Bioinformatics advances最新文献

Motivation: Advances in genomics have created an insistent need for accessible tools that simplify complex genetic data analysis, enabling researchers across fields to harness the power of genome-wide association studies and genomic prediction. GWAStic was developed to bridge this gap, providing an intuitive platform that combines artificial intelligence with traditional statistical methods, making sophisticated genomic analysis accessible without requiring deep expertise in statistical software.

Results: We present GWAStic, an intuitive, cross-platform desktop application designed to streamline genome-wide association studies and genomic prediction for biological and medical researchers. With a user-friendly graphical interface, GWAStic integrates machine learning and traditional statistical approaches to support genetic analysis. The application accepts inputs from standard text-based Variant Call Formats and PLINK binary files, generating clear graphical outputs, including Manhattan plots, quantile-quantile plots, and genomic prediction correlation plots to enhance data visualization and analysis.

Availability and implementation: Project page: https://github.com/snowformatics/gwastic_desktop; GWAStic documentation: https://snowformatics.gitbook.io/product-docs; PyPI: https://pypi.org/project/gwastic-desktop/.

Motivation: The UK Biobank data holds immense potential for human health research. However, the complex data preparation and interpretation processes often act as barriers for researchers, diverting them from their core research questions.

Results: We developed LUKB, an R Shiny-based web tool that simplifies UK Biobank data preparation by automating these preprocessing tasks. LUKB reduces preprocessing time and integrates functions for initial data exploration, allowing researchers to dedicate more time to their scientific endeavors. Detailed deployment and usage can be found in the Supplementary Data.

Availability and implementation: LUKB is freely available at https://github.com/HaiGenBuShang/LUKB.

Motivation: In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population.

Results: We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection.

Availability and implementation: DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.

Motivation: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.

Results: Building upon the similarity between human and mouse mitochondrial metabolism, we present mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is able to model mouse and human mitochondrial metabolism. To demonstrate this, using an adapted E-Flux algorithm, we integrated proteomic data from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue, as well as transcriptomic data from in vitro differentiated human brown adipocytes and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research in cardiac disease and diabetes in both mouse and human contexts.

Availability and implementation: The MitoMammal Jupyter Notebook is available at: https://gitlab.com/habermann_lab/mitomammal.

Motivation: Genomic signal processing (GSP), which transforms biomolecular sequences into discrete signals for spectral analysis, has provided valuable insights into DNA sequence, structure, and evolution. However, challenges persist with spectral representations of variable-length sequences for tasks like species classification and in interpreting these spectra to identify discriminative DNA regions.

Results: We introduce SpecGMM, a novel framework that integrates sliding window-based Spectral analysis with a Gaussian Mixture Model to transform variable-length DNA sequences into fixed-dimensional spectral representations for taxonomic classification. SpecGMM's hyperparameters were selected using a dataset of plant sequences, and applied unchanged across diverse datasets, including mitochondrial DNA, viral and bacterial genome, and 16S rRNA sequences. Across these datasets, SpecGMM outperformed a baseline method, with 9.45% average and 35.55% maximum improvement in test accuracies for a Linear Discriminant classifier. Regarding interpretability, SpecGMM revealed discriminative hypervariable regions in 16S rRNA sequences-particularly V3/V4 for discriminating higher taxa and V2/V3 for lower taxa-corroborating their known classification relevance. SpecGMM's spectrogram video analysis helped visualize species-specific DNA signatures. SpecGMM thus provides a robust and interpretable method for spectral DNA analysis, opening new avenues in GSP research.

Availability and implementation: SpecGMM's source code is available at https://github.com/BIRDSgroup/SpecGMM.

Motivation: Protein function prediction is crucial in bioinformatics, driven by the growth of protein sequence data from high-throughput technologies. Traditional methods are costly and slow, underscoring the need for computational solutions. While deep learning offers powerful tools, many models lack optimization for brain development datasets, critical for neurodevelopmental disorder research. To address this, we developed RecGOBD (Recognition of Gene Ontology-related Brain Development protein function), a model tailored to predict protein functions essential to brain development.

Result: RecGOBD targets 10 key gene ontology (GO) terms for brain development, embedding protein sequences associated with these terms. Leveraging advanced pre-trained models, it captures both sequence and structure data, aligning them with GO terms through attention mechanisms. The category attention layer enhances prediction accuracy. RecGOBD surpassed five benchmark models in AUROC, AUPR, and Fmax metrics and was further used to predict autism-related protein functions and assess mutation impacts on GO terms. These findings highlight RecGOBD's potential in advancing protein function prediction for neurodevelopmental disorders.

Availability and implementation: All Python codes associated with this study are available at https://github.com/ZL-Xia/RECGOBD.git.

Summary: Amino acid scales are crucial for sequence-based protein prediction tasks, yet no gold standard scale set or simple scale selection methods exist. We developed AAclust, a wrapper for clustering models that require a pre-defined number of clusters k, such as k-means. AAclust obtains redundancy-reduced scale sets by clustering and selecting one representative scale per cluster, where k can either be optimized by AAclust or defined by the user. The utility of AAclust scale selections was assessed by applying machine learning models to 24 protein benchmark datasets. We found that top-performing scale sets were different for each benchmark dataset and significantly outperformed scale sets used in previous studies. Noteworthy is the strong dependence of the model performance on the scale set size. AAclust enables a systematic optimization of scale-based feature engineering in machine learning applications.

Availability and implementation: The AAclust algorithm is part of AAanalysis, a Python-based framework for interpretable sequence-based protein prediction, which is documented and accessible at https://aaanalysis.readthedocs.io/en/latest and https://github.com/breimanntools/aaanalysis.

Motivation: Gene transcripts are distinguished by the composition of their exons, and this different exon composition may contribute to advancing proteome complexity. Despite the availability of alternative splicing information documented in various databases, a ready association of exonic variations to the protein sequence remains a mammoth task.

Results: To associate exonic variation(s) with the protein systematically, we designed the Exon Nomenclature and Classification of Transcripts (ENACT) framework for uniquely annotating exons that tracks their loci in gene architecture context with encapsulating variations in splice site(s) and amino acid coding status. After ENACT annotation, predicted protein features (secondary structure/disorder/Pfam domains) are mapped to exon attributes. Thus, ENACTdb provides trackable exonic variation(s) association to isoform(s) and protein features, enabling the assessment of functional variation due to changes in exon composition. Such analyses can be readily performed through multiple views supported by the server. The exon-centric visualizations of ENACT annotated isoforms could provide insights on the functional repertoire of genes due to alternative splicing and its related processes and can serve as an important resource for the research community.

Availability and implementation: The database is publicly available at https://www.iscbglab.in/enactdb/. It contains protein-coding genes and isoforms for Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus, and Homo sapiens.

Motivation: The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference.

Results: In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method's ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations.

Availability and implementation: The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.

Motivation: Metabolic phenotyping, using high-resolution spectroscopic molecular fingerprints of biological samples, has demonstrated diagnostic, prognostic, and mechanistic value in clinical studies. However, clinical translation is hindered by the lack of viable workflows and challenges in converting spectral data into usable information.

Results: MetaboScope is an analytical and statistical workflow for learning, designing and analyzing clinically relevant ¹H nuclear magnetic resonance data. It features modular preprocessing pipelines, multivariate modeling tools including Principal Components Analysis (PCA), Orthogonal-Projection to Latent Structure Discriminant Analysis (OPLS-DA), and biomarker discovery tools (multiblock PCA and statistical spectroscopy). A simulation tool is also provided, allowing users to create synthetic spectra for hypothesis testing and power calculations.

Availability and implementation: MetaboScope is built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The MetaboScope tool is available at https://www.cheminfo.org/flavor/metabolomics/index.html. The code is open-source and can be deployed locally if necessary. Module notes, video tutorials, and clinical spectral datasets are provided for modeling.