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We present the new R package instrument to perform Bayesian estimation of person explanatory multidimensional item response theory. The package implements an exploratory multidimensional item response theory model and a higher-order multidimensional item response theory model, a type of confirmatory multidimensional item response theory. Explanation of person parameters is accomplished by fixed and random effect linear regression models. Estimation is carried out using Hamiltonian Monte Carlo in Stan. In this article, we provide a detailed description of the models; we use the instrument package to demonstrate fitting explanatory item response models with fixed and random effects (i.e., mixed modeling) of person parameters in R; and, we perform a simulation study to evaluate the performance of our implementation of the models., (© 2024. The Psychonomic Society, Inc.)

Background: Binary diagnostic tests are commonly used in medicine to answer a question about a patient's clinical status, most commonly, do they or do they not have some disease. Recent advances in statistical methodologies for performing inferential statistics to compare commonly used test metrics for two diagnostic tests have not yet been implemented in a statistical package., Methods: Up-to-date statistical methods to compare the test metrics achieved by two binary diagnostic tests are implemented in the new R package testCompareR. The output and efficiency of testCompareR is compared to the only other available package which performs this function, DTComPair, as well as an open-source program, compbdt, using a motivating example., Results: testCompareR achieves similar results to DTComPair using statistical methods with improved coverage and asymptotic performance. Further, testCompareR is faster than the currently available package and requires fewer pre-processing steps in order to produce accurate results., Conclusions: testCompareR provides a new tool to compare the test metrics for two binary diagnostic tests compared with the gold standard. This tool allows flexible inputs, which minimises the need for data pre-processing, and operates in very few steps, so that it is easy to use even for those less experienced with R. testCompareR achieves results comparable to those computed by DTComPair, using optimised statistical methods and with improved computational efficiency., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Wilson KJ et al.)

Hybrid zones, where genetically distinct groups of organisms meet and interbreed, offer valuable insights into the nature of species and speciation. Here, we present a new R package, bgchm, for population genomic analyses of hybrid zones. This R package extends and updates the existing bgc software and combines Bayesian analyses of hierarchical genomic clines with Bayesian methods for estimating hybrid indexes, interpopulation ancestry proportions, and geographic clines. Compared to existing software, bgchm offers enhanced efficiency through Hamiltonian Monte Carlo sampling and the ability to work with genotype likelihoods combined with a hierarchical Bayesian approach, enabling inference for diverse types of genetic data sets. The package also facilitates the quantification of introgression patterns across genomes, which is crucial for understanding reproductive isolation and speciation genetics. We first describe the models underlying bgchm and then provide an overview of the R package and illustrate its use through the analysis of simulated and empirical data sets. We show that bgchm generates accurate estimates of model parameters under a variety of conditions, especially when the genetic loci analyzed are highly ancestry informative. This includes relatively robust estimates of genome-wide variability in clines, which has not been the focus of previous models and methods. We also illustrate how both selection and genetic drift contribute to variability in introgression among loci and how additional information can be used to help distinguish these contributions. We conclude by describing the promises and limitations of bgchm, comparing bgchm to other software for genomic cline analyses, and identifying areas for fruitful future development., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.)

The Identity algorithm implemented in the MS Search (NIST) software is widely used for library searches of gas chromatography/mass spectrometry data against electron ionization mass spectral databases. It has been available to researchers since 1993, with the release of MS Search 1.5a. Despite its extensive use, the operational details of the algorithm have remained ambiguous. Attempts to replicate the algorithm have been unsuccessful because, as found in this research, the description in the manual is neither fully complete nor accurate. The main novelty of this work is the development of a unique approach for deconstructing the Identity algorithm. It is purely based on analyzing library search results obtained from several groups of synthetic mass spectra, each tailored to isolate and examine specific components of the algorithm. This approach facilitated a comprehensive understanding of the Identity algorithm and led to the development of a custom implementation that fully replicates the results obtained from the original MS Search software. The custom implementation of the Identity algorithm is now available in the mssearchr R package, enhancing accessibility for researchers.

R is widely regarded as unrivaled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative polymerase chain reaction (qPCR) and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Few standardized and open-source tools exist for calculating multiple dietary pattern indexes from dietary intake data in epidemiological and clinical studies., Objectives: The primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes., Methods: Dietaryindex can calculate multiple dietary indexes of high interest in research, including the Healthy Eating Index (HEI)-2020, the Alternative HEI 2010, the Dietary Approaches to Stop Hypertension Index, the Alternate Mediterranean Diet Score, the Dietary Inflammatory Index, the American Cancer Society 2020 dietary index, and the Planetary Health Diet Index from the EAT-Lancet Commission. The package includes generic dietary index calculation functions that accept any dietary assessment with preprocessed serving sizes of food groups and nutrients as defined by the research group that developed each index. For ease of use and to eliminate any need for data preprocessing, dietaryindex also offers 1-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES), the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24), the Diet History Questionnaire III (DHQ3), and the Block Food Frequency Questionnaire. At least 2 independent researchers validated the serving size definitions and scoring algorithms of dietaryindex., Results: In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with 2-decimal rounding precision when compared with hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI-2015 outputs from dietaryindex aligned (99.95%-100%) with results using the Statistical Analysis System codes from the National Cancer Institute., Conclusions: Dietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex)., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Methods involving summary statistics in genetics can be quite powerful but can be limited in utility. For instance, many post-hoc analyses of disease studies require case and control allele frequencies (AFs), which are not always published. We present two frameworks to derive case and control AFs from GWAS summary statistics using the odds ratio, case and control sample sizes, and either the total (case and control aggregated) AF or standard error (SE). In simulations and real data, derivations of case and controls AFs using total AF is highly accurate across all settings (e.g., minor AF, condition prevalence). Conversely, derivations using SE underestimate common variant AFs (e.g. minor allele frequency >0.3) in the presence of covariates. We develop an adjustment using gnomAD AFs as a proxy for true AFs, which reduces the bias when using SE. While estimating case and control AFs using the total AF is preferred due to its high accuracy, estimating from the SE can be used more broadly since SE can be derived from p-values and beta estimates, which are commonly provided. The methods provided here expand the utility of publicly available genetic summary statistics and promote the reusability of genomic data. The R package CCAFE, with implementations of both methods, is freely available on Bioconductor and GitHub.

The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure. Therefore, assuming orthogonality in the MMRM analysis is not preferable. However, without the assumption of orthogonality, the small-sample bias in the standard error of the treatment effect is significant. Nonetheless, there is no method to improve small-sample performance. Furthermore, there is no software that can easily implement inferences on treatment effects without assuming orthogonality. Hence, we propose two small-sample adjustment methods inflating standard errors that are reasonable in ideal situations and achieve empirical conservatism even in general situations. We also provide an R package to implement these inference processes. The simulation results show that one of the proposed small-sample adjustment methods performs particularly well in terms of underestimation bias of standard errors; consequently, the proposed method is recommended. When using the MMRM analysis, our proposed method is recommended if the sample size is not large and between-group heteroscedasticity is expected.

Background and Objective: The primary cilium is a small protrusion found on most mammalian cells. It acts as a cellular antenna, being involved in various cell signaling pathways. The length of the primary cilium affects its function. To study the impact of physical or chemical stimuli on cilia, their lengths must be determined easily and reproducibly., Methods: We have developed and evaluated an open-source R package called detectCilia to detect and measure primary cilia automatically. As a case study to demonstrate the capability of our tool, we compared the influence of 4 different cell culture media compositions on the lengths of primary cilia in human chondrocytes. These media compositions include (1) insulin-transferrin-selenium (ITS); (2) ITS and dexamethasone (Dexa); (3) ITS, Dexa, insulin-like growth factor 1 (IGF-1), and transforming growth factor beta 1 (TGF-β1); and (4) fetal bovine serum (FBS)., Results: The assessment of detectCilia included a comparison with 2 similar tools: ACDC (Automated Cilia Detection in Cells) and CiliaQ. Several differences and advantages of our package make it a valuable addition to these tools. In the case study, we have observed variations in the ciliary lengths associated with using different media compositions., Conclusions: We conclude that detectCilia can automatically and reproducibly detect and measure primary cilia in confocal microscopy images with low false-positive rates without requiring extensive user interaction., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea . It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms., (© 2024. The Author(s).)

Background/objectives: We present hidecan, an R package for generating visualisations that summarise the results of one or more genome-wide association studies (GWAS) and differential expression analyses, as well as manually curated candidate genes, e.g., extracted from the literature. This tool is applicable to all ploidy levels; we notably provide functionalities to facilitate the visualisation of GWAS results obtained for autotetraploid organisms with the GWASpoly package., Results: We illustrate the capabilities of hidecan with examples from two autotetraploid potato datasets., Conclusions: The hidecan package is implemented in R and is publicly available on the CRAN repository and on GitHub. A description of the package, as well as a detailed tutorial, is made available alongside the package. It is also part of the VIEWpoly tool for the visualisation and exploration of results from polyploids computational tools.

Background: Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains., Results: We present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies., Conclusions: Simona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems., (© 2024. The Author(s).)

As plant research generates an ever-growing volume of spatial quantitative data, the need for decentralized and user-friendly visualization tools to explore large and complex datasets becomes crucial. Existing resources, such as the Plant eFP (electronic Fluorescent Pictograph) viewer, have played a pivotal role on the communication of gene expression data across many plant species. However, although widely used by the plant research community, the Plant eFP viewer lacks open and user-friendly tools for the creation of customized expression maps independently. Plant biologists with less coding experience can often encounter challenges when attempting to explore ways to communicate their own spatial quantitative data. We present 'ggPlantmap' an open-source R package designed to address this challenge by providing an easy and user-friendly method for the creation of ggplot representative maps from plant images. ggPlantmap is built in R, one of the most used languages in biology, to empower plant scientists to create and customize eFP-like viewers tailored to their experimental data. Here, we provide an overview of the package and tutorials that are accessible even to users with minimal R programming experience. We hope that ggPlantmap can assist the plant science community, fostering innovation, and improving our understanding of plant development and function., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families., (© 2024. The Author(s).)

Rvisdiff is an R/Bioconductor package that generates an interactive interface for the interpretation of differential expression results. It creates a local web page that enables the exploration of statistical analysis results through the generation of auto-analytical visualizations. Users can explore the differential expression results and the source expression data interactively in the same view. As input, the package supports the results of popular differential expression packages such as DESeq2, edgeR, and limma. As output, the package generates a local HTML page that can be easily viewed in a web browser. Rvisdiff is freely available at https://bioconductor.org/packages/Rvisdiff/., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Barrios and Prieto.)

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Background: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network., Results: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments., Conclusions: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias., (© 2024. The Author(s).)

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma , an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma 's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

COVID-19 caused by SARS-CoV-2 has resulted in a global pandemic with a rapidly developing global health and economic crisis. Variations in the disease have been observed and have been associated with the genomic sequence of either the human host or the pathogen. Worldwide scientists scrambled initially to recruit patient cohorts to try and identify risk factors. A resource that presented itself early on was the UK Biobank (UKBB), which is investigating the respective contributions of genetic predisposition and environmental exposure to the development of disease. To enable COVID-19 studies, UKBB is now receiving COVID-19 test data for their participants every two weeks. In addition, UKBB is delivering more frequent updates of death and hospital inpatient data (including critical care admissions) on the UKBB Data Portal. This frequently changing dataset requires a tool that can rapidly process and analyse up-to-date data. We developed an R package specifically for the UKBB COVID-19 data, which summarises COVID-19 test results, performs association tests between COVID-19 susceptibility/severity and potential risk factors such as age, sex, blood type, comorbidities and generates input files for genome-wide association studies (GWAS). By applying the R package to data released in April 2021, we found that age, body mass index, socioeconomic status and smoking are positively associated with COVID-19 susceptibility, severity, and mortality. Males are at a higher risk of COVID-19 infection than females. People staying in aged care homes have a higher chance of being exposed to SARS-CoV-2. By performing GWAS, we replicated the 3p21.31 genetic finding for COVID-19 susceptibility and severity. The ability to iteratively perform such analyses is highly relevant since the UKBB data is updated frequently. As a caveat, users must arrange their own access to the UKBB data to use the R package., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Wang L et al.)

Objectives: Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies., Target Audience: All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package., Scope: We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Inbreeding and relationship coefficients are essential for conservation and breeding programs. Whether dealing with a small conserved population or a large commercial population, monitoring the inbreeding rate and designing mating plans that minimize the inbreeding rate and maximize the effective population size is important. Free, open-source, and efficient software may greatly contribute to conservation and breeding programs and help students and researchers. Efficient methods exist for calculating inbreeding coefficients. Therefore, an efficient way of calculating the numerator relationship coefficients is via the inbreeding coefficients. i.e., the relationship coefficient between parents is twice the inbreeding coefficient of their progeny. A dummy progeny is introduced where no progeny exists for a pair of individuals. Calculating inbreeding coefficients is very fast, and finding whether a pair of individuals has a progeny and picking one from multiple progenies is computationally more demanding. Therefore, the R package introduces a dummy progeny for any pair of individuals whose relationship coefficient is of interest, whether they have a progeny or not., Results: Runtime and peak memory usage were benchmarked for calculating relationship coefficients between two sets of 250 and 800 animals (200,000 dummy progenies) from a pedigree of 2,721,252 animals. The program performed efficiently (200,000 relationship coefficients, which involved calculating 2,721,252 + 200,000 inbreeding coefficients) within 3:45 (mm:ss). Providing the inbreeding coefficients (for real animals), the runtime was reduced to 1:08. Furthermore, providing the diagonal elements of D in A = TDT ' (d), the runtime was reduced to 54s. All the analyses were performed on a machine with a total memory size of 1 GB., Conclusions: The R package FnR is free and open-source software with implications in conservation and breeding programs. It proved to be time and memory efficient for large populations and many dummy progenies. Calculation of inbreeding coefficients can be resumed for new animals in the pedigree. Thus, saving the latest inbreeding coefficient estimates is recommended. Calculation of d coefficients (from scratch) was very fast, and there was limited value in storing those for future use., (© 2024. The Author(s).)

Background: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves., Methods: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves., Results: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement., Conclusions: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ ., (© 2024. The Author(s).)

Estimating sample size and statistical power is an essential part of a good epidemiological study design. Closed-form formulas exist for simple hypothesis tests but not for advanced statistical methods designed for exposure mixture studies. Estimating power with Monte Carlo simulations is flexible and applicable to these methods. However, it is not straightforward to code a simulation for non-experienced programmers and is often hard for a researcher to manually specify multivariate associations among exposure mixtures to set up a simulation. To simplify this process, we present the R package mpower for power analysis of observational studies of environmental exposure mixtures involving recently-developed mixtures analysis methods. The components within mpower are also versatile enough to accommodate any mixtures methods that will developed in the future. The package allows users to simulate realistic exposure data and mixed-typed covariates based on public data set such as the National Health and Nutrition Examination Survey or other existing data set from prior studies. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples of power analysis using mpower., Competing Interests: Competing interests The authors have no competing interests to declare that are relevant to the content of this article.

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime)., Competing Interests: All authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

Background: The functional coupling between alternative pre-mRNA splicing (AS) and the mRNA quality control mechanism called nonsense-mediated decay (NMD) can modulate transcript abundance. Previous studies have identified several examples of such a regulation in developing neurons. However, the systems-level effects of AS-NMD in this context are poorly understood., Results: We developed an R package, factR2, which offers a comprehensive suite of AS-NMD analysis functions. Using this tool, we conducted a longitudinal analysis of gene expression in pluripotent stem cells undergoing induced neuronal differentiation. Our analysis uncovers hundreds of AS-NMD events with significant potential to regulate gene expression. Notably, this regulation is significantly overrepresented in specific functional groups of developmentally downregulated genes. Particularly strong association with gene downregulation is detected for alternative cassette exons stimulating NMD upon their inclusion into mature mRNA. By combining bioinformatic analyses with CRISPR/Cas9 genome editing and other experimental approaches we show that NMD-stimulating cassette exons regulated by the RNA-binding protein PTBP1 dampen the expression of their genes in developing neurons. We also provided evidence that the inclusion of NMD-stimulating cassette exons into mature mRNAs is temporally coordinated with NMD-independent gene repression mechanisms., Conclusions: Our study provides an accessible workflow for the discovery and prioritization of AS-NMD targets. It further argues that the AS-NMD pathway plays a widespread role in developing neurons by facilitating the downregulation of functionally related non-neuronal genes., (© 2024. The Author(s).)

The analysis of photosynthetic traits has become an integral part of plant (eco-)physiology. Many of these characteristics are not directly measured, but calculated from combinations of several, more direct, measurements. The calculations of such derived variables are based on underlying physical models and may use additional constants or assumed values. Commercially available gas-exchange instruments typically report such derived variables, but the available implementations use different definitions and assumptions. Moreover, no software is currently available to allow a fully scripted and reproducible workflow that includes importing data, pre-processing and recalculating derived quantities. The R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations. In addition, the package facilitates performing sensitivity analyses on variables or assumptions used in the calculations to allow researchers to better assess the robustness of the results. The use of the package and how to perform sensitivity analyses are demonstrated using three different examples., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Annals of Botany Company.)

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Combining pertinent data from multiple studies can increase the robustness of epidemiological investigations. Effective "pre-statistical" data harmonization is paramount to the streamlined conduct of collective, multi-study analysis. Harmonizing data and documenting decisions about the transformations of variables to a common set of categorical values and measurement scales are time consuming and can be error prone, particularly for numerous studies with large quantities of variables. The psHarmonize R package facilitates harmonization by combining multiple datasets, applying data transformation functions, and creating long and wide harmonized datasets. The user provides transformation instructions in a "harmonization sheet" that includes dataset names, variable names, and coding instructions and centrally tracks all decisions. The package performs harmonization, generates error logs as necessary, and creates summary reports of harmonized data. psHarmonize is poised to serve as a central feature of data preparation for the joint analysis of multiple studies., Competing Interests: N.B.A. receives funding from the National Institutes of Health and the American Heart Association. D.M.S. receives funding from the National Institutes of Health., (© 2024 The Authors.)

Background and Objective: There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset., Methods: The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively., Results: The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results., Conclusions: Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes., Competing Interests: Declaration of competing interest We declare that all authors have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: The prevalence of comorbid pain and Bipolar Disorder in clinical practice continues to be high, with an increasing number of related publications. However, no study has used bibliometric methods to analyze the research progress and knowledge structure in this field. Our research is dedicated to systematically exploring the global trends and focal points in scientific research on pain comorbidity with bipolar disorder from 2003 to 2023, with the goal of contributing to the field., Methods: Relevant publications in this field were retrieved from the Web of Science core collection database (WOSSCC). And we used VOSviewer, CiteSpace, and the R package "Bibliometrix" for bibliometric analysis., Results: A total of 485 publications (including 360 articles and 125 reviews) from 66 countries, 1019 institutions, were included in this study. Univ Toront and Kings Coll London are the leading research institutions in this field. J Affect Disorders contributed the largest number of articles, and is the most co-cited journal. Of the 2,537 scholars who participated in the study, Stubbs B, Vancampfort D, and Abdin E had the largest number of articles. Stubbs B is the most co-cited author. "chronic pain," "neuropathic pain," "psychological pain" are the keywords in the research., Conclusion: This is the first bibliometric analysis of pain-related bipolar disorder. There is growing interest in the area of pain and comorbid bipolar disorder. Focusing on different types of pain in bipolar disorder and emphasizing pain management in bipolar disorder are research hotspots and future trends. The study of pain related bipolar disorder still has significant potential for development, and we look forward to more high-quality research in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhao, Zhou, Jiang, Luo and Wang.)

Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information., Competing Interests: Declaration of interests M.A.D. has been a member of advisory boards for GSK, CTX CRC, Storm Therapeutics, Celgene, and Cambridge Epigenetix. The Dawson laboratory is a recipient of grant funding from the emerging sciences fund administered through Pfizer., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade-offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long-table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI-BGC-Functional-Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Systematic reviews (SRs) have an important role in the healthcare decision-making practice. Assessing the overall confidence in the results of SRs using quality assessment tools, such as "A MeaSurement Tool to Assess Systematic Reviews 2" (AMSTAR 2), is crucial since not all SRs are conducted using the most rigorous methods. In this article, we introduce a free, open-source R package called "amstar2Vis" (https://github.com/bougioukas/amstar2Vis) that provides easy-to-use functions for presenting the critical appraisal of SRs, based on the items of AMSTAR 2 checklist. An illustrative example is outlined, describing the steps involved in creating a detailed table with the item ratings and the overall confidence ratings, generating a stacked bar plot that shows the distribution of ratings as percentages of SRs for each AMSTAR 2 item, and creating a "ggplot2" graph that shows the distribution of overall confidence ratings ("Critically Low," "Low," "Moderate," or "High"). We expect "amstar2Vis" to be useful for overview authors and methodologists who assess the quality of SRs with AMSTAR 2 checklist and facilitate the production of pertinent publication-ready tables and figures. Future research and applications could further investigate the functionality or potential improvements of our package., (© 2024 John Wiley & Sons Ltd.)

RNA sequencing (RNA-seq) technology has become one of the standard tools for studying biological mechanisms at the transcriptome level. Advances in RNA-seq technology have led to the development of numerous publicly available tools for RNA-seq data analysis. Most of these tools target linear genome sequences despite the necessity of studying organisms with circular genome sequences. For example, studying the infection mechanisms of viroids which comprise 246-401 nucleotides circular RNAs and target plants may prevent tremendous economic and agricultural damage. Unfortunately, using the available tools to construct workflows for the analysis of circular genome sequences is difficult, especially for non-bioinformaticians. To overcome this limitation, we present CircSeqAlignTk, an easy-to-use and richly documented R package. CircSeqAlignTk offers both command line and graphical user interfaces for end-to-end RNA-seq data analysis, spanning alignment to the visualisation of circular genome sequences, via a series of functions. Moreover, it includes a feature to generate synthetic sequencing data that mirrors real RNA-seq data from biological experiments. CircSeqAlignTk not only provides an easy-to-use analysis interface for novice users but also allows developers to evaluate the performance of alignment tools and new workflows., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Sun J et al.)

Background: Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred., Results: We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations., Conclusions: Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses., (© 2024. The Author(s).)

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Background/aims: Protecting patient safety is an essential component of the conduct of clinical trials. Rigorous safety monitoring schemes are implemented for these studies to guard against excess toxicity risk from study therapies. They often include protocol-specified stopping rules dictating that an excessive number of safety events will trigger a halt of the study. Statistical methods are useful for constructing rules that protect patients from exposure to excessive toxicity while also maintaining the chance of a false safety signal at a low level. Several statistical techniques have been proposed for this purpose, but the current literature lacks a rigorous comparison to determine which method may be best suitable for a given trial design. The aims of this article are (1) to describe a general framework for repeated monitoring of safety events in clinical trials; (2) to survey common statistical techniques for creating safety stopping criteria; and (3) to provide investigators with a software tool for constructing and assessing these stopping rules., Methods: The properties and operating characteristics of stopping rules produced by Pocock and O'Brien-Fleming tests, Bayesian Beta-Binomial models, and sequential probability ratio tests (SPRTs) are studied and compared for common scenarios that may arise in phase II and III trials. We developed the R package "stoppingrule" for constructing and evaluating stopping rules from these methods. Its usage is demonstrated through a redesign of a stopping rule for BMT CTN 0601 (registered at Clinicaltrials.gov as NCT00745420), a phase II, single-arm clinical trial that evaluated outcomes in pediatric sickle cell disease patients treated by bone marrow transplant., Results: Methods with aggressive stopping criteria early in the trial, such as the Pocock test and Bayesian Beta-Binomial models with weak priors, have permissive stopping criteria at late stages. This results in a trade-off where rules with aggressive early monitoring generally will have a smaller number of expected toxicities but also lower power than rules with more conservative early stopping, such as the O-Brien-Fleming test and Beta-Binomial models with strong priors. The modified SPRT method is sensitive to the choice of alternative toxicity rate. The maximized SPRT generally has a higher number of expected toxicities and/or worse power than other methods., Conclusions: Because the goal is to minimize the number of patients exposed to and experiencing toxicities from an unsafe therapy, we recommend using the Pocock or Beta-Binomial, weak prior methods for constructing safety stopping rules. At the design stage, the operating characteristics of candidate rules should be evaluated under various possible toxicity rates in order to guide the choice of rule(s) for a given trial; our R package facilitates this evaluation., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Many genetic models (including models for epistatic effects as well as genetic-by-environment) involve covariance structures that are Hadamard products of lower rank matrices. Implementing these models requires factorizing large Hadamard product matrices. The available algorithms for factorization do not scale well for big data, making the use of some of these models not feasible with large sample sizes. Here, based on properties of Hadamard products and (related) Kronecker products, we propose an algorithm that produces an approximate decomposition that is orders of magnitude faster than the standard eigenvalue decomposition. In this article, we describe the algorithm, show how it can be used to factorize large Hadamard product matrices, present benchmarks, and illustrate the use of the method by presenting an analysis of data from the northern testing locations of the G × E project from the Genomes to Fields Initiative (n ∼ 60,000). We implemented the proposed algorithm in the open-source "tensorEVD" R package., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the metamedian R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19., (© 2023 John Wiley & Sons Ltd.)

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier ., (© 2023. The Author(s).)

Background: Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as an N × N distance matrix based on posterior decodings., Results: We provide a high-performance engine to make these posterior decodings readily accessible with minimal pre-processing via an easy to use package kalis, in the statistical programming language R. kalis enables investigators to rapidly resolve the ancestry at loci of interest and developers to build a range of variant-specific ancestral inference pipelines on top. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to hundreds of thousands of genomes., Conclusions: The resulting distance matrices accessible via kalis enable local ancestry, selection, and association studies in modern large scale genomic datasets., (© 2024. The Author(s).)

Understanding the distribution of herbivore damage among leaves and individual plants is a central goal of plant-herbivore biology. Commonly observed unequal patterns of herbivore damage have conventionally been attributed to the heterogeneity in plant quality or herbivore behaviour or distribution. Meanwhile, the potential role of stochastic processes in structuring plant-herbivore interactions has been overlooked. Here, we show that based on simple first principle expectations from metabolic theory, random sampling of different sizes of herbivores from a regional pool is sufficient to explain patterns of variation in herbivore damage. This is despite making the neutral assumption that herbivory is caused by randomly feeding herbivores on identical and passive plants. We then compared its predictions against 765 datasets of herbivory on 496 species across 116° of latitude from the Herbivory Variability Network. Using only one free parameter, the estimated attack rate, our neutral model approximates the observed frequency distribution of herbivore damage among plants and especially among leaves very well. Our results suggest that neutral stochastic processes play a large and underappreciated role in natural variation in herbivory and may explain the low predictability of herbivory patterns. We argue that such prominence warrants its consideration as a powerful force in plant-herbivore interactions.

Given the increasing prevalence of tick-borne diseases, such as Lyme disease, modeling the population and infection dynamics of tick vectors is an important public health tool. These models have applications for testing the effects of control methods or climate change on tick populations. There is an established history of tick population models, but code for them is rarely shared, especially not in a convenient format for others to modify and use. We present an R package, called IxPopDyMod, intended to function as a flexible and consistent framework for reproducible Ixodidae (hard-bodied ticks) population dynamics models. Here we focus on two key parts of the package: a function to create valid model configurations and a function to run a configured model and return the daily population over time. We provide three examples in appendices: one reproducing an existing Ixodes scapularis population model, one providing a novel Dermacentor albipictus model, and one showing Borrelia burgdorferi infection in ticks. Together these examples show the flexibility of the package to model scenarios of interest to tick researches., (© 2024. The Author(s).)

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR., (© The Author(s) 2023. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.)

Recent technological advances in sequencing DNA and RNA modifications using high-throughput platforms have generated vast epigenomic and epitranscriptomic datasets whose power in transforming life science is yet fully unleashed. Currently available in silico methods have facilitated the identification, positioning and quantitative comparisons of individual modification sites. However, the essential challenge to link specific 'epi-marks' to gene expression in the particular context of cellular and biological processes is unmet. To fast-track exploration, we generated epidecodeR implemented in R, which allows biologists to quickly survey whether an epigenomic or epitranscriptomic status of their interest potentially influences gene expression responses. The evaluation is based on the cumulative distribution function and the statistical significance in differential expression of genes grouped by the number of 'epi-marks'. This tool proves useful in predicting the role of H3K9ac and H3K27ac in associated gene expression after knocking down deacetylases FAM60A and SDS3 and N6-methyl-adenosine-associated gene expression after knocking out the reader proteins. We further used epidecodeR to explore the effectiveness of demethylase FTO inhibitors and histone-associated modifications in drug abuse in animals. epidecodeR is available for downloading as an R package at https://bioconductor.riken.jp/packages/3.13/bioc/html/epidecodeR.html., (Published by Oxford University Press 2024.)

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier . The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Immunofluorescent staining is commonly used to generate images to characterize cytological phenotypes. The manual quantification of DNA double-strand breaks and their repair intermediates during meiosis using image data requires a series of subjective steps, from image selection to the counting of particular events per nucleus. Here we describe "synapsis," a bioconductor package, which includes a set of functions to automate the process of identifying meiotic nuclei and quantifying key double-strand break formation and repair events in a rapid, scalable, and reproducible workflow, and compare it to manual user quantification. The software can be extended for other applications in meiosis research, such as incorporating machine learning approaches to categorize meiotic substages., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)