OPERA-gSAM: Big Data Processing Framework for UMI Sequencing at High Scalability and Efficiency-Reference-Cited by-同舟云学术

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OPERA-gSAM: Big Data Processing Framework for UMI Sequencing at High Scalability and Efficiency

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)
language:
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Author:

Caderno Pablo Vazquez¹,Awaysheh Feras M.²,Colino-Sanguino Yolanda³,Fuente Laura Rodriguez De La³,Valdes-Mora Fatima³,Cabaleiro Jose C.¹,Pena Tomas F.¹,Gallego-Ortega David⁴

Affiliation:

1. University of Santiago de Compostela,CiTIUS research center,Spain

2. Institute of Computer Science, Delta Center University of Tartu,Tartu,Estonia

3. Cancer Epigenetic Biology and Therapeutics Laboratory, Children’s Cancer Institute,Sydney,Australia

4. The Kinghorn Cancer Centre, Garvan Institute of Medical Research,Sydney,Australia

Funder

European Social Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10181139/10181105/10181150.pdf?arnumber=10181150

Reference36 articles.

1. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

2. EME: An Automated, Elastic and Efficient Prototype for Provisioning Hadoop Clusters On-demand

3. ATAC-seq with unique molecular identifiers improves quantification and footprinting

4. Spark: Cluster computing with working sets;zaharia;HotCloud,2010

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A cognitive deep learning approach for medical image processing;Scientific Reports;2024-02-24

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