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1. Bayesian graphical models for modern biological applications

   https://doi.org/10.1007/s10260-021-00572-8  

   Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C. (May 2021, Statistical Methods & Applications)

   Abstract Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging. 

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2. Latent Model-Based Clustering for Biological Discovery

   https://doi.org/https://doi.org/10.1016/j.isci.2019.03.018  

   Bing, X; Bunea, F; Royer, M; Das, J. (March 2019, iScience)

   LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters. In our formulation, a cluster comprises variables associated with the same latent factor and is determined from an allocation matrix that indexes our latent model. We prove that the allocation matrix and corresponding clusters are uniquely defined. We apply LOVE to biological datasets (gene expression, serological responses measured from HIV controllers and chronic progressors, vaccine-induced humoral immune responses) resulting in meaningful biological output. For all three datasets, the clusters generated by LOVE remain stable across tuning parameters. Finally, we compared LOVE's performance to that of 13 state-of-the-art methods using previously established benchmarks and found that LOVE outperformed these methods across datasets. Our results demonstrate that LOVE can be broadly used across large-scale biological datasets to generate accurate and meaningful overlapping and non-overlapping clusters. 

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3. Genomic Tools in Biological Invasions: Current State and Future Frontiers

   https://doi.org/10.1093/gbe/evad230  

   McGaughran, Angela; Dhami, Manpreet K; Parvizi, Elahe; Vaughan, Amy L; Gleeson, Dianne M; Hodgins, Kathryn A; Rollins, Lee A; Tepolt, Carolyn K; Turner, Kathryn G; Atsawawaranunt, Kamolphat; et al (January 2024, Genome Biology and Evolution)

   Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success. Here, we consider the ability of genomic tools and technologies to (i) inform mechanistic understanding of biological invasions and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community. 

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4. signatureSearch : environment for gene expression signature searching and functional interpretation

   https://doi.org/10.1093/nar/gkaa878  

   Duan, Yuzhu; Evans, Daniel S; Miller, Richard A; Schork, Nicholas J; Cummings, Steven R; Girke, Thomas (October 2020, Nucleic Acids Research)

   null (Ed.)

   Abstract signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases. 

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5. A teaching and training framework to promote findable, accessible, interoperable, and reusable data generation in agriculture

   https://doi.org/10.1093/database/baaf034  

   Marrano, Annarita; Cabugos, Leyla; Hafner, Alenka; Kapoor, Beant; McNamara, John; O’Donnell, Megan; Reiser, Leonore; Tello-Ruiz, Marcela_Karey; Zhang, Huiting; Staton, Margaret (April 2025, Database)

   Abstract Advances in agricultural genetic, genomic, and breeding (GGB) technologies generate increasingly large and complex datasets that need to be adequately managed and shared. While several agricultural biological databases maintain and curate GGB data, not all scientists are aware of them and how they can be used to access and share data. In addition, there is the need to increase scientists’ awareness that appropriate data archiving and curation increases data longevity and value and bolsters scientific discoveries’ reproducibility and transparency. The AgBioData Education working group aims to address these unmet needs and developed a modular curriculum for educators teaching the basics of biological databases and the findable, accessible, interoperable, and reusable (FAIR) principles to undergraduate and graduate students (https://www.agbiodata.org/). The present paper provides an overview of the topics covered within the curriculum, called ‘AgBioData Curriculum for Ag FAIR Data,’ its audience and modalities, and how it will positively impact all the different stakeholders of the agricultural database ecosystem. We hope the modular curriculum presented here can help scientists and students understand and support database use in all aspects of improving our global food system. Database URL: https://zenodo.org/records/14278084 

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