ReadBouncer: precise and scalable adaptive sampling for nanopore sequencing | Zendy

Jens-Uwe Ulrich | Zendy; Ahmad Lutfi | Zendy; Kilian Rutzen | Zendy; Bernhard Y. Renard | Zendy

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ReadBouncer: precise and scalable adaptive sampling for nanopore sequencing

Author(s) -

Jens-Uwe Ulrich,

Ahmad Lutfi,

Kilian Rutzen,

Bernhard Y. Renard

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac223

Subject(s) - computer science , nanopore sequencing , scalability , source code , sampling (signal processing) , adaptive sampling , field (mathematics) , nanopore , data mining , computer engineering , dna sequencing , programming language , filter (signal processing) , operating system , biology , computer vision , dna , statistics , genetics , mathematics , pure mathematics , monte carlo method , materials science , nanotechnology

Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications.

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