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1. MR.RGM: an R package for fitting Bayesian multivariate bidirectional Mendelian randomization networks

   https://doi.org/10.1093/bioinformatics/btaf130  

   Sarkar, Bitan; Ni, Yang (March 2025, Bioinformatics)

   Cowen, Lenore (Ed.)

   Abstract MotivationMendelian randomization (MR) infers causal relationships between exposures and outcomes using genetic variants as instrumental variables. Typically, MR considers only a pair of exposure and outcome at a time, limiting its capability of capturing the entire causal network. We overcome this limitation by developing MR.RGM (Mendelian randomization via reciprocal graphical model), a fast R-package that implements the Bayesian reciprocal graphical model and enables practitioners to construct holistic causal networks with possibly cyclic/reciprocal causation and proper uncertainty quantifications, offering a comprehensive understanding of complex biological systems and their interconnections. ResultsWe developed MR.RGM, an open-source R package that applies bidirectional MR using a network-based strategy, enabling the exploration of causal relationships among multiple variables in complex biological systems. MR.RGM holds the promise of unveiling intricate interactions and advancing our understanding of genetic networks, disease risks, and phenotypic complexities. Availability and implementationMR.RGM is available at CRAN (https://CRAN.R-project.org/package=MR.RGM, DOI: 10.32614/CRAN.package.MR.RGM) and https://github.com/bitansa/MR.RGM. 

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2. Fast multiple sequence alignment via multi-armed bandits

   https://doi.org/10.1093/bioinformatics/btae225  

   Mazooji, Kayvon; Shomorony, Ilan (June 2024, Bioinformatics)

   Abstract SummaryMultiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementationThe code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. 

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3. PlantSimLab - a modeling and simulation web tool for plant biologists

   https://doi.org/10.1186/s12859-019-3094-9  

   Ha, S; Dimitrova, E; Hoops, S; Altarawy, D; Ansariola, M; Deb, D; Glazebrook, J; Hillmer, R; Shahin, H; Katagiri, F; et al (December 2019, BMC Bioinformatics)

   Abstract BackgroundAt the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. ResultsThis paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. ConclusionsMathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise. 

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4. A supervised Bayesian factor model for the identification of multi-omics signatures

   https://doi.org/10.1093/bioinformatics/btae202  

   Gygi, Jeremy P; Konstorum, Anna; Pawar, Shrikant; Aron, Edel; Kleinstein, Steven H; Guan, Leying (May 2024, Bioinformatics)

   Kendziorski, Christina (Ed.)

   Abstract MotivationPredictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. ResultsWe developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes. Availability and implementationSPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR. 

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5. modSaRa: a computationally efficient R package for CNV identification

   https://doi.org/10.1093/bioinformatics/btx212  

   Xiao, Feifei; Niu, Yue; Hao, Ning; Xu, Yanxun; Jin, Zhilin; Zhang, Heping (August 2017, Bioinformatics)

   Hancock, John (Ed.)

   Abstract SummaryChromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps. Availability and ImplementationmodSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa. Supplementary informationSupplementary data are available at Bioinformatics online. 

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