In PLoS Genetics this week, researchers at the University of Helsinki in Finland and their colleagues report their elucidation of novel candidate genes for obesity, which they found by mining the "gray zone" of data from genome-wide association studies. Using their method, the team was able to infer the "causality of genes with obesity by employing a unique set of monozygotic twin pairs  discordant for BMI," they write. In this way, they identified 27 genes with potential causal roles in "determining the degree of human adiposity." The authors suggest their study represents a novel approach for mining gene expression studies for "informing choice of candidate genes for complex human phenotypes."
Similarly, a research team at Aarhus University in Denmark suggests in PLoS One that their re-inspection of small RNA sequence data sets revealed novel human miRNA genes. Using two previously published data sets — from human embryonic stem cells and embroid bodies — the authors "identified 112 novel miRNA-like structures and were able to validate miRNA processing in 12 out of 17 investigated cases," and substantiated several addition miRNA candidates "by including additional available small RNA data sets, thereby demonstrating the power of combining data sets to identify miRNAs that otherwise may be assigned as experimental noise," they write.
A team from the University of California, San Diego, reports their investigation of the protein families in published human gut genomic and metagenomic data in PLoS Computational Biology this week. "Using an automated procedure, we identified a group of protein families strongly overrepresented in the human gut. These not only include many families described previously but also, interestingly, a large group of previously unrecognized protein families, which suggests that we still have much to discover about this environment," the authors write, adding that analysis of these families could supply novel information about an optimal environment for human health.
Researchers at the University of Michigan and GeneGo discuss their "'topological significance' analysis of gene expression and proteomic profiles from prostate cancer cells," which revealed "key mechanisms of androgen response," in PLoS One this week. Specifically, the team used microarrays and iTRAQ proteomic techniques to concurrently measure gene expression and protein levels; they analyzed groups of up-regulated genes and proteins using their "novel concept of 'topological significance,'" which "combines high-throughput molecular data with the global network of protein interactions to identify nodes which occupy significant network positions with respect to differentially expressed genes or proteins," they write.