Biology subjects

Dunn, N.

Publications and source records attributed to Dunn, N..

5 recordsLinked to original sources

bioRxiv2026

Apollo 3: Multi-Species Genome Curation

We present Apollo 3, a new manual genome annotation tool that integrates with the JBrowse 2 genome browser. Its functionality is inspired by existing manual genome annotation tools such as Apollo, Artemis, and Otter, but uses an updated and more scalable architecture and technology stack. It allows the simultaneous editing of multiple genomes, including the visualization of synteny to inform those annotations. Apollo 3 can be used as a standalone annotation editor, or it can be installed on a server and used collaboratively. We describe the application's design, features, and use cases.

genomics↗

bioRxiv2019

iPSC-Derived Ovarian Tissue Restores Ovarian Function in Subfertile Mice After Gonadotoxic Chemotherapy

Many reproductive age women with cancer who receive chemotherapy are exposed to gonadotoxic agents and risk diminished ovarian reserve, sterility, and premature menopause. Previously, we reported the derivation of steroidogenic ovarian cells from induced pluripotent and embryonic stem cells. Derived cells not only produced reproductive hormones, but also displayed markers of ovarian tissue and primordial gametes. Here, we describe that human follicular fluid (HFF), when added to our stem cell differentiation system, enhances the steroidogenic potential of differentiating stem cells and increases the subpopulation of cells that express the ovarian and germ cell markers GJA1 and ZP1, respectively. More importantly, using an in vivo model of chemotherapy-induced premature ovarian insufficiency in subfertile nude mice, we demonstrate that orthotopic implantation of these derived cells restores ovarian hormone synthesis and produces functional stem cell-derived oocytes. Additionally, these cells also ameliorate subfertility in nude mice, as demonstrated by the delivery of multiple litters of healthy pups from stem cell-derived oocytes. Collectively, these data support the hypothesis that stem cell-derived steroidogenic ovarian tissue could be used to promote neo-gametogenesis and treat the endocrinologic and reproductive sequelae of premature ovarian insufficiency. One Sentence SummaryWe show that orthotopic injection of sorted, differentiated iPSCs in ovaries of subfertile mice restores reproductive hormone synthesis and fertility.

cell biology↗

bioRxiv2019

Apollo: Democratizing genome annotation

Genome annotation is the process of identifying the location and function of a genomes encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.\n\nIn this paper we first outline some of Apollos newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. O_LISource: https://github.com/GMOD/Apollo\nC_LIO_LILicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.md\nC_LIO_LIDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apollo\nC_LIO_LIRequirements: JDK 1.8, Node v6.0+\nC_LIO_LIUser guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/\nC_LIO_LIMailing list: apollo@lists.lbl.gov\nC_LI

bioinformatics↗

bioRxiv2017

WikiGenomes: an open Web application for community consumption and curation of gene annotation data in Wikidata.

With the advancement of genome sequencing technologies, new genomes are being sequenced daily. While these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources dont exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomic data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.

genomics↗

bioRxiv2016

The Monarch Initiative: Insights across species reveal human disease mechanisms

The principles of genetics apply across the whole tree of life: on a cellular level, we share mechanisms with species from which we diverged millions or even billions of years ago. We can exploit this common ancestry at the level of sequences, but also in terms of observable outcomes (phenotypes), to learn more about health and disease for humans and all other species. Applying the range of available knowledge to solve challenging disease problems requires unified data relating genomics, phenotypes, and disease; it also requires computational tools that leverage these multimodal data to inform interpretations by geneticists and to suggest experiments. However, the distribution and heterogeneity of databases is a major impediment: databases tend to focus either on a single data type across species, or on single species across data types. Although each database provides rich, high-quality information, no single one provides unified data that is comprehensive across species, biological scales, and data types. Without a big-picture view of the data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) is an international consortium dedicated to providing computational tools that leverage a computational representation of phenotypic data for genotype-phenotype analysis, genomic diagnostics, and precision medicine on the basis of a large-scale platform of multimodal data that is deeply integrated across species and covering broad areas of disease.

evolutionary biology↗