DUDE-Seq specifications

Information


Unique identifier OMICS_19919
Name DUDE-Seq
Alternative name Discrete Universal Denoiser-Sequence
Interface Web user interface
Restrictions to use None
Input format FASTA, FASTQ
Programming languages C, C++, Python
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline Sungroh Yoon <>
  • person_outline Taesup Moon <>

Additional information


The desktop version is available at: https://github.com/datasnu/dude-seq The web application is available at: http://data.snu.ac.kr/pub/dude-seq/#

Information


Unique identifier OMICS_19919
Name DUDE-Seq
Alternative name Discrete Universal Denoiser-Sequence
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Windows
Programming languages C, C++, Python
Computer skills Basic
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Sungroh Yoon <>
  • person_outline Taesup Moon <>

Additional information


The desktop version is available at: https://github.com/datasnu/dude-seq The web application is available at: http://data.snu.ac.kr/pub/dude-seq/#

DUDE-Seq article

DUDE-Seq institution(s)
Electrical and Computer Engineering, Seoul National University, Seoul, Korea; College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea; Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA; Electrical Engineering, Stanford University, Stanford, CA, USA
DUDE-Seq funding source(s)
Supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT and Future Planning) [2014M3A9E2064434 and 2016M3A7B4911115], in part by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare [HI14C3405030014], in part by the Basic Science Research Program through the National Research Foundation of Korea [NRF-2016R1C1B2012170], in part by the ICT R&D program of MSIP/IITP [2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion], and in part by NIH Grant 5U01CA198943-03.

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