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9 results for “Genomic Knowledge”
Extant and extinct bilby genomes combined with indigenous knowledge improve conservation of a unique Australian marsupial
<p>The Ninu (Greater bilby, <em>Macrotis lagotis</em>) is a desert-dwelling, culturally and ecologically important marsupial. In collaboration with Indigenous rangers and conservation managers, we generated the first Ninu chromosome-level genome assembly (3.66 Gbp) and genome sequences for the extinct Yallara (Lesser bilby, <em>Macrotis leucura</em>). We developed and tested a scat SNP panel, based on our genomic datasets, to inform current and future conservation actions, to undertake future ecological assessments, and improve our understanding of Ninu genetic diversity in managed and wild populations. We also assessed the beneficial impact of targeted conservation actions, like translocations, in the contemporary metapopulation (N=363 Ninu). Resequenced genomes (temperate Ninu=6; semi-arid Ninu=6; Yallara=4) revealed two major population crashes during global cooling events for both species and differences in Ninu genes involved in anatomical and metabolic pathway adaptations to aridity. Despite their 45-year long captive history, Ninu have fewer long runs of homozygosity than other larger mammals, which may be attributable to their boom-bust life-history. We also investigated the unique Ninu biology using 12 tissue transcriptomes revealing expression of all 115 conserved eutherian chorioallantoic placentation genes in the uterus; an XY<sub>1</sub>Y<sub>2</sub> sex chromosome system generated by fusion of the X with a large telocentric autosome; and expansions in olfactory receptor genes. Together, we demonstrate the holistic value of genomics in improving key conservation management actions, understanding unique biological traits, and developing tools for Indigenous rangers to monitor remote wild populations.</p>
Appendix of the thesis "Acquisition of new genetic knowledge on strains of Mycobacterium bovis, circulating in France, by the whole genome sequencing approach." Ciriac CHARLES
<p>Appendix 1 Table showing the <em>Mycobacterium bovis </em>genome information used in this study in panel 1 The 87 genomes are from previous work (Hauer et al., 2019). The names of the genomes are highlighted with color to distinguish them within a previously defined M. bovis cluster. In Yellow Cluster I/Clonal Complex Eu3. In orange Cluster G/Family F9. In brown Cluster C/Family SB0134. In purple Cluster D/Clonal Complex Eu1. In pink the Cluster A/Family F4. In blue the Cluster F/Clonal Complex Eu2. In grey, the others.</p> <p>Appendix 2 Table showing the <em>Mycobacterium bovis </em>genome information used in this study in panel 3. The 187 genomes are of genotypes F7 and F15 (SB0821 and SB0832 respectively) which are two closely related genotypes. These genomes had already been sequenced before the start of the thesis work. Some were published with the work of Duault and collaborators and others have not yet been published (noted NA) (Duault et al., 2022).</p> <p>Appendix 3 Table showing the <em>Mycobacterium bovis </em>genome information used in this study in panel 4. The 227 genomes are of genotype SB0120-DHV. These genomes had already been sequenced before the start of the thesis work but are currently unpublished (noted NA in the columns "Date of deposit" and "Genbank biosample").</p> <p>Appendix 4 Position of IS<em>6110</em> insertion sites in the panel of <em>Mycobacterium bovis </em>strains from Pyrénées-Atlantiques (SB0821 and SB0832). The position of the start and end of IS<em>6110</em> are determined with the ISMapper tool and the reference genome AF2122/97.</p> <p>Appendix 5 Position of IS<em>6110</em> insertion sites in the <em>Mycobacterium bovis </em>strain panel of SB0120-DHV. Position of the start and end of IS<em>6110</em> are determined with the ISMapper tool and the Mb3601 reference genome.</p> <p>Appendix 6 This figure from the Modenesi internship report represents a circular consensus phylogenetic tree of 300 strains of SB0120-DHV (Modenesi 2019). The strains in panel 3 are part of these 300 strains and were selected according to their sequencing quality, which explains the smaller number of strains retained (227). This tree is divided into 11 different colored clades. The posterior probability values (which correspond to the probability that these clades are true) determined with Beast (Drummond and Rambaut, 2007) are represented inside the tree.</p>
Appendix of the thesis "Acquisition of new genetic knowledge on Mycobacterium bovis strains, circulating in France, by the whole genome sequencing approach", part "4. Sequencing of new complete genomes", Ciriac CHARLES
<p>Annex S1: Sequencing metric of the 10 new genomes and obtained with fastqc. A: Metric provide to Illumina metric. B: metric provide to MinION metric.</p> <p>Annex S2: Pan-genomic study performed on 12 <em>M. bovis</em> complete genomes. The table indicates the genes accessory. “1” shows the presence of CDS and “0” his absence.</p> <p>Annex S3: Indels between the ten new complete genomes and Mb3601 using progressiveMauve. Annotation of these indels was performed with reference genome comparison for gap or with the annotation (with Prokka) of the new complete genome studied for insertion. IS<em>6110</em> is marked in green. "Indel distribution" sheet shows the indel distribution on the <em>M. bovis</em> genome for the ten new complete genomes. Black arrows show the genomic region with the most of indel found.</p> <p>Annex S4: WgSNP analysis performed on 98 <em>M. bovis</em>. SNPs were annotated and selected according to their specificity to an <em>M. bovis</em> group described in Fig 5. The last common give information on the genetic impact of the SNP. The last sheet presents a graph of SNP number in genomic position.</p> <p>Annex S5: Alignments of the 12 <em>M. bovis </em>complete genomes.</p>
HOPE-Genomics Intervention for the Improvement of Cancer Patient Knowledge of Genomics
ClinicalTrials.gov study NCT04905082. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Extant and extinct bilby genomes combined with indigenous knowledge improve conservation of a unique Australian marsupial
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Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Data from: Predicting contemporary range-wide genomic variation using climatic, phylogeographic and morphological knowledge in an ancient, unglaciated landscape
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WheatGenomicsSLKG: Wheat Genomics Scientific Literature Knowledge Graph
<p>The <strong>Wheat Genomics Scientific Literature Knowledge Graph </strong>(<strong>WheatGenomicsSLKG</strong>) is a FAIR knowledge graph that exploits the Semantic Web technologies to integrate information about Named Entities (NE) extracted automatically from a corpus of PubMed scientific papers on wheat genetics and genomics.</p> <p>Code an details: https://github.com/Wimmics/WheatGenomicsKG</p>
Uncertainty-aware genomic deep learning with knowledge distillation
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.