Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 17, Issue 10, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1008950
Keywords
-
Funding
- Ira & Debra Cohen Graduate Fellowship
- NSF [ABI-1458652]
Ask authors/readers for more resources
The paper introduces a new method called MAGUS for aligning large numbers of sequences, with enhancements that allow for faster alignment of larger datasets compared to other methods. Results demonstrate the advantages of MAGUS in both accuracy and speed over other alignment software.
Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at .
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available