ORCID Profile
0000-0001-6315-3707
Current Organisations
Lawrence Berkely National Laboratory
,
Princeton University
,
Massachusetts Institute of Technology
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Publisher: Springer Science and Business Media LLC
Date: 28-04-2023
Publisher: Cold Spring Harbor Laboratory
Date: 27-01-2023
DOI: 10.1101/2023.01.26.525742
Abstract: Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2022
DOI: 10.1101/2022.04.13.22273750
Abstract: There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.
Publisher: Springer Science and Business Media LLC
Date: 24-01-2023
DOI: 10.1038/S41597-022-01886-2
Abstract: Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the ersity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.
Publisher: Cold Spring Harbor Laboratory
Date: 30-10-2022
DOI: 10.1101/2022.10.26.513573
Abstract: Characterizing cellular ersity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
Publisher: F1000 Research Ltd
Date: 20-12-2019
DOI: 10.12688/F1000RESEARCH.21568.1
Abstract: The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on “Building infrastructure for responsible open science in Africa”. Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.
Publisher: Oxford University Press (OUP)
Date: 2022
Abstract: Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a erse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly erse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: github.com/INCATools/ontology-development-kit
Location: United States of America
Location: United States of America
No related grants have been discovered for Nomi Harris.