Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types

braingeneers/braingeneerspy: v0.1.0

<h2>What's Changed</h2> <ul> <li>Added concatenate method, fixed fatal error in subset(), and fixed issue with neuron_attributes being empty when reading from phys data by @MissValeska in https://github.com/braingeneers/braingeneerspy/pull/7</li> <li>Fix, moved experiment to metadata instead of NeuronAttributes by @ashsrobbins in https://github.com/braingeneers/braingeneerspy/pull/11</li> <li>The most basic possible test for read_phy_files(). by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/12</li> <li>Replacing debug prints with logging by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/16</li> <li>Allow SpikeData.randomized() when spikes alias. by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/17</li> <li>fix(randomization): sporadic ValueError by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/18</li> <li>constructor refactor by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/19</li> <li>Ephys shadows by @pvbaudin in https://github.com/braingeneers/braingeneerspy/pull/21</li> <li>fix(data): phy loading template shape by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/22</li> <li>Shadows trash system by @pvbaudin in https://github.com/braingeneers/braingeneerspy/pull/24</li> <li>Added ability for message broker to reconnect and resubscribe after lost network connection by @davidparks21 in https://github.com/braingeneers/braingeneerspy/pull/25</li> <li>Refactor STTC by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/29</li> <li>SpikeData.from_raster() by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/30</li> <li>Memoization to S3 by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/28</li> <li>Add NaN to loaded unit groups in load_spike_data by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/35</li> <li>Update S3 endpoint by @davidparks21 in https://github.com/braingeneers/braingeneerspy/pull/36</li> <li>fixed ephys. call on ephys.load_data() and added exception on load_wi… by @warrenmm in https://github.com/braingeneers/braingeneerspy/pull/37</li> <li>Add MEArec metadata loader. by @DailyDreaming in https://github.com/braingeneers/braingeneerspy/pull/23</li> <li>Add a filter function for raw data by @surygeng in https://github.com/braingeneers/braingeneerspy/pull/41</li> <li>Modify metadata.json for Maxwell to replace raw file paths with NWB file paths if they exist. by @DailyDreaming in https://github.com/braingeneers/braingeneerspy/pull/40</li> <li>Update configure.py by @DailyDreaming in https://github.com/braingeneers/braingeneerspy/pull/46</li> <li>consolidate filter methods by @atspaeth in https://github.com/braingeneers/braingeneerspy/pull/44</li> <li>Merge <code>development</code> branch into the <code>master</code> branch by @uwcdc in https://github.com/braingeneers/braingeneerspy/pull/42</li> <li>Bump actions/checkout from 3 to 4 by @dependabot in https://github.com/braingeneers/braingeneerspy/pull/47</li> <li>chore(doi): Add DOI to README.md by @lsetiawan in https://github.com/braingeneers/braingeneerspy/pull/48</li> </ul> <h2>New Contributors</h2> <ul> <li>@MissValeska made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/7</li> <li>@ashsrobbins made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/11</li> <li>@atspaeth made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/12</li> <li>@pvbaudin made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/21</li> <li>@davidparks21 made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/25</li> <li>@warrenmm made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/37</li> <li>@surygeng made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/41</li> <li>@uwcdc made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/42</li> <li>@dependabot made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/47</li> <li>@lsetiawan made their first contribution in https://github.com/braingeneers/braingeneerspy/pull/48</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/braingeneers/braingeneerspy/compare/v0.0.1-alpha...v0.1.0</p&gt

ComparativeGenomicsToolkit/cactus: Cactus 2.6.13 2023-11-15

Official home of genome aligner based upon notion of Cactus graph

ComparativeGenomicsToolkit/cactus: Cactus 2.7.1 2024-01-19

<p>in progress</p&gt

ComparativeGenomicsToolkit/cactus: Cactus 2.6.10 2023-10-30

Official home of genome aligner based upon notion of Cactus graph

ComparativeGenomicsToolkit/cactus: Cactus 2.7.0 2023-12-05

Official home of genome aligner based upon notion of Cactus graph

ComparativeGenomicsToolkit/cactus: Cactus 2.6.12 2023-11-07

Official home of genome aligner based upon notion of Cactus graph

ComparativeGenomicsToolkit/cactus: Cactus 2.6.9 2023-10-20

Official home of genome aligner based upon notion of Cactus graph

ComparativeGenomicsToolkit/cactus: Cactus 2.7.1 2024-01-19

<p>Cactus 2.7.1 has been released! See here for details:</p> <p>https://github.com/ComparativeGenomicsToolkit/cactus/releases/tag/v2.7.1</p> <p>Cactus 2.7.1 is available in the following forms:</p> <ul> <li>Docker Image: <code>quay.io/comparative-genomics-toolkit/cactus:v2.7.1</code> GPU-accelerated Docker Image: <code>quay.io/comparative-genomics-toolkit/cactus:v2.7.1-gpu</code> Install instructions in <a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/v2.7.1/README.md">README.md</a></li> <li>Pre-compiled Binaries Linux Tarball: <a href="https://github.com/ComparativeGenomicsToolkit/cactus/releases/download/v2.7.1/cactus-bin-v2.7.1.tar.gz">cactus-bin-v2.7.1.tar.gz</a> Install instructions in <a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/v2.7.1/BIN-INSTALL.md">BIN-INSTALL.md</a></li> <li>Pre-compiled Binaries For Older CPU Architectures Linux Tarball: <a href="https://github.com/ComparativeGenomicsToolkit/cactus/releases/download/v2.7.1/cactus-bin-legacy-v2.7.1.tar.gz">cactus-bin-legacy-v2.7.1.tar.gz</a> Install instructions in <a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/v2.7.1/BIN-INSTALL.md">BIN-INSTALL.md</a></li> <li>Source Tarball: <a href="https://github.com/ComparativeGenomicsToolkit/cactus/releases/download/v2.7.1/cactus-v2.7.1.tar.gz">cactus-v2.7.1.tar.gz</a> Install instructions in <a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/v2.7.1/README.md">README.md</a></li> </ul> <p><strong>WARNING</strong>: do not use the github automatically generated source files (<code>Source code (zip)</code> or <code>Source code (tar.gz)</code>), these are not correct.</p> <p><strong>The Docker images and binaries linked above are built using <a href="https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#Advanced_Vector_Extensions_2">AVX2 extensions</a>, and require a CPU that supports them, except the "Pre-compiled Binaries For Older CPU Architectures" which should be compatible with any 64-bit architecture (and, since version 2.3.1, support Cactus's pangenome pipeline).</strong></p> <p>Please subscribe to the <a href="https://groups.google.com/d/forum/cactus-announce">cactus-announce</a> low-volume mailing list to receive notice of Cactus release.</p> <h2>Release Notes</h2> <p>This release adds some options to tune outgroup selection, as well as updates many included dependencies and tools</p> <ul> <li>Add <code>--chromInfo</code> option to specify sex chromosomes of input genomes, in order to make sure outgroups are selected accordingly</li> <li>Add <code>--maxOutgroups</code> option so that the number of outgroups can be toggled via the command line (previously required using a modified configuration file).</li> <li>Update to Toil 6.0.0</li> <li>Update to vg 1.54.0</li> <li>Update to odgi 0.8.4</li> <li>Update to latest taffy (fixing bug in paf export)</li> <li>Update to abPOA 1.5.1</li> <li>Fix Dockerfile so that Phast binaries are included</li> <li><code>--indexMemory</code> now acts as upper limit on chromosome-level jobs.</li> </ul&gt

A strategy for building and using a human reference pangenome.

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript