NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell's output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations.

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers' ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

Motivation: Genetic reporter assays are a convenient, relatively inexpensive method for studying the regulation of gene expression. Massively Parallel Reporter Assays (MPRA) are high-throughput functionalization assays that interrogate the transcriptional activity of many genetic variants at once using a library of synthetic barcoded constructs. Despite growing interest in this area, there are few computational tools to design and execute MPRA studies. Results: We designed an online web-tool and R package that allows for interactive MPRA experimental design encompassing both power analysis and design of constructs. Our tool is tuned using data from real MPRA studies. Users can adjust experimental parameters to examine the predicted effect on assay power as well as upload VCFs for automated construct sequence generation. Availability and implementation: The MPRA Design Tools web application is available here: https://andrewghazi.shinyapps.io/designmpra/, https://github.com/andrewGhazi/designMPRA and https://github.com/andrewGhazi/mpradesigntools. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Set-based network similarity metrics are increasingly used to productively analyze genome-wide data. Conventional approaches, such as mean shortest path and clique-based metrics, have been useful but are not well suited to all applications. Computational scientists in other disciplines have developed communicability as a complementary metric. Network communicability considers all paths of all lengths between two network members. Given the success of previous network analyses of protein-protein interactions, we applied the concepts of network communicability to this problem. Here we show that our communicability implementation has advantages over traditional approaches. Overall, analyses suggest network communicability has considerable utility in analysis of large-scale biological networks. AVAILABILITY AND IMPLEMENTATION: We provide our method as an R package for use in both human protein-protein interaction network analyses and analyses of arbitrary networks along with a tutorial at http://www.shawlab.org/NetComm/.

The Dirichlet-multinomial (DMN) distribution is a fundamental model for multicategory count data with overdispersion. This distribution has many uses in bioinformatics including applications to metagenomics data, transctriptomics and alternative splicing. The DMN distribution reduces to the multinomial distribution when the overdispersion parameter ψ is 0. Unfortunately, numerical computation of the DMN log-likelihood function by conventional methods results in instability in the neighborhood of [Formula: see text]. An alternative formulation circumvents this instability, but it leads to long runtimes that make it impractical for large count data common in bioinformatics. We have developed a new method for computation of the DMN log-likelihood to solve the instability problem without incurring long runtimes. The new approach is composed of a novel formula and an algorithm to extend its applicability. Our numerical experiments show that this new method both improves the accuracy of log-likelihood evaluation and the runtime by several orders of magnitude, especially in high-count data situations that are common in deep sequencing data. Using real metagenomic data, our method achieves manyfold runtime improvement. Our method increases the feasibility of using the DMN distribution to model many high-throughput problems in bioinformatics. We have included in our work an R package giving access to this method and a vingette applying this approach to metagenomic data.

UNLABELLED: Many important data in current biological science comprise hundreds, thousands or more individual results. These massive data require computational tools to navigate results and effectively interact with the content. Mobile device apps are an increasingly important tool in the everyday lives of scientists and non-scientists alike. These software present individuals with compact and efficient tools to interact with complex data at meetings or other locations remote from their main computing environment. We believe that apps will be important tools for biologists, geneticists and physicians to review content while participating in biomedical research or practicing medicine. We have developed a prototype app for displaying gene expression data using the iOS platform. To present the software engineering requirements, we review the model-view-controller schema for Apple's iOS. We apply this schema to a simple app for querying locally developed microarray gene expression data. The challenge of this application is to balance between storing content locally within the app versus obtaining it dynamically via a network connection. AVAILABILITY: The Hematopoietic Expression Viewer is available at http://www.shawlab.org/he_viewer. The source code for this project and any future information on how to obtain the app can be accessed at http://www.shawlab.org/he_viewer.

UNLABELLED: Gene Ontology (GO) annotations have become a major tool for analysis of genome-scale experiments. We have created OntologyTraverser--an R package for GO analysis of gene lists. Our system is a major advance over previous work because (1) the system can be installed as an R package, (2) the system uses Java to instantiate the GO structure and the SJava system to integrate R and Java and (3) the system is also deployed as a publicly available web tool. AVAILABILITY: Our software is academically available through http://franklin.imgen.bcm.tmc.edu/OntologyTraverser/. Both the R package and the web tool are accessible. CONTACT: [email protected]