Bioinformatics advances最新文献

Summary: Visium HD Spatial Transcriptomics Data Analysis and Visualization (VST-DAVis) is an interactive, R Shiny application and web browser designed for intuitive analysis of spatial transcriptomics data generated using the 10x Genomics Visium HD platform. This user-friendly tool empowers researchers, particularly those without programming expertise, to perform end-to-end spatial transcriptomics analysis through a streamlined graphical interface. The platform is capable of handling both single and multiple samples, enabling comparative analyses across diverse biological conditions or replicates. It accepts various input formats including both H5 and matrix-based files from Space Ranger and outputs high-quality graphics from various visualization tools. VST-DAVis integrates several widely used R packages, such as Seurat, Monocle3, CellChat, and hdWGCNA, to offer a robust and flexible analytical environment that supports a wide range of analytical tasks, including quality control, clustering, marker gene identification, subclustering, trajectory inference, pathway enrichment analysis, cell-cell communication modeling, co-expression analysis, and transcription factor network reconstruction. By combining its analytical depth with user-friendliness, VST-DAVis makes advanced analyses accessible to various research communities that utilize spatial transcriptomics data.

Availability and implementation: VST-DAVis is freely available at https://www.gudalab-rtools.net/VST-DAVis. It is implemented in R 4.5.2 and Bioconductor ≥ 3.22 using the Shiny framework and supports input from Space Ranger outputs. The source code and documentation are hosted on GitHub: https://github.com/GudaLab/VST-DAVis.

Motivation: Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation.

Results: We introduce EC-Bench, a benchmark for EC number prediction, consisting of (i) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), (ii) existing and novel accuracy and efficiency performance metrics, and (iii) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative framework. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely "exact EC number prediction," "EC number completion," and (partial or additional) "EC number recommendation." We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy.

Availability and implementation: The benchmarking pipeline is available at https://github.com/dsaeedeh/EC-Bench.

Motivation: Nutrition is an important factor in human health, used to alleviate or prevent symptoms of various diseases. However, the effects of nutrition on the gut microbiome and human metabolism are not well understood. Whole-body metabolic models (WBMs) have been applied to study relationships between regional diets and human/microbiome metabolism. This method requires diets to be defined at the metabolite level, rather than the food item level, which has gated the application of personalized diets to WBMs.

Results: We developed the Nutrition Toolbox, which leverages open-source databases containing metabolite composition for over ten thousand food items to convert food items into their metabolic composition to create in silico diets. Additionally, when used with a previously published nutrition algorithm, minimal changes to a diet can be identified to achieve desirable shifts in human and microbiome metabolism. Taken together, we believe that the Nutrition Toolbox can help to understand the effects of nutrition on human metabolism and has the potential to contribute to personalized nutrition.

Availability and implementation: The Nutrition Toolbox is written in MATLAB. The code can be found at https://github.com/opencobra/cobratoolbox. A tutorial explaining the code is available in the COBRA toolbox and as view-only supplementary tutorial. Details on installing the COBRA toolbox are available at https://opencobra.github.io/cobratoolbox/stable/installation.html.

Summary: Since the emergence of SARS-CoV-2, numerous studies have investigated antibody interactions with viral variants in vitro, and several datasets have been curated to compile available protein structures and experimental measurements. However, existing data remain fragmented, limiting their utility for the development and validation of machine learning models for antibody-antigen interaction prediction. Here, we present CoV-UniBind, a unified database comprising over 75 000 entries of SARS-CoV-2 antibody-antigen sequence, binding, and structural data, integrated and standardized from three public sources and multiple peer-reviewed publications. To demonstrate its utility, we benchmarked multiple protein folding, inverse folding, and language models across tasks relevant to antibody design and vaccine development. We expect CoV-UniBind to facilitate future computational efforts in antibody and vaccine development against SARS-CoV-2.

Availability and implementation: The curated datasets, model scores and antibody synonyms are free to download at https://huggingface.co/datasets/InstaDeepAI/cov-unibind. Folded structures are available upon request.

Motivation: Although high-quality chromosome-scale genome assemblies are feasible, assembling large ones remains complex and resource-intensive. This demands reproducible and automated workflows that not only implement current best practices efficiently but also allow for improvement alongside future updates to those standards.

Results: We present Pipeasm, a Snakemake-based genome assembly pipeline containerized with Singularity. Pipeasm can use HiFi, ONT, and Hi-C data, automating read trimming, nuclear and mitogenome assembly, scaffolding, decontamination, and quality evaluation. Applied to four vertebrate species with distinct genomic characteristics, starting from a single command line and configuration file, it produced assemblies with scaffold L50 proportional to the expected chromosome and genome length, and up to 99.6% BUSCO completeness. Its output also includes detailed reports for each step, genome statistics, Hi-C maps, and files ready for curation.

Availability and implementation: Pipeasm is available at https://github.com/itvgenomics/pipeasm, implemented in Python/Snakemake with Singularity, and runs on Unix-based systems.

Motivation: Creating publication-quality visualizations is essential for bioinformatics but remains a bottleneck for researchers with limited coding expertise. While Large Language Models (LLMs) are proficient at generating code, they often fail in practice due to library dependencies, dataset mismatches, or syntax errors. These issues require manual intervention, slowing data interpretation.

Results: We present ggplotAgent, a novel multi-modal, self-debugging artificial intelligence agent that automates publication-ready ggplot2 visualizations. It features a dual-layered framework that resolves code execution errors and uses a vision-enabled agent to verify aesthetic correctness. In benchmarks against the DeepSeek-V3 model, ggplotAgent achieved a 100% code executability rate(versus 85%) and a "Publication-Ready" score of 1.9 (versus 0.7). Surprisingly, it showcased the ability to act as an expert collaborator by intelligently enhancing plots beyond the user's literal prompt, achieving a positive Insight Score of +0.3 over than the baseline (-0.05). These results demonstrate its ability to reliably produce accurate, high-quality visualizations directly from natural language.

Availability and implementation: ggplotAgent is freely accessible as a public web application at https://ggplotagent.databio1.com/ and an offline Streamlit app. The source code is available on GitHub at https://github.com/charlin90/ggplotAgent. This software is distributed under the MIT License.

Motivation: Chromosome-level assemblies are essential for modern genomics, from comparative genomics and evolutionary studies to precision breeding. While integrated HiFi and Hi-C data now enable accurate chromosome-scale genome assemblies, the bioinformatic process remains complex and involves specialized tools and expertise. With large-scale pan-genomic efforts requiring dozens to hundreds of platinum quality chromosome-scale genomes, there is a need for scalable, portable, and user-friendly pipelines that streamline and standardize high-quality genome assembly workflows.

Results: We introduce Puzzler, a containerized, scalable pipeline for chromosome-scale de novo genome assembly using PacBio HiFi and Hi-C data. Designed for portability and minimal user input, Puzzler automates contig assembly, duplicate purging, Hi-C-based scaffolding, and chromosome assignment via synteny, even with highly diverged reference taxa. Optional modules generate input files for manual Hi-C curation or operate reference-free. Quality control is integrated and includes Hi-C contact maps, BUSCO, yak k-mer completeness, and BlobTools contamination screening. A checkpointing system ensures that previously completed tasks are not re-executed, while a simple sample sheet input structure supports scalable batch processing. Puzzler has been validated on genomes ranging from 24 Mbp to 6.5 Gbp, delivering highly contiguous assemblies with <10 min of user input, enabling high-throughput platinum-quality genome assembly.

Availability and implementation: Puzzler is released into the public domain under 17 U.S.C. §105. Source code, documentation, and tutorials are available at https://github.com/merondun/puzzler and archived on Zenodo: https://doi.org/10.5281/zenodo.15733730 and https://doi.org/10.5281/zenodo.15693025. Pre-configured runtime environments including dependencies are provided via both a Conda environment (https://anaconda.org/heritabilities/puzzler) and an Apptainer hosted both on Zenodo and Sylabs (https://cloud.sylabs.io/library/merondun/default/puzzler).

Motivation: Cyclic immunofluorescence (IF) techniques enable deep phenotyping of cells and help quantify tissue organization at high resolution. Due to its high dimensionality, workflows typically rely on unsupervised clustering, followed by cell type annotation at a cluster level for cell type assignment. Most of these methods use marker expression averages that lack a statistical evaluation of cell type annotations, which can result in misclassification. Here, we propose a strategy through an end-to-end pipeline using a semi-supervised, random forest approach to predict cell type annotations.

Results: Our method includes cluster-based sampling for training data, cell type prediction, and downstream visualization for interpretability of cell annotation that ultimately improves classification results. We show that our workflow can annotate cells more accurately compared to representative deep learning and probabilistic methods, with a training set <5% of the total number of cells tested. In addition, our pipeline outputs cell type probabilities and model performance metrics for users to decide if it could boost their existing clustering-based workflow results for complex IF data.

Availability and implementation: Fluoro-forest is freely available on GitHub under an MIT license (https://github.com/Josh-Brand/Fluoro-forest).

Summary: This study presents a proof-of-concept, comprehensive, modular framework for AI-driven drug discovery (DD) and clinical trial simulation, spanning from target identification to virtual patient recruitment. Synthesized from a systematic analysis of 51 large language model (LLM)-based systems, the proposed Prompt-to-Pill architecture and corresponding implementation leverages a multi-agent system (MAS) divided into DD, preclinical and clinical phases, coordinated by a central Orchestrator. Each phase comprises specialized LLM for molecular generation, toxicity screening, docking, trial design, and patient matching. To demonstrate the full pipeline in practice, the well-characterized target Dipeptidyl Peptidase 4 (DPP4) was selected as a representative use case. The process begins with generative molecule creation and proceeds through ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation, structure-based docking, and lead optimization. Clinical-phase agents then simulate trial generation, patient eligibility screening using electronic health records (EHRs), and predict trial outcomes. By tightly integrating generative, predictive, and retrieval-based LLM components, this architecture bridges drug discovery and preclinical phase with virtual clinical development, offering a demonstration of how LLM-based agents can operationalize the drug development workflow in silico.

Availability and implementation: The implementation and code are available at: https://github.com/ChatMED/Prompt-to-Pill.

Motivation: The integration of genome-scale metabolic and regulatory networks has received significant interest in cancer systems biology. However, the identification of lethal genetic interventions in these integrated models remains challenging due to the combinatorial explosion of potential solutions. To address this, we developed the genetic Minimal Cut Set (gMCS) framework, which computes synthetic lethal interactions-minimal sets of gene knockouts that are lethal for cellular proliferation- in genome-scale metabolic networks with signed directed acyclic regulatory pathways. Here, we present a novel formulation to calculate genetic Minimal Intervention Sets, gMISs, which incorporate both gene knockouts and knock-ins.

Results: With our gMIS approach, we assessed the landscape of lethal genetic interactions in human cells, capturing interventions beyond synthetic lethality, including synthetic dosage lethality and tumor suppressor gene complexes. We applied the concept of synthetic dosage lethality to predict essential genes in cancer and demonstrated a significant increase in sensitivity when compared to large-scale gene knockout screen data. We also analyzed tumor suppressors in cancer cell lines and identified lethal gene knock-in strategies. Finally, we demonstrate how gMISs can help uncover potential therapeutic targets, providing examples in hematological malignancies.

Availability and implementation: The gMCSpy Python package now includes gMIS functionalities. Access: https://github.com/PlanesLab/gMCSpy.