N-body simulations of the Magellanic Stream

A suite of high-resolution N-body simulations of the Magellanic Clouds -- Milky Way system are presented and compared directly with newly available data from the HI Parkes All-Sky Survey (HIPASS). We show that the interaction between Small and Large Magellanic Clouds results in both a spatial and kinematical bifurcation of both the Stream and the Leading Arm. The spatial bifurcation of the Stream is readily apparent in the HIPASS data, and the kinematical bifurcation is also tentatively identified. This bifurcation provides strong support for the tidal disruption origin for the Magellanic Stream. A fiducial model for the Magellanic Clouds is presented upon completion of an extensive parameter survey of the potential orbital configurations of the Magellanic Clouds and the viable initial boundary conditions for the disc of the Small Magellanic Cloud. The impact of the choice of these critical parameters upon the final configurations of the Stream and Leading Arm is detailed.Comment: Accepted by MNRAS, 07 Jun 2006. 14 pages, 14 figures, 3 tables. LaTeX (mn2e.sty). File with decent resolution images (strongly recommended) available at http://astronomy.swin.edu.au/~tconnors/publications/ . References added; distance and HI-LOres difference figures added; clearer figures; discussion added to, but conclusions unchange

The Neutral Hydrogen Properties of Galaxies in Gas-rich Groups

We present an analysis of the integrated neutral hydrogen (Hi) properties for 27 galaxies within nine low mass, gas-rich, late-type dominated groups which we denote \Choirs". We find that majority of the central Choir galaxies have average Hi content: they have a normal gas-mass fraction with respect to isolated galaxies of the same stellar mass. In contrast, we find more satellite galaxies with a lower gas-mass fraction than isolated galaxies of the same stellar mass. A likely reason for the lower gas content in these galaxies is tidal stripping. Both the specific star formation rate and the star formation efficiency of the central group galaxies are similar to galaxies in isolation. The Choir satellite galaxies have similar specific star formation rate as galaxies in isolation, therefore satellites that exhibit a higher star formation efficiency simply owe it to their lower gas-mass fractions. We find that the most Hi massive galaxies have the largest Hi discs and fall neatly onto the Hi size-mass relation, while outliers are galaxies that are experiencing interactions. We find that high specific angular momentum could be a reason for galaxies to retain the large fraction of Hi gas in their discs. This shows that for the Choir groups with no evidence of interactions, as well as those with traces of minor mergers, the internal galaxy properties dominate over the effects of residing in a group. The probed galaxy properties strengthen evidence that the Choir groups represent the early stages of group assembly

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

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-focussed measurable trait data. 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

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The large-scale structure of the halo of the Andromeda galaxy II. Hierarchical structure in the Pan-Andromeda Archaeological Survey

The Pan-Andromeda Archaeological Survey is a survey of $>400$ square degrees centered on the Andromeda (M31) and Triangulum (M33) galaxies that has provided the most extensive panorama of a $L_\star$ galaxy group to large projected galactocentric radii. Here, we collate and summarise the current status of our knowledge of the substructures in the stellar halo of M31, and discuss connections between these features. We estimate that the 13 most distinctive substructures were produced by at least 5 different accretion events, all in the last 3 or 4 Gyrs. We suggest that a few of the substructures furthest from M31 may be shells from a single accretion event. We calculate the luminosities of some prominent substructures for which previous estimates were not available, and we estimate the stellar mass budget of the outer halo of M31. We revisit the problem of quantifying the properties of a highly structured dataset; specifically, we use the OPTICS clustering algorithm to quantify the hierarchical structure of M31's stellar halo, and identify three new faint structures. M31's halo, in projection, appears to be dominated by two `mega-structures', that can be considered as the two most significant branches of a merger tree produced by breaking M31's stellar halo into smaller and smaller structures based on the stellar spatial clustering. We conclude that OPTICS is a powerful algorithm that could be used in any astronomical application involving the hierarchical clustering of points. The publication of this article coincides with the public release of all PAndAS data products.Comment: Accepted for publication in the Astrophysical Journal. 51 pages, 24 figures, 5 tables. Some figures have degraded resolution. All PAndAS data products are available via the CADC at http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/community/pandas/query.html where you can also find a version of the paper with full resolution figure