Classification and Clustering on Microarray Data for Gene Functional Prediction Using R.

Gene expression data (microarrays and RNA-sequencing data) as well as other kinds of genomic data can be extracted from publicly available genomic data. Here, we explain how to apply multivariate cluster and classification methods on gene expression data. These methods have become very popular and are implemented in freely available software in order to predict the participation of gene products in a specific functional category of interest. Taking into account the availability of data and of these methods, every biological study should apply them in order to obtain knowledge on the organism studied and functional category of interest. A special emphasis is made on the nonlinear kernel classification methods.

Sjögren's syndrome at the crossroad of polyautoimmunity.

The coexistence of autoimmune diseases (i.e., polyautoimmunity) in Sjögren's syndrome (SS) was investigated in a cross-sectional study involving 410 patients. Logistic regression analysis and the Rogers and Tanimoto index were used to evaluate risk factors and clustering, respectively. There were 134 (32.6%) patients with polyautoimmunity. The most frequent and closer coexistent diseases were autoimmune thyroid disease (21.5%), rheumatoid arthritis (8.3%), systemic lupus erythematosus (7.6%), and inflammatory bowel disease (0.7%) which together constituted a cluster group. There were 35 (8.5%) patients with multiple autoimmune syndrome. Besides disease duration, a history of habitual smoking and spontaneous abortion were found to be risk factors for the developing of polyautoimmunity. This study discloses a high prevalence of polyautoimmunity in SS, its associated risk factors and the grouping pattern of such a condition. These results may serve to define plausible approaches to study the common mechanisms of autoimmune diseases.

Losing helena: the extinction of a drosophila line-like element.

BACKGROUND: Transposable elements (TEs) are major players in evolution. We know that they play an essential role in genome size determination, but we still have an incomplete understanding of the processes involved in their amplification and elimination from genomes and populations. Taking advantage of differences in the amount and distribution of the Long Interspersed Nuclear Element (LINE), helena in Drosophila melanogaster and D. simulans, we analyzed the DNA sequences of copies of this element in samples of various natural populations of these two species. RESULTS: In situ hybridization experiments revealed that helena is absent from the chromosome arms of D. melanogaster, while it is present in the chromosome arms of D. simulans, which is an unusual feature for a TE in these species. Molecular analyses showed that the helena sequences detected in D. melanogaster were all deleted copies, which diverged from the canonical element. Natural populations of D. simulans have several copies, a few of them full-length, but most of them internally deleted. CONCLUSION: Overall, our data suggest that a mechanism that induces internal deletions in the helena sequences is active in the D. simulans genome.

Role of bacterial peptidase F inferred by statistical analysis and further experimental validation.

Despite the quantity of high-throughput data available nowadays, the precise role of many proteins has not been elucidated. Available methods for classifying proteins and reconstructing metabolic networks are efficient for finding global categories, but do not answer the biologist's specific and targeted questions. Following Yamanishi et al. [Yamanishi, Y, Vert, JP, Nakaya, A, and Kaneisha, M (2003). "Extraction of correlated clusters from multiple genomic data by generalized kernel canonical correlation analysis." Bioinformatics 19, Suppl. 1, i323-i330] we used a kernel canonical correlation analysis (KCCA) to predict the role of the bacterial peptidase PepF. We integrated five existing data types: protein metabolic networks, microarray data, phylogenetic profiles, distances between proteins and incomplete two-dimensional-gel data (for which we propose a completion strategy), available for Lactococcus lactis to determine relationships between proteins. The predicted relationships were then used to guide our laboratory work which proved most of the predictions correct. PepF had previously been characterized as a zinc dependent endopeptidase [Nardi, M, Renault, P, and Monnet, V (1997). "Duplication of the pepF gene and shuffling of DNA fragments on the lactose plasmid of Lactococcus lactis." J. Bacteriol. 179, 4164-4171; Monnet, V, Nardi, M, Chopin, MC, and Gripon, JC (1994). "Biochemical and genetic characterization of PepF on oligoendopeptidase from Lactococcus lactis." J. Bio. Chem. 269, 32070-32076]. Analyzing a PepF mutant, we confirmed its participation in protein secretion through a strong relationship between the signal peptidase I and PepF predicted by the KCCA. The global nature of our approach made it possible to discover pleiotropic roles of the protein which had remained unknown using classical approaches.