The mutant mouse resource and research center (MMRRC) consortium: the US-based public mouse repository system.

Now in its 25th year, the Mutant Mouse Resource and Research Center (MMRRC) consortium continues to serve the United States and international biomedical scientific community as a public repository and distribution archive of laboratory mouse models of human disease for research. Supported by the National Institutes of Health (NIH), the MMRRC consists of 4 regionally distributed and dedicated vivaria, offices, and specialized laboratory facilities and an Informatics Coordination and Service Center (ICSC). The overarching purpose of the MMRRC is to facilitate groundbreaking biomedical research by offering an extensive repertoire of mutant mice that are essential for advancing the understanding of human physiology and disease.

PhagoScreener: A novel phagogram platform based on a capillary-wave microbioreactor.

Due to the overuse of antibiotics, the number of multidrug-resistant pathogen bacteria is rising in recent years posing a serious threat to human health. One promising alternative for treatment is the application of phage therapy using highly selective bacteriophages. Because of their selectivity, individual screens called phagograms for each patient are required to select phages from a phage library.

Placental-brain axis in females detected within broadly impacted metabolic gene networks protects against prenatal PCB exposure.

Background: Neurodevelopmental disorders have a strong male bias that is poorly understood. Placenta is a rich source of molecular information about environmental interactions with genetics (including biological sex), that affect the developing brain. We investigated placental-brain transcriptional responses in an established mouse model of prenatal exposure to a human-relevant mixture of polychlorinated biphenyls (PCBs).

Improving rigor and reproducibility in chromatin immunoprecipitation assay data analysis workflows with Rocketchip.

As genome sequencing technologies advance, the accumulation of sequencing data in public databases necessitates more robust and adaptable data analysis workflows. Here, we present Rocketchip, which aims to offer a solution to this problem by allowing researchers to easily compare and swap out different components of ChIP-seq, CUT&RUN, and CUT&Tag data analysis, thereby facilitating the identification of reliable analysis methodologies. Rocketchip enables researchers to efficiently process large datasets while ensuring reproducibility and allowing for the reanalysis of existing data.