A team from Denmark, Germany, and Italy outline an integrated, meta-gene-based approach for tapping de novo pathway data to uncover potential expression-based biomarkers to predict complex disease outcomes. The researchers considered their strategy in the context of breast cancer data generated for the Cancer Genome Atlas, for example, before exploring the performance of the meta-gene approach relative to biomarker searches that rely on single gene features. The meta-gene strategy "consistently outperformed [single gene] models," authors say, noting that they have established a web service for scrutinizing gene expression datasets stemming from breast cancer studies.
Researchers from Germany and Austria present applications for a web-based tool called miRMaster for finding and quantifying microRNAs, non-coding RNAs, and other small RNAs based on high-throughput sequence data. After considering small RNAs in more than 1,800 datasets, the team uncovered almost 22,000 new and previously described miRNAs that served as the basis for a custom array spanning almost 12,000 mature miRNAs. "[O]ur predicted miRNA candidates provided as [a] custom array will allow researchers to perform in-depth validation of candidates interesting to them," the authors note.
Swiss and Spanish investigators delve into the gene sub-networks at play in biological pathways, particularly those under selection in human populations adapted to life at high altitude. In an effort to detect selection across more than one gene, the team came up with a computational method for finding polygenic selection from genome-wide data and gene networks. The group applied this so-called signet approach to available datasets for high-altitude-adapted Andean and Tibetan populations, for example, using multi-gene sub-networks to verify known contributors to altitude adaptation and to narrow in on potential new contributors.