Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908420667342848 |
|---|---|
| author | Krummenacher, Reto Simsek, Osman Seckin Leemann, Michèle Alexander, Leila T. Schwede, Torsten Ciorba, Florina M. Pereira, Joana |
| author_facet | Krummenacher, Reto Simsek, Osman Seckin Leemann, Michèle Alexander, Leila T. Schwede, Torsten Ciorba, Florina M. Pereira, Joana |
| contents | GCsnap2 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 mpi4py[.]futures, GCsnap2 Cluster achieved a 22x improvement in execution time and can now perform genomic context analysis for hundreds of thousands of input sequences in HPC clusters. Its modular architecture enables the creation of task-specific workflows and flexible deployment in various computational environments, making it well suited for bioinformatics studies of large-scale datasets. This work highlights the potential for applying similar approaches to solve scalability challenges in other scientific domains that rely on large-scale data analysis pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters Krummenacher, Reto Simsek, Osman Seckin Leemann, Michèle Alexander, Leila T. Schwede, Torsten Ciorba, Florina M. Pereira, Joana Distributed, Parallel, and Cluster Computing J.3; C.2.4 GCsnap2 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 mpi4py[.]futures, GCsnap2 Cluster achieved a 22x improvement in execution time and can now perform genomic context analysis for hundreds of thousands of input sequences in HPC clusters. Its modular architecture enables the creation of task-specific workflows and flexible deployment in various computational environments, making it well suited for bioinformatics studies of large-scale datasets. This work highlights the potential for applying similar approaches to solve scalability challenges in other scientific domains that rely on large-scale data analysis pipelines. |
| title | Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters |
| topic | Distributed, Parallel, and Cluster Computing J.3; C.2.4 |
| url | https://arxiv.org/abs/2505.02195 |