IGLOSS: iterative gapless local similarity search
Braslav Rabar, Strahil Ristov, Maja Zagor\v{s}\v{c}ak, Martin, Rosenzweig, Pavle Goldstein

TL;DR
IGLOSS is a high-sensitivity web-based tool designed for iterative local sequence similarity searches, addressing the need for exploring local patterns in the rapidly growing sequence data in bioinformatics.
Contribution
The paper introduces IGLOSS, a novel iterative local similarity search method that enhances detection of sequence patterns without relying heavily on pre-existing information.
Findings
High sensitivity in local sequence pattern detection
Effective in exploring large-scale sequence data
Web-based interface for accessibility
Abstract
Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a high sensitivity web-based iterative local similarity scanner, that finds sequence patterns similar to a submitted query.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
