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6 recordsLinked to original sources
PubMed2026

shinyDeepGxP: a user-friendly R shiny app for predicting surface protein abundance from scRNA-seq expression using deep learning in blood cells.

MOTIVATION: Understanding accurate immune cell heterogeneity and function in single-cell datasets requires access to protein-level information, which is often unavailable due to experimental limitations. RESULTS: We present shinyDeepGxP, an interactive web application featuring our deep learning model, DeepGxP, for predicting surface protein abundance from single-cell RNA-sequencing (scRNA-seq) data. This platform makes DeepGxP accessible to researchers without programming skills. Users can upload scRNA-seq count matrices and use "Predict Protein" to predict the abundance of 224 biologically relevant surface proteins. shinyDeepGxP provides visualizations to help identify distinct cell populations based on predicted protein profiles. Moreover, users can choose "Explore Model" to reveal key RNA predictors and their associated biological pathways for each protein. Overall, shinyDeepGxP is a user-friendly, freely available web tool that provides protein-level detail for RNA-only single-cell datasets, enabling multimodal discovery without additional experiments. AVAILABILITY AND IMPLEMENTATION: shinyDeepGxP can be launched on https://shiny.crc.pitt.edu/deepgxp/.

Journal Article↗
PubMed2026

Assessing the influence of different alignment tools on the accuracy of a forensic epigenetic clock.

MOTIVATION: DNA methylation (DNAm) has long been a commonly investigated biomarker in biomedical research. The current gold standard for DNAm detection is bisulfite sequencing which requires dedicated alignment tools that can handle reduced sequence complexity. One commonly used application of DNAm are epigenetic clock measurements. These clocks have been adapted by many fields for their specific needs, including forensic genetics. Here, epigenetic clocks were designed to help estimate the chronological age of a biological stain donor for investigative purposes. RESULTS: In this study, data generated with a well-established forensic epigenetic clock is aligned with four different bisulfite-specific alignment tools: "Bwa-meth," "Abismal," "Bismark," and "BS-Seeker2." For each tool, we tested up to six different settings, altering parameters such as the maximum number of mismatches or the score function setting. The goal was to investigate whether the final predicted ages differed considerably between the tested alignment tools and settings. Quality controls such as read depth, precision, recall, F1 score, and alignment run time were also assessed. To allow other researchers to easily perform such methylation comparison analyses on their own data, a Shiny app called "MethylAge Explorer" was developed within this study. None of the tested settings for the three alignment tools "Abismal," "Bismark," and "BS-Seeker2" outperformed the originally used alignment tool "Bwa-meth" in terms of age prediction accuracy. However, differences in final age predictions were observed between the different alignment tools. Therefore, it is necessary to be aware of which alignment tool to use for particular epigenetic clocks. AVAILABILITY AND IMPLEMENTATION: The data underlying this article and the code for the shiny app are available on GitHub (https://github.com/charlsut/methylage_explorer).

DNA Methylation↗
PubMed2025

Hepatocyte dedifferentiation in 2D culture reveals extensive transcriptomic and proteomic rewiring.

BACKGROUND: Primary hepatocytes are commonly used in vitro to model liver metabolism, but prolonged culturing results in dedifferentiation and potentially limits the applicability of this model. METHODS: We characterized the transcriptome and proteome of full liver and primary hepatocytes as either freshly isolated cells or after 24 hours of 2D-culturing. RESULTS: We found that 2D-culturing for 24 hours changes more than 10,000 genes and 3000 proteins compared with freshly isolated cells, accompanied by a decrease in transcriptional heterogeneity and a loss of zonal markers. Moreover, there were changes in proteins associated with the extracellular matrix, in mitochondrial and ribosomal protein abundances, as well as an increase in the abundance of acute-phase response proteins. CONCLUSION: Collectively, primary mouse hepatocytes in culture rewire the transcriptome and proteome, which may affect the utility of this model to study physiological and molecular mechanisms related to the liver. We developed the Shiny app "Hepamorphosis" (https://cbmr.ku.dk/research/resources/shiny-apps/), which allows users to explore RNA/protein correlations, zonation profiles, and cell-type-specific transcription in full liver and cultured hepatocytes.

Hepatocytes↗
PubMed2025

Fishing for a reelGene: evaluating gene models with evolution and machine learning.

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.

Machine Learning↗
PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗
PubMed2025

EDAmame: interactive exploratory data analyses with explainable models.

SUMMARY: Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. AVAILABILITY AND IMPLEMENTATION: A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.

Machine Learning↗