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DTSTAMP:20260421T090515Z
LOCATION:Bldg. 6 - Room 103
DTSTART;TZID=Europe/Stockholm:20260629T173000
DTEND;TZID=Europe/Stockholm:20260629T180000
UID:submissions.pasc-conference.org_PASC26_sess158_msa180@linklings.com
SUMMARY:Open-source GPU Computing Methods for Accelerated Nanopore Sequenc
 ing Data Analysis
DESCRIPTION:Hasindu Gamaarachchi (UNSW Sydney, Garvan Institute of Medical
  Research)\n\nAcross life sciences, DNA and RNA sequencing have become ess
 ential, enabling progress in areas such as precision medicine, agriculture
 , biosecurity and forensics. Among the latest innovations, third-generatio
 n Nanopore sequencing stands out for its ability to produce ultra-long rea
 ds and detect epigenetic modifications, along with several other advantage
 s. Unlike traditional sequencers, Nanopore devices measure electrical curr
 ent as DNA/RNA strands pass through nanoscale pores. These raw signal trac
 es are then computationally decoded into nucleotide sequences and epigenet
 ic markers. For large genomes such as humans, signal data can exceed a ter
 abyte for a single sample, making analysis at scale highly challenging. To
  address this, researchers leverage massively parallel GPUs for efficient 
 processing. In this talk, I will present computational methods for Nanopor
 e signal analysis and open-source GPU-accelerated research software packag
 es we developed to enable scalable, high-performance nanopore sequencing a
 nalysis workflows.\n\nDomain: Engineering, Life Sciences, Computational Me
 thods and Applied Mathematics\n\nSession Chairs: Gagandeep Singh (AMD), Sr
 iranjani Sitaraman (AMD), Bertil Schmidt (Johannes Gutenberg University Ma
 inz), Kristof Denolf (AMD), Mittul Singh (AMD), and Yijie Xu (AMD)\n\n
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