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1. kegg_pull: a software package for the RESTful access and pulling from the Kyoto Encyclopedia of Gene and Genomes

   https://doi.org/10.1186/s12859-023-05208-0  

   Huckvale, Erik; Moseley, Hunter N. (December 2023, BMC Bioinformatics)

   Abstract Background The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides organized genomic, biomolecular, and metabolic information and knowledge that is reasonably current and highly useful for a wide range of analyses and modeling. KEGG follows the principles of data stewardship to be findable, accessible, interoperable, and reusable (FAIR) by providing RESTful access to their database entries via their web-accessible KEGG API. However, the overall FAIRness of KEGG is often limited by the library and software package support available in a given programming language. While R library support for KEGG is fairly strong, Python library support has been lacking. Moreover, there is no software that provides extensive command line level support for KEGG access and utilization. Results We present kegg_pull, a package implemented in the Python programming language that provides better KEGG access and utilization functionality than previous libraries and software packages. Not only does kegg_pull include an application programming interface (API) for Python programming, it also provides a command line interface (CLI) that enables utilization of KEGG for a wide range of shell scripting and data analysis pipeline use-cases. As kegg_pull’s name implies, both the API and CLI provide versatile options for pulling (downloading and saving) an arbitrary (user defined) number of database entries from the KEGG API. Moreover, this functionality is implemented to efficiently utilize multiple central processing unit cores as demonstrated in several performance tests. Many options are provided to optimize fault-tolerant performance across a single or multiple processes, with recommendations provided based on extensive testing and practical network considerations. Conclusions The new kegg_pull package enables new flexible KEGG retrieval use cases not available in previous software packages. The most notable new feature that kegg_pull provides is its ability to robustly pull an arbitrary number of KEGG entries with a single API method or CLI command, including pulling an entire KEGG database. We provide recommendations to users for the most effective use of kegg_pull according to their network and computational circumstances. 

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2. caltechdata_api – First release of CaltechDATA CLI

   https://doi.org/10.22002/9w6h3-4d081  

   Morrell, Thomas E; Rohan, Bhattarai; Elizabeth, Won (June 2024, CaltechDATA)

   This release makes a new command line interface (CLI) available for CaltechDATA. It allows you to create and edit records in CaltechDATA using the API entirely from the command line. You can create metadata entirely on the command line, or utilize a README template https://github.com/caltechlibrary/caltechdata_api/blob/main/templates/README.md, or use the previously supported json file https://github.com/caltechlibrary/caltechdata_api/blob/main/example.json. As we bring online new storage options online for Caltech, the CLI will be updated and enhanced. 

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3. gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support

   https://doi.org/10.1371/journal.pcbi.1009835  

   Irrgang, M. Eric; Davis, Caroline; Kasson, Peter M. (February 2022, PLOS Computational Biology)

   Schneidman-Duhovny, Dina (Ed.)

   Gmxapi provides an integrated, native Python API for both standard and advanced molecular dynamics simulations in GROMACS. The Python interface permits multiple levels of integration with the core GROMACS libraries, and legacy support is provided via an interface that mimics the command-line syntax, so that all GROMACS commands are fully available. Gmxapi has been officially supported since the GROMACS 2019 release and is enabled by default in current versions of the software. Here we describe gmxapi 0.3 and later. Beyond simply wrapping GROMACS library operations, the API permits several advanced operations that are not feasible using the prior command-line interface. First, the API allows custom user plugin code within the molecular dynamics force calculations, so users can execute custom algorithms without modifying the GROMACS source. Second, the Python interface allows tasks to be dynamically defined, so high-level algorithms for molecular dynamics simulation and analysis can be coordinated with loop and conditional operations. Gmxapi makes GROMACS more accessible to custom Python scripting while also providing support for high-level data-flow simulation algorithms that were previously feasible only in external packages. 

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4. Coffea-casa: an analysis facility prototype

   https://doi.org/10.1051/epjconf/202125102061  

   Adamec, Matous; Attebury, Garhan; Bloom, Kenneth; Bockelman, Brian; Lundstedt, Carl; Shadura, Oksana; Thiltges, John (January 2021, EPJ Web of Conferences)

   Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A. (Ed.)

   Data analysis in HEP has often relied on batch systems and event loops; users are given a non-interactive interface to computing resources and consider data event-by-event. The “Coffea-casa” prototype analysis facility is an effort to provide users with alternate mechanisms to access computing resources and enable new programming paradigms. Instead of the command-line interface and asynchronous batch access, a notebook-based web interface and interactive computing is provided. Instead of writing event loops, the columnbased Coffea library is used. In this paper, we describe the architectural components of the facility, the services offered to end users, and how it integrates into a larger ecosystem for data access and authentication. 

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5. dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

   https://doi.org/10.1101/2023.06.15.545182  

   Huang, Xin; Struck, Travis J; Davey, Sean W; Gutenkunst, Ryan N (June 2023, bioRxiv)

   Abstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/. 

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