Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

Comparative map between the domestic pig and dog

Cross-species chromosome painting with probes derived from flow-sorted dog and human chromosomes was used to construct a high-resolution comparative map for the pig. In total 98 conserved autosomal segments between pig and dog were detected by probes specific for the 38 autosomes and X Chromosome of the dog. Further integration of our results with the published human-dog and cat-dog comparative maps, and with data from comparative gene mapping, increases the resolution of the current pig-human comparative map. It allows for the conserved syntenies detected in the pig, human, and cat to be aligned against the putative ancestral karyotype of eutherian mammals and for the history of karyotype evolution of the pig lineage to be reconstructed. Fifteen fusions, 17 fissions, and 23 inversions are required to convert the ancestral mammalian karyotype into the extant karyotype of the pig.

Molecular cloning, characterization and localization of chicken type II procollagen gene

Chicken type II procollagen (ccol2a1) has become as an important oral tolerance protein for effective treatment of rheumatoid arthritis. However, its molecular identity remains unclear. Here, we reported the full-length cDNA and nearly complete genomic DN