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DTSTAMP:20250822T115805Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250616T170000
DTEND;TZID=Europe/Stockholm:20250616T173000
UID:submissions.pasc-conference.org_PASC25_sess169_pap118@linklings.com
SUMMARY:Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters
DESCRIPTION:Reto Krummenacher and Osman Seckin Simsek (University of Basel
 ); Michèle Leemann, Leila Alexander, and Torsten Schwede (University of Ba
 sel, Swiss Institute of Bioinformatics); Florina Ciorba (University of Bas
 el); and Joana Pereira (University of Basel, Swiss Institute of Bioinforma
 tics)\n\nGCsnap2 Cluster is a scalable, high performance tool for genomic 
 context analysis, developed to overcome the limitations of its predecessor
 , GCsnap1 Desktop. Leveraging distributed computing with\nmpi4py.futures, 
 GCsnap2 Cluster achieved a 22× improvement in execution time and can now p
 erform genomic context analysis for hundreds of thousands of input sequenc
 es in HPC clusters. Its modular architecture enables the creation of task-
 specific workflows and flexible deployment in various computational enviro
 nments, making it well suited for bioinformatics studies of large-scale da
 tasets.\nThis work highlights the potential for applying similar approache
 s to solve scalability challenges in other scientific domains that rely on
  large-scale data analysis pipelines.\n\nDomain: Engineering, Computationa
 l Methods and Applied Mathematics\n\nSession Chair: Nina Mujkanovic (ETH Z
 urich / CSCS)\n\n
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