Multi-tissue DNA methylation microarray signature is predictive of gene function.

Background Transcriptional correlation networks derived from publicly available gene expression microarrays have been previously shown to be predictive of known gene functions, but less is known about the predictive capacity of correlated DNA methylation at CpG sites. Guilt-by-association co-expression methods can adapted for use with DNA methylation when a representative methylation value is created for each gene. We examine how methylation compares to expression in predicting Gene Ontology terms using both co-methylation and traditional machine learning approaches across different types of representative methylation values per gene.

Many chronological aging clocks can be found throughout the epigenome: Implications for quantifying biological aging.

Epigenetic alterations are a hallmark of aging and age-related diseases. Computational models using DNA methylation data can create "epigenetic clocks" which are proposed to reflect "biological" aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology.

MNEMONIC: MetageNomic Experiment Mining to create an OTU Network of Inhabitant Correlations.

Background: The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance.

Results: We propose a novel framework for comparing microbial shifts between conditions.

ALE: automated label extraction from GEO metadata.

Background: NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments.