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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.

Additional file 1: Figure S1. Correlation distribution between node degree and rank score. Figure S2. Sensitivity and specificity for MP and MeSH terms. Figure S3. Sensitivity for network and ontology method. Figure S4. AUC values for different dumping factors. X axis is dumping factor and Y axis is AUC value. To calculate AUC across different dumping factor, we selected the top 10% genes from the prediction result. ROC analysis was performed using the R ROCR package.