Data files

Nakamichi 2010 Fig5 (from BioDare)

Nakamichi data for upload (from BioDare)

Nakamichi Fig6 (from BioDare)

nakamichi PRRs PC 10 all (from BioDare)

nakamichi2007 (from BioDare)

nakamichi2007 (from BioDare)

"Plates inoculated with Col-0 seed were grown under the same photoperiod conditions to the plants to be analysed. Plant tissue was harvested, making aliquots of 0.4 gFW. MBP-NL3F10H protein was prepared by the method described by Urquiza-Garcia U. and Millar A.J. in Plant Methods 2019. and then quantified by the linearized Bradford assay protocol using both Bovine serum albumin BSA and Ovoalbumin as standards (Ernst & Zor 2010). Then aliquots spiked with purified enzyme to generate a curve ...

4 seeds of stable NanoLUC T3 homozygous lines for LHY, TOC1 and ELF3 were seeded in 96-well flat white plate that contained 150 µl of ROBUST media and stratified for 2 days at 4ºC. Then a 2 hours pulse of white light given and transferred to 22 hours darkness at 21 ºC. Then transferred 12L:12D photoperiod for 10 days at which 50 µl of 1:50 Furimazine:0.05% Triton X-100 added to each well for tracking NanoLUC bioluminescence. Measurements using a Tristar plate reader were performed automatically ...

"Samples of plants were collected in pre-weighed 2 ml microfuge tubes (safelock, Eppendorf) with 5 mm stainless steel grinding balls, and flash frozen in liquid nitrogen. The tissue was ground twice at 30Hz for 1 min in a Tissue Lyser (Qiagen). The samples were flash frozen between grinding steps, then placed on ice and 150 μl of BSII buffer was added to protect the samples from proteolysis, without phosphatase inhibitors (Huang et al. 2016). The tube was weighed and further BSII buffer added to ...

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Salmon feed experiment: Lipidomic data (NEG mode) of liver samples from fresh water sampling.

Salmon feed experiment: Lipidomic data (NEG mode) of muscle samples from fresh water sampling.

Liver samples (fresh water sampling) - Identification of compounds based on the LipidBlast database.

Muscle samples (fresh water sampling) - Identification of compounds based on the LipidBlast database.

Liver samples (salt water sampling) - Identification of compounds based on the LipidBlast database.

Muscle samples (salt water sampling) - Identification of compounds based on the LipidBlast database.

Negative mode lipidomics of liver samples from saltwater sampling.

Negative mode lipidomics of muscle samples from saltwater sampling.

Basic Graph statistics

RCM: Reaction-Compound Mapping, an edge between nodes means reaction contains metabolite MMM: Metabolite-Metabolite Mapping, an edge between nodes means these metabolites are reaction partners

_comp: entire network after removal of duplicated edges _del: taken from the entire network the largest connected subgraph after deletion of a set of nodes

betw: betweeness of nodes clos: closeness of nodes neigh: neighbours node_degree: number of edges per node path: shortest paths ...

The first sheet in this excel file is a Table that collects the standard Gibbs energies of formation (from the elements) of a large number of chemical species that are relevant for metabolism. It obtained these from a substantial number of sources, including Thauer et al and Alberty, as indicated at the bottom of the first sheet of the Table. Following Alberty, (2006) the Table also computes the further transformed Gibbs energies of formation, under the standard conditions also used by Alberty, ...

The NFDI4Health Task Force COVID-19 Metadata Schema (Metadata Schema) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub).

The NFDI4Health Task Force COVID-19 Metadata Schema (MDS) Mapping to FHIR contains a list of properties describing a resource (study, questionnaire or document) being registered in the Central Search Hub of the NFDI4Health Task Force COVID-19 and their mapping to FHIR.

The NFDI4Health Task Force COVID-19 Metadata Schema Mapping (Metadata Schema Mapping) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub) and how those properties align with other standards (FHIRE, CDISK, DRKS, ITRCP)

Batch sample publishing

Batch sample publishing

Batch sample publishing

niwa2007 (from BioDare)

niwa2007 (from BioDare)

Growth yeast with 0mM K+ and collection at 0',10',20',40',60',90',120' and 180'

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 0mM K+ and collection at 0',10',20',40',60',90',120' and 180'

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and long timing collection

Growth yeast with 50mM K+ and short timing collection