genomics	No	Good	Moderate	Limited	Web-based	Multiple annotation tools, widely recognized	Outdated interface, limited updates	[Link to paper]
WebGestalt	Web-based gene set analysis toolkit	Genomics	Unknown	Good	Active	Good	Web-based	Multiple enrichment methods, integrated databases	Limited by web-interface constraints	[Link to paper]
g:Profiler	Functional profiling of gene lists from large-scale experiments	Genomics	Yes	Good	Active	Good	Web-based	Multi-level annotation, user-friendly interface	Web-based, can have slow response times	[Link to paper]
PANTHER	Protein ANalysis THrough Evolutionary Relationships	Genomics	No	Excellent	Managed by PANTHER	Limited	Web-based	Classification system, evolutionary data	Mainly for protein-centric analysis	[Link to paper]
Metascape	Tool for gene annotation and analysis resource	Genomics	Unknown	Good	Active	Good	Web-based	Multiple methods and databases combined	Limited to predefined gene sets	[Link to paper]
LION/web	Lipidome isotope labeling-based ontology	Lipidomics	Unknown	Good	Growing	Moderate	Web-based	Comprehensive lipid databases	Web-based constraints	[Link to paper]
ClueGO	Cytoscape plug-in to decipher functionally grouped gene ontology networks	Genomics	Unknown	Good	Active	Excellent	Cytoscape plug-in	Visual representation, integrates multiple data	Requires Cytoscape	[Link to paper]
FAST	Functional Annotation of the Mammalian Genome	Genomics	Unknown	Good	Managed by FANTOM	Limited	Web-based	Broad mammalian genome annotation	Focused on mammalian genomes	[Link to paper]