German researchers present a computational strategy for pulling together data from multiple omics sources when doing biological assessments of gene sets. The team outlined the rationale behind its "Multi-level Ontology Analysis" algorithm, nicknamed MONA, in the study, demonstrating that the model-based method could accurately deal with multiple levels of information — both synthetic data and data generated for three studies using various molecular profiling types. Based on their findings, the study's authors say MONA "performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including [messenger RNA] fine-tuning by microRNAs."
A team from Japan, China, and France describes its methylation-specific fluorescence in situ hybridization, or MeFISH, method for visualizing methylation status at specific sequences in another Nucleic Acids Research study. The researchers used fluorescence-labeled probes that interact differently with 5'-methylated cytosine than with the unmodified form of the base. Using the MeFISH method, which centers on so-called "inter-strand complex formation with osmium and bipyridine-containing nucleic acids," the group demonstrated that it could detect methylated satellite repeat sequences in mouse cell nuclei or chromosomes.
Finally, French researchers outline a quality-control strategy for dealing with data generated by chromatin immunoprecipitation-sequencing, or ChIP-seq, or other enrichment-based sequencing methods. The group's bioinformatics-based approach, called NGS-QCi Generator, taps raw sequencing datasets to uncover local and global quality control indicators, optimal depths, antibody and dataset comparability, and the like. For instance, the investigators applied NGS-QCi Generator to more than 5,600 publicly available datasets generated by ChIP-seq, RNA-sequencing, global run-on sequencing, and so on, developing a database to house the resulting quality control-related profiles.