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

Motivation: The comparison of diverse genomic datasets is fundamental tounderstanding genome biology. Researchers must explore many large datasets ofgenome intervals (e.g., genes, sequence alignments) to place their experimentalresults in a broader context and to make new discoveries. Relationships betweengenomic datasets are typically measured by identifying intervals thatintersect: that is, they overlap and thus share a common genome interval. Giventhe continued advances in DNA sequencing technologies, efficient methods formeasuring statistically significant relationships between many sets of genomicfeatures is crucial for future discovery.  Results: We introduce the Binary Interval Search (BITS) algorithm, a noveland scalable approach to interval set intersection. We demonstrate that BITSoutperforms existing methods at counting interval intersections. Moreover, weshow that BITS is intrinsically suited to parallel computing architectures suchas Graphics Processing Units (GPUs) by illustrating its utility for efficientMonte-Carlo simulations measuring the significance of relationships betweensets of genomic intervals.

Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections