Table 3.

High-throughput technologies for network modeling

Experiment	Data type	Output	Application for network modeling
RNA-seq/scRNA-seq	transcriptomics	sequences of expressed transcripts	inferring regulatory relationships between gene expression levels
ATAC-seq/scATAC-seq	chromatin conformation	sequences of DNA that are in an open conformation	identifying DNA sequences that are undergoing epigenetic regulation and which regions can express transcripts
Methyl-seq/scMethyl-seq	DNA methylation	methylated regions of DNA	identifying DNA sequences that are methylated and are thus unlikely to be able to express transcripts
ChIP-seq/scChIP-seq	protein binding to DNA	sequences of DNA with a particular protein/proteins bound	determining where particular regulatory proteins are binding in the genome
Protein mass spectrometry	proteomics	abundance of molecules with specific mass/charge ratio	estimate protein abundance and protein interaction networks
Protein microarrays	proteomics	abundance of a set of proteins	estimate protein abundance and protein interaction networks for a particular set of proteins
CyTOF	proteomics	abundance and location of a set of proteins	estimate protein abundance and protein interaction networks for a particular set of proteins, including a spatial element
CITE-seq	transcriptomics and proteomics	single-cell transcriptomics and abundance of cell surface proteins	infer relationships between gene expression and cell surface protein abundance
Metabolite mass spectrometry	metabolomics	abundance of molecules with specific mass/charge ratio	estimate the relationships between metabolite levels, to data from other experiments
NMR spectroscopy	metabolomics	abundances of organic and some inorganic molecules	estimate the relationships between metabolite levels, to data from other experiments

A list of high-throughput experiments, their outputs, and how these can be potentially applied for biological network modeling.