HTSJDK: Enhancing the Java Toolkit for Emerging Sequencing Technologies
To enhance the HTSJDK Java toolkit for genomics with an extensible plugin framework that will enable support for emerging technologies required by contemporary analysis methods, such as long reads, graph/circular references, and epigenetic modifications.
Project Lead: Eric Banks (Broad Institute of MIT and Harvard)
ilastik and Scientific Python Ecosystem: Deep Integration with Other Tools
To integrate ilastik with napari and Dask, replacing the outdated internal viewer and task scheduler by modern, community-supported alternatives with the aim to reduce technical debt, engage with the community, and deliver a superior user experience for the bioimage analysis community.
Project Lead: Anna Kreshuk (European Molecular Biology Laboratory)
ilastik: Faster and More User-Friendly Through Full Pyramid Support
To enable multi-scale interactive machine learning on large datasets in ilastik through full exploitation of state-of-the-art pyramidal file formats and viewers, and extend functionality to other bioimage analysis tools.
Project Lead: Anna Kreshuk (European Molecular Biology Laboratory)
ilastik: Future-Proof Through Stable APIs and Interoperability
To make ilastik more interoperable and its results more reusable and reproducible through the development of Python APIs and general improvement of the third-party developer experience.
Project Lead: Anna Kreshuk (European Molecular Biology Laboratory)
JupyterHub Community Strategic Lead
To broaden participation in the JupyterHub community by establishing a role dedicated to strategy and stewardship for pathways into and throughout the community, as well as programs that provide onboarding and mentorship for historically underrepresented groups.
Project Lead: Chris Holdgraf (NumFOCUS)
JupyterHub Contributor in Residence Program
To improve community support and technical maintenance across the JupyterHub repositories.
Project Lead: Chris Holdgraf (University of California, Berkeley; NumFOCUS)
LinkML: An Open Data Modeling Framework
to increase usability of LinkML (Linked data Modeling Language), an open, extensible framework for modeling, validating, and distributing data that is reusable and interoperable. Note: This proposal was funded by Wellcome Trust as part of our co-funded EOSS Cycle 6.
Project Lead: Sierra Moxon (Lawrence Berkeley National Laboratory)
MACS3, Peak Caller with Single-Cell Resolution
To maintain the established infrastructure and optimize the current features of the popular peak caller MACS for gene regulation studies, while focusing on building the data structure and features for single-cell data analysis.
Project Lead: Tao Liu (Roswell Park Alliance Foundation)
MACS3: A Versatile Peak Caller for Gene Regulation Studies
To enhance the infrastructure to support the continuous development and growing community of the popular algorithm MACS for gene regulation studies, in order to expand its features and adapt to new technologies such as single-cell ATAC-seq.
Project Lead: Tao Liu (Roswell Park Alliance Foundation)