KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model.

The dynamic impact approach (DIA) represents an alternative to overrepresentation analysis (ORA) for functional analysis of time-course experiments or those involving multiple treatments. The DIA can be used to estimate the biological impact of the differentially expressed genes (DEGs) associated with particular biological functions, for example, as represented by the Kyoto encyclopedia of genes and genomes (KEGG) annotations. However, the DIA does not take into account the correlated dependence structure of the KEGG pathway hierarchy. We have developed herein a path analysis model (KEGG-PATH) to subdivide the total effect of each KEGG pathway into the direct effect and indirect effect by taking into account not only each KEGG pathway itself, but also the correlation with its related pathways. In addition, this work also attempts to preliminarily estimate the impact direction of each KEGG pathway by a gradient analysis method from principal component analysis (PCA). As a result, the advantage of the KEGG-PATH model is demonstrated through the functional analysis of the bovine mammary transcriptome during lactation.

[1]  W. Woodward,et al.  On mammary stem cells , 2005, Journal of Cell Science.

[2]  M. Neville,et al.  Tight Junction Regulation in the Mammary Gland , 1998, Journal of Mammary Gland Biology and Neoplasia.

[3]  P. Theil,et al.  Cellular mechanisms in regulating mammary cell turnover during lactation and dry period in dairy cows. , 2008, Journal of dairy science.

[4]  Jeffrey P. Bond,et al.  Onset of lactation in the bovine mammary gland: gene expression profiling indicates a strong inhibition of gene expression in cell proliferation , 2008, Functional & Integrative Genomics.

[5]  W. Hurley,et al.  A Novel Dynamic Impact Approach (DIA) for Functional Analysis of Time-Course Omics Studies: Validation Using the Bovine Mammary Transcriptome , 2012, PloS one.

[6]  M. Paape,et al.  Mammary cell number, proliferation, and apoptosis during a bovine lactation: relation to milk production and effect of bST. , 2001, Journal of dairy science.

[7]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[8]  R. Callahan,et al.  Notch Signaling in Mammary Development and Oncogenesis , 2004, Journal of Mammary Gland Biology and Neoplasia.

[9]  J. Loor,et al.  Bioinformatics and Biology Insights Gene Networks Driving Bovine Mammary Protein Synthesis during the Lactation Cycle , 2022 .

[10]  C. Lebrilla,et al.  Bovine milk glycome. , 2008, Journal of dairy science.

[11]  M. C. Rudolph,et al.  Functional Development of the Mammary Gland: Use of Expression Profiling and Trajectory Clustering to Reveal Changes in Gene Expression During Pregnancy, Lactation, and Involution , 2003, Journal of Mammary Gland Biology and Neoplasia.

[12]  Jing Cao,et al.  GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach , 2010, Bioinform..

[13]  P. Khatri,et al.  A systems biology approach for pathway level analysis. , 2007, Genome research.

[14]  Tom Freeman,et al.  Gene expression profiling of mammary gland development reveals putative roles for death receptors and immune mediators in post-lactational regression , 2003, Breast Cancer Research.

[15]  S. Ellis,et al.  Lactation persistency: insights from mammary cell proliferation studies. , 2003, Journal of animal science.

[16]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[17]  W. Hurley,et al.  Old and New Stories: Revelations from Functional Analysis of the Bovine Mammary Transcriptome during the Lactation Cycle , 2012, PloS one.