PerCon SFA Snekmer KBase Narrative App
- 1. Pacific Northwest National Laboratory [ROR:05h992307]
Contributors
Data curator:
- 1. Pacific Northwest National Laboratory [ROR:05h992307]
Description
The Snekmer KBase App is an extension toolkit that allows for running supervised modeling and unsupervised clustering from protein family scores and/or searching unknown sequences for protein families using the Snekmer workflow within the KBase narrative user interface.
- Programming Languages: 99.0% Python, 1.0% Other
- Related Licenses: MIT license
- Dependencies: Requires installation of KBase Software Development Kit (SDK)
- KBase Narrative Information: Snekmer Catalog Status (Running v2.2.4 of the Catalog Server)
- Narrative App Methods:
- Help Documentation: https://kbase.github.io/kb_sdk_docs/
Federal Acknowledgements
This research was supported in part by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of the Genomic Science Program (GSP) as a contribution of the Pacific Northwest National Laboratory (PNNL) Secure Biosystems Design Science Focus Area: Persistence Control of Engineered Functions in Complex Soil Microbiomes (PerCon SFA). Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory managed by the Battelle Memorial Institute Battelle Memorial Institute, operating under the U.S. Department of Energy, Contract DE-AC05-76RL01830.
Disclaimer: This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830.
Notes
Files
Snekmer-KBase-App-main.zip
Files
(78.6 MB)
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Additional details
Related works
- Is derived from
- Software: 10.5281/zenodo.7662597 (DOI)
- Is documented by
- Workflow: https://narrative.kbase.us/#catalog/modules/Snekmer (URL)
- Requires
- Software: https://github.com/kbase/kb_sdk (URL)
References
- Christine H Chang, William C Nelson, Abby Jerger, Aaron T Wright, Robert G Egbert, Jason E McDermott, Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding, Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad005. https://doi.org/10.1093/bioadv/vbad005
Subjects
- software module
- http://semanticscience.org/resource/SIO_000099
- Python script
- http://edamontology.org/format_3996
- machine learning
- https://w3id.org/aio/Machine_Learning
- amino acid protein sequence data
- http://www.ebi.ac.uk/swo/data/SWO_3000067
- sequence analysis
- http://edamontology.org/topic_0080
- unsupervised clustering
- https://w3id.org/aio/Unsupervised_Clustering
- supervised learning
- https://w3id.org/aio/Supervised_Learning