CountASAP: a lightweight, easy to use python package for processing ASAPseq data
CountASAP shows good agreement with existing, well-tested data processing tools in the analysis of similarly-structured benchmarking data. CountASAP runs efficiently on a standard laptop, has user-friendly documentation, a one-step installation, and represents the first and only tool designed specifically for the processing of ASAPseq data.
Authors
- Ivan Košík (ORCID: https://orcid.org/0000-0001-5756-5182)
- Vel Murugan (ORCID: https://orcid.org/0000-0002-7368-7742)
- Wilbur H. Chen (ORCID: https://orcid.org/0000-0001-7741-5536)
- Yuko Ohta
- Christopher T. Boughter (ORCID: https://orcid.org/0000-0002-7106-4699)
- Carly Blair
- Budhaditya Chatterjee (ORCID: https://orcid.org/0000-0003-2260-8642)
- Martin Meier‐Schellersheim (ORCID: https://orcid.org/0000-0002-8754-6377)
- Zachary Fasana (ORCID: https://orcid.org/0009-0004-5915-8491)
- Nevil J. Singh (ORCID: https://orcid.org/0000-0002-8384-9290)
- Katrina Gorga
- Adedola Adebamowo (ORCID: https://orcid.org/0009-0003-3931-8219)
- Farah Ammar
- Elizabeth Hill
Institutions
- University of Maryland, Baltimore (US)
- National Institutes of Health (US)
- National Institute of Allergy and Infectious Diseases (US)
- Arizona State University (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1186/s12859-025-06311-0
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00