KaPPA-View4

KaPPA-View4 visualizes gene-to-gene co-expression and metabolite-to-metabolite co-accumulation correlations on metabolic pathway maps to integrate transcriptome and metabolome data for pathway-level analysis.

Key Features:

• Integration of transcriptome and metabolome data: Calculates correlations between gene expression and metabolite accumulation from large-scale transcriptome and metabolome datasets.
• Correlation calculation types: Computes gene-to-gene co-expression and metabolite-to-metabolite co-accumulation correlations.
• Visualization as curves on pathway maps: Overlays correlation relationships as curves on metabolic pathway maps, applicable to single or up to four pathway maps simultaneously.
• Access to pathway maps: Uses pathway maps from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and additional maps generated based on gene classifications.
• Integrated co-expression data sources: Incorporates co-expression data from ATTED-II, COXPRESdb, CoP, and MiBASE across organisms including humans, mice, rats, Arabidopsis, rice, tomato, and other plants.
• Regulatory inference: Facilitates identification of novel functions for transcription factors and other regulatory elements through mapped correlation patterns.

Scientific Applications:

• Gene function discovery: Enables inference of unknown gene functions by linking co-expression and co-accumulation patterns to pathway positions.
• Gene family functional diversity: Assesses functional divergence among gene family members via differential correlation patterns on pathways.
• Regulatory mechanism elucidation: Reveals regulatory influences of transcription factors and other regulators on metabolic pathways through correlation overlays.
• Systems-level integration of genomics and metabolomics: Connects transcriptomic and metabolomic datasets to interpret pathway dynamics in genomics, metabolomics, and systems biology studies.

Methodology:

Calculates gene-to-gene co-expression and metabolite-to-metabolite co-accumulation correlations from transcriptome and metabolome datasets and overlays these correlations as curves on metabolic pathway maps (single or up to four maps simultaneously).