English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

FM-indexes are a crucial data structure in DNA alignment, for example, butsearching with them usually takes at least one random access per character inthe query pattern. Ferragina and Fischer observed in 2007 that word-basedindexes often use fewer random accesses than character-based indexes, and thussupport faster searches. Since DNA lacks natural word-boundaries, however, itis necessary to parse it somehow before applying word-based FM-indexing. Lastyear, Deng et al.\ proposed parsing genomic data by induced suffix sorting, andshowed the resulting word-based FM-indexes support faster counting queries thanstandard FM-indexes when patterns are a few thousand characters or longer. Inthis paper we show that using prefix-free parsing -- which takes parametersthat let us tune the average length of the phrases -- instead of induced suffixsorting, gives a significant speedup for patterns of only a few hundredcharacters. We implement our method and demonstrate it is between 3 and 18times faster than competing methods on queries to GRCh38. And was consistentlyfaster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence,it is very clear that our method accelerates the performance of count over allstate-of-the-art methods with a minor increase in the memory. Our source codeis available at https://github.com/marco-oliva/afm .

Acceleration of FM-index Queries Through Prefix-free Parsing