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Background: Risk management programs targeting women with genetic predispositions to breast cancer (BC), eg, BRCA1 and BRCA2, are effective assuming full adherence with the program protocol. However, high risk to BC in women and equal access to care may not result in high and uniform adherence with the program., Objective: To elucidate factors influencing adherence with screening program in women with genetic predispositions to BC., Material and Methods: We retrieved data from a multicenter pathogenic-related BC surveillance program across 4 French regions. We used multilevel logistic modeling to analyze factors of adherence with the program, with "on-time" or postponed screening as the dependent variable., Results: Seven hundred and seventy-eight participants were followed for a 4.7-year median. We observed 2796 annual screening rounds and 5.4% postponed rounds with a 6-month margin. Women with prevalent BC and carriers of BRCA1 and BRCA2 mutations did not have on-time annual screenings any more than women low cancer risk. Better adherence was observed with screenings after the 2nd round, with higher total number of rounds. Having one or more recalls was significantly associated with worse adherence. No contextual factors affected adherence. Furthermore, postponed rounds increased between 2018 and 2020 compared to 2015 and 2017., Conclusion: Having a higher BC risk status does not result in better adherence to the risk management program. However, factors directly related to screening rounds reduced postponements. Future research should address the benefits of screening-related organizational factors that contribute to adherence improvement., (© The Author(s) 2024. Published by Oxford University Press.)

Biological nitrogen fixation is a fundamental biogeochemical process that transforms molecular nitrogen into biologically available nitrogen via diazotrophic microbes. Diazotrophs anaerobically fix nitrogen using the nitrogenase enzyme which is arranged in three different gene clusters: (i) molybdenum nitrogenase ( nifHDK ) is the most abundant, followed by it's alternatives, (ii) vanadium nitrogenase ( vnfHDK ) and (iii) iron nitrogenase ( anfHDK ). Multiple databases have been constructed as resources for diazotrophic 'omics analysis; however, an integrated database based on whole genome references does not exist. Here, we present NF ix DB (Nitrogen Fixation DataBase), a comprehensive integrated whole genome based database for diazotrophs, which includes all nitrogenases ( nifHDK , vnfHDK , anfHDK ) and nitrogenase-like enzymes (e.g. nflHD ) linked to ribosomal RNA operons (16S-5S-23S). NF ix DB was computed using Hidden Markov Models (HMMs) against the entire whole genome based Genome Taxonomy Database (GTDB R214), providing searchable reference HMMs for all nitrogenase and nitrogenase-like genes, complete ribosomal RNA operons, both GTDB and NCBI/RefSeq taxonomy, and an SQL database for querying matches. We compared NF ix DB to nifH databases from Buckley, Zehr, Mise and FunGene finding extensive evidence of nifH , in addition to vnfH and nflH . NF ix DB contains >4000 verified nifHDK sequences contained on 50 unique phyla of bacteria and archaea. NF ix DB provides the first comprehensive nitrogenase database available to researchers unlocking diazotrophic microbial potential., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Motivation: MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities., Results: MetaCerberus is 1.3× as fast on a single node than eggNOG-mapper v2 on 5× less memory using an exclusively HMM/HMMER mode. In a direct comparison, MetaCerberus provides better annotation of viruses, phages, and archaeal viruses than DRAM, Prokka, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186× smaller database, and with 63× less memory. MetaCerberus is fully integrated for automatic analysis of statistics and pathways using differential statistic tools (i.e. DESeq2 and edgeR), pathway enrichment (GAGE R), and pathview R. MetaCerberus provides a novel tool for unlocking the biosphere across the tree of life at scale., Availability and Implementation: MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus compatible with Python 3 and works on both Mac OS X and Linux. MetaCerberus can also be easily installed using bioconda: mamba create -n metacerberus -c bioconda -c conda-forge metacerberus., (© The Author(s) 2024. Published by Oxford University Press.)