Biology subjects

Gennari, J. H.

Publications and source records attributed to Gennari, J. H..

7 recordsLinked to original sources

bioRxiv2026

QNPtoVox: A methods pipeline for mapping 2D quantitative neuropathology to 3D MNI voxel space.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

neuroscience↗

bioRxiv2025

Elucidating Neurodevelopmental Trajectories in Cancer with Topic Modeling: Revealing Persistent External Granule Layer Lineages in Medulloblastoma

Single-cell transcriptomics transformed our understanding of cellular heterogeneity, yet cross-dataset comparison remains fundamentally limited by batch effects, technology differences, and inconsistent annotation frameworks. These challenges have impeded efforts to connect developmental programs with disease states which creates a critical gap for understanding pediatric cancers with suspected developmental origins. Here, we demonstrate that topic modeling, an unsupervised natural language processing technique, overcomes these barriers by learning transferable transcriptional genetic signatures that generalize across datasets, technologies, and biological contexts without requiring data integration. Applying this framework to over one million fetal cerebellar nuclei, we identify seven distinct topics that capture the developmental spectrum of rhombic lip progenitors through external granule layer (EGL) differentiation, including transitional states missed by conventional clustering. These topics transfer successfully across sequencing technologies (SPLiT-seq to sci-RNA-seq3), developmental timepoints, species (human to mouse), and from single-cell to bulk RNA sequencing of 876 medulloblastoma tumors. We reveal that Sonic hedgehog (SHH) medulloblastoma subtypes retain these topics, corresponding to distinct stages of EGL development. Our findings highlight that developmental timing at tumor initiation fundamentally shapes tumor biology, with immediate implications for subtype-specific therapeutic strategies in pediatric brain cancer. Our workflow establishes topic modeling as a scalable solution for mining expanding genomic atlases with broad applications across different datasets, technologies, and biological contexts.

bioinformatics↗

bioRxiv2023

An Automated Model Annotation System (AMAS) for SBML Models

MotivationAnnotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. ResultsWe present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g., species, reactions) by specifying the reference database (e.g., ChEBI for species) and the match score function (e.g., string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has sub-second response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AvailabilityOur project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license. Contacthsauro@uw.edu Supplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2021

SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models

The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.

systems biology↗

bioRxiv2018

SBOL-OWL: An ontological approach for formal and semantic representation of synthetic genetic circuits

Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent genetic circuit designs, and it is based on capturing data using graphs. The language provides the syntax using a free text document which is accessible to humans only. Here, we provide SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tooling for synthetic biology. Here, we demonstrate some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries are simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cant be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation and querying of information about synthetic genetic circuits and is important to incorporate complex design information via the integration of biological ontologies.

synthetic biology↗

bioRxiv2018

Model Annotation and Discovery with the Physiome Model Repository

MotivationMathematics and physics based computational models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models encoded in computable form help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse; tools and guidelines to facilitate semantic description of mathematical models; and repositories in which to archive, share, and discover models. Biologists and clinicians can leverage these resources to investigate specific questions and hypotheses.\n\nResultsWe have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. In helping a user to discover relevant models this tool will provide users with suggestions for similar or alternative models they may wish to explore or utilize in their model based on the models they would like to use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research.\n\nAvailability and implementationhttps://github.com/dewancse/model-discovery-tool

bioinformatics↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗