Bioinformatics (Oxford, England)最新文献

Motivation: Mathematical modelling plays a crucial role in understanding inter- and intracellular signalling processes. Currently, ordinary differential equations (ODEs) are the predominant approach in systems biology for modelling such pathways. While ODE models offer mechanistic interpretability, they also suffer from limitations, including the need to consider all relevant compounds, resulting in large models difficult to handle numerically and requiring extensive data.

Results: In previous work, we introduced the retarded transient function (RTF) as an alternative method for modelling temporal responses of signalling pathways. Here, we extend the RTF approach to integrate concentration or dose-dependencies into the modelling of dynamics. With this advancement, RTF modelling now fully encompasses the application range of ordinary differential equation (ODE) models, which comprises predictions in both time and concentration domains. Moreover, characterizing dose-dependencies provides an intuitive way to investigate and characterize signalling differences between biological conditions or cell-types based on their response to stimulating inputs. To demonstrate the applicability of our extended approach, we employ data from time- and dose-dependent inflammasome activation in bone-marrow derived macrophages (BMDMs) treated with nigericin sodium salt. Our results show the effectiveness of the extended RTF approach as a generic framework for modelling dose-dependent kinetics in cellular signalling. The approach results in intuitively interpretable parameters that describe signal dynamics and enables predictive modelling of time- and dose-dependencies even if only individual cellular components are quantified.

Availability: The presented approach is available within the MATLAB-based Data2Dynamics modelling toolbox at https://github.com/Data2Dynamics and https://zenodo.org/records/14008247 and as R code at https://github.com/kreutz-lab/RTF.

Summary: Fast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general.

Availability and implementation: The software tool, code and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541.

Supplementary information: Supplementary material is available at Bioinformatics online.

Motivation: For more than 25 years, learning-based eukaryotic gene predictors were driven by hidden Markov models (HMMs), which were directly inputted a DNA sequence. Recently, Holst et al. demonstrated with their program Helixer that the accuracy of ab initio eukaryotic gene prediction can be improved by combining deep learning layers with a separate HMM postprocessor.

Results: We present Tiberius, a novel deep learning-based ab initio gene predictor that end-to-end integrates convolutional and long short-term memory layers with a differentiable HMM layer. Tiberius uses a custom gene prediction loss and was trained for prediction in mammalian genomes and evaluated on human and two other genomes. It significantly outperforms existing ab initio methods, achieving F1-scores of 62% at gene level for the human genome, compared to 21% for the next best ab initio method. In de novo mode, Tiberius predicts the exon-intron structure of two out of three human genes without error. Remarkably, even Tiberius's ab initio accuracy matches that of BRAKER3, which uses RNA-seq data and a protein database. Tiberius's highly parallelized model is the fastest state-of-the-art gene prediction method, processing the human genome in under 2 hours.

Availability and implementation: https://github.com/Gaius-Augustus/Tiberius.

Motivation: Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. These proteins exhibit distinct secondary structures that form repetitive tertiary arrangements, often resulting in large molecular assemblies. Despite highly variable sequences, STRPs can perform important and diverse biological functions, maintaining a consistent structure with a variable number of repeat units. With the advent of protein structure prediction methods, millions of 3D-models of proteins are now publicly available. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation remains the most accurate method for detecting and classifying STRPs, making it impracticable to annotate millions of structures.

Results: We introduce STRPsearch, a novel tool for the rapid identification, classification, and mapping of STRPs. Leveraging manually curated entries from RepeatsDB as the known conformational space of STRPs, STRPsearch employs the latest advances in structural alignment for a fast and accurate detection of repeated structural motifs in proteins, followed by an innovative approach to map units and insertions through the generation of TM-score profiles. STRPsearch is highly scalable, efficiently processing large datasets, and can be applied to both experimental structures and predicted models. Additionally, it demonstrates superior performance compared to existing tools, offering researchers a reliable and comprehensive solution for STRP analysis across diverse proteomes.

Availability and implementation: STRPsearch is coded in Python. All scripts and associated documentation are available from: https://github.com/BioComputingUP/STRPsearch.

Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: DamID sequencing is a technique to map the genome-wide interaction of a protein with DNA. Damsel is the first Bioconductor package to provide an end to end analysis for DamID sequencing data within R. Damsel performs quantification and testing of significant binding sites along with exploratory and visual analysis. Damsel produces results consistent with previous analysis approaches.

Availability: The R package Damsel is available for install through the Bioconductor project https://bioconductor.org/packages/release/bioc/html/Damsel.html and the code is available on GitHub https://github.com/Oshlack/Damsel/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs.

Results: Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs. The framework exploits the primal and dual formulations of these models to derive sensitivity coefficients based on relations between variables, constraints, and the objective function. This approach enhances our understanding of the growth-limiting factors of metabolic phenotypes under specific environmental or genetic conditions. Compared to other traditional methods for calculating sensitivities, sEnz requires substantially less computation time and facilitates more intuitive comparison and analysis of sensitivities. The sensitivities calculated by sEnz cover enzymes, reactions and protein sectors, enabling a holistic overview of the factors influencing metabolism. When applied to an Escherichia coli PAM, sEnz revealed major pathways and enzymes driving overflow metabolism. Overall, sEnz offers a computational efficient framework for understanding PAM predictions and unravelling the factors governing a particular metabolic phenotype.

Availability and implementation: sEnz is implemented in the modular toolbox for the generation and analysis of PAMs in Python (PAModelpy; v.0.0.3.3), available on Pypi (https://pypi.org/project/PAModelpy/). The source code together with all other python scripts and notebooks are available on GitHub (https://github.com/iAMB-RWTH-Aachen/PAModelpy).

Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies.

Results: To facilitate the development, evaluation and reproducibility of phenotyping pipelines, we developed an open-source Python library named medkit. It enables composing data processing pipelines made of easy-to-reuse software bricks, named medkit operations. In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment.

Availability and implementation: medkit is available at https://github.com/medkit-lib/medkit.

Supplementary information: Documentation, examples and tutorials are available at https://medkit-lib.org/.

Motivation: The recent progress in RNA structure determination methods has resulted in a surge of newly solved RNA 3D structures. However, there is an absence of a user-friendly browser-based tool that can facilitate the comparison and visualization of RNA motifs across multiple 3D structures.

Results: We introduce R3DMCS, a web server that allows users to compare selected RNA nucleotides across all 3D structures of a given molecule from a given species, or across all 3D structures mapped to a single Rfam family. Starting from one instance of the motif, R3DMCS retrieves, aligns, annotates, organizes, and displays 3D coordinates of corresponding sets of nucleotides from other 3D structures. With R3DMCS, one can explore conformational changes of motifs due to 3D structures being solved in different functional states or different experimental conditions. One can also investigate conservation of 3D structure across species, or changes in 3D structure due to changes in sequence.

Availability: R3DMCS is open-source software and freely available at  https://rna.bgsu.edu/correspondence/  and  https://github.com/BGSU-RNA/RNA-3D-correspondence  .

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Large Language Models (LLMs) have provided spectacular results across a wide variety of domains. However, persistent concerns about hallucination and fabrication of authoritative sources raise serious issues for their integral use in scientific research. Retrieval-augmented generation (RAG) is a technique for making data and documents, otherwise unavailable during training, available to the LLM for reasoning tasks. In addition to making dynamic and quantitative data available to the LLM, RAG provides the means by which to carefully control and trace source material, thereby ensuring results are accurate, complete and authoritative.

Results: Here we introduce LmRaC, an LLM-based tool capable of answering complex scientific questions in the context of a user's own experimental results. LmRaC allows users to dynamically build domain specific knowledge-bases from PubMed sources (RAGdom). Answers are drawn solely from this RAG with citations to the paragraph level, virtually eliminating any chance of hallucination or fabrication. These answers can then be used to construct an experimental context (RAGexp) that, along with user supplied documents (e.g., design, protocols) and quantitative results, can be used to answer questions about the user's specific experiment. Questions about quantitative experimental data are integral to LmRaC and are supported by a user-defined and functionally extensible REST API server (RAGfun).

Availability and implementation: Detailed documentation for LmRaC along with a sample REST API server for defining user functions can be found at https://github.com/dbcraig/LmRaC. The LmRaC web application image can be pulled from Docker Hub (https://hub.docker.com) as dbcraig/lmrac.

Motivation: Integrating multiple omics datasets can significantly advance our understanding of disease mechanisms, physiology, and treatment responses. However, a major challenge in multi-omics studies is the disparity in sample sizes across different datasets, which can introduce bias and reduce statistical power. To address this issue, we propose a novel framework, OmicsNMF, designed to impute missing omics data and enhance disease phenotype prediction. OmicsNMF integrates Generative Adversarial Networks (GANs) with Non-Negative Matrix Factorization (NMF). NMF is a well-established method for uncovering underlying patterns in omics data, while GANs enhance the imputation process by generating realistic data samples. This synergy aims to more effectively address sample size disparity, thereby improving data integration and prediction accuracy.

Results: For evaluation, we focused on predicting breast cancer subtypes using the imputed data generated by our proposed framework, OmicsNMF. Our results indicate that OmicsNMF consistently outperforms baseline methods. We further assessed the quality of the imputed data through survival analysis, revealing that the imputed omics profiles provide significant prognostic power for both overall survival and disease-free status. Overall, OmicsNMF effectively leverages GANs and NMF to impute missing samples while preserving key biological features. This approach shows potential for advancing precision oncology by improving data integration and analysis.

Availability and implementation: Source code is available at: https://github.com/compbiolabucf/OmicsNMF.