MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using and publishing useful databases. To address these issues, the authors developed a Resource Description Framework-based database platform, called the RIKEN MetaDatabase (http://metadb.riken.jp), that allows biologists to develop, publish and integrate multiple databases easily. The platform manages the metadata of both research and individual data described using standardised vocabularies and ontologies, and has a simple browser-based graphical user interface to view data including tabular and graphical forms. The platform was released in April 2015, and 113 databases, including mammalian, plant, bioresource and image databases, with 26 ontologies have been published using this platform as of January 2017. This paper describes the technical knowledge obtained through the development and operation of the RIKEN MetaDatabase to accelerate life science data distribution.

Abstract Summary: This article reports the development of SDOP-DB, which can provide definite, detailed and easy comparison of experimental protocols used in mouse phenotypic analyses among institutes or laboratories. Because SDOP-DB is fully compliant with international standards, it can act as a practical foundation for international sharing and integration of mouse phenotypic information. Availability: SDOP-DB (http://www.brc.riken.jp/lab/bpmp/SDOP/) Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.