MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

INTRODUCTION: Ultradense peptide binding arrays that can probe millions of linear peptides comprising the entire proteomes of human or mouse, or hundreds of thousands of microbes, are powerful tools for studying the antibody repertoire in serum samples to understand adaptive immune responses. MOTIVATION: There are few tools for exploring high-dimensional, significant and reproducible antibody targets for ultradense peptide binding arrays at the linear peptide, epitope (grouping of adjacent peptides), and protein level across multiple samples/subjects (i.e. epitope spread or immunogenic regions of proteins) for understanding the heterogeneity of immune responses. RESULTS: We developed Hierarchical antibody binding Epitopes and pROteins from liNear peptides (HERON), an R package, which can identify immunogenic epitopes, using meta-analyses and spatial clustering techniques to explore antibody targets at various resolution and confidence levels, that can be found consistently across a specified number of samples through the entire proteome to study antibody responses for diagnostics or treatment. Our approach estimates significance values at the linear peptide (probe), epitope, and protein level to identify top candidates for validation. We tested the performance of predictions on all three levels using correlation between technical replicates and comparison of epitope calls on two datasets, and results showed HERON's competitiveness in estimating false discovery rates and finding general and sample-level regions of interest for antibody binding. AVAILABILITY AND IMPLEMENTATION: The HERON R package is available at Bioconductor https://bioconductor.org/packages/release/bioc/html/HERON.html.

MOTIVATION: Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. RESULTS: We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms. AVAILABILITY AND IMPLEMENTATION: The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

SUMMARY: Peptide microarrays have emerged as a powerful technology in immunoproteomics as they provide a tool to measure the abundance of different antibodies in patient serum samples. The high dimensionality and small sample size of many experiments challenge conventional statistical approaches, including those aiming to control the false discovery rate (FDR). Motivated by limitations in reproducibility and power of current methods, we advance an empirical Bayesian tool that computes local FDR statistics and local false sign rate statistics when provided with data on estimated effects and estimated standard errors from all the measured peptides. As the name suggests, the MixTwice tool involves the estimation of two mixing distributions, one on underlying effects and one on underlying variance parameters. Constrained optimization techniques provide for model fitting of mixing distributions under weak shape constraints (unimodality of the effect distribution). Numerical experiments show that MixTwice can accurately estimate generative parameters and powerfully identify non-null peptides. In a peptide array study of rheumatoid arthritis, MixTwice recovers meaningful peptide markers in one case where the signal is weak, and has strong reproducibility properties in one case where the signal is strong. AVAILABILITYAND IMPLEMENTATION: MixTwice is available as an R software package https://cran.r-project.org/web/packages/MixTwice/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case- control study, garnering 49 mammography descriptors and 77 high- frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and $\ell^p$ ($1\leq p\leq2$) fusion penalty to improve breast cancer risk prediction, taking into account structure information in mammography descriptors and SNPs. We demonstrate that our method provides benefits that are both statistically significant and potentially significant to people's lives.

Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Transcriptional regulation plays an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We try to discover how genes interact when submitted to stress by exploring techniques of gene expression data analysis. We use several types of data, including high-throughput data. These results will help us recreate plausible regulatory networks by using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.