The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease

There are numerous genetic factors like MC4R (Melanocortin-4 receptor), POMC (Pro-opiomelanocortin), SIM1 (Single Minded Gene) etc. important in obesity, which can be used as biomarker. But more reliable diagnostic markers are the need for today, along with new therapeutic strategies that target specific molecules in the disease pathways. As in mouse and human genes, where mutations in one or both species are associated with some phenotypic characteristics as observed in human disease. In molecular mechanisms of development, differentiation, and disease gene expression data provide crucial insights. Up-regulation and down-regulation of selective genes can have major effects on diet-induced obesity, but there is little or no effect when animals are fed a low-fat diet. In present study we have studied the gene expression data of mouse at different theiler stages using GXD BioMart. The interacting partners and pathway of the genes that are already used as biomarker in mouse as well as in humans have been studied. A gene NPY1R (Neuropeptide Y1 receptor) was taken as common after STRING and KEGG results on the basis of biochemical pathways and interactions similar to MC4R. Our present work focuses on comparative genomics and proteomics analysis of NPY1R, which has led to identification of biomarker by comparing it with already known MC4R human and mouse biomarker. It has been concluded that both the proteins are structurally and functionally similar.

We utilized a comparative genomics approach to analyze a core set of tumorigenesis orthologs among human, mouse, dog and naked mole rat. The analysis identified cancer orthologs that are both conserved and divergent between dog and the cancer resistant species naked mole rat. These tumorigenesis orthologous are associated with phenotypes that modulate cancer susceptibility, cardiac development, craniofacial development, brain development, skeletal development, and immune function, to name a few. This bioinformatics approach employed a variety of literature mining tools to further uncover relationships between the tumorigenesis orthologs. Together, these results shed light on the relationship between breed formations, breed associated morphological traits and breed associated susceptibility to tumorigenesis. These findings support the use of a comparative genomic analysis between species with dramatically different disease phenotypes as a gene discovery tool. A total of 146 proteins coding SNPs were identified in these tumorigenesis orthologs representing missense variations, frame shift variations and nonsense variations. The genes identified in this study can serve as a list of candidates for subsequent laboratory and clinical study. Furthermore, the identification of SNPs impacting the primary structure of the tumorigenesis orthologs may provide clues about the basis of cancer susceptibility between dog breeds.

Motivation: The incidence of ageing-related diseases has been constantly increasing in the last decades, raising the need for creating effective methods to analyze ageing-related protein data. These methods should have high predictive accuracy and be easily interpretable by ageing experts. 15 To enable this, one needs interpretable classification models (supervised machine learning) and features with rich biological meaning. In this paper we propose two interpretable feature types based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compare them with traditional feature types in hierarchical classification (a more challenging classification task regarding predictive performance) and binary classification (a classification task producing easier to inter-20 pret classification models). As far as we know, this work is the first to: (i) explore the potential of the KEGG pathway data in the hierarchical classification setting, (i) use the graph structure of KEGG pathways to create a feature type that quantifies the influence of a current protein on another specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the classification models induced using KEGG features. 25 Results: We performed tests measuring predictive accuracy considering hierarchical and binary class labels extracted from the Mouse Phenotype Ontology. One of the KEGG feature types leads to the highest predictive accuracy among five individual feature types across three hierarchical classification algorithms. Additionally, the combination of the two KEGG feature types proposed in this work results in one of the best predictive accuracies when using the binary class version of our 30 datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information.