An Efficient Approach to Merging Paired-End Reads and Incorporation of Uncertainties
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BiBTeXSEEK ID: https://publications.h-its.org/publications/215
Research Groups: Computational Molecular Evolution
Publication type: InCollection
Book Title: Algorithms for Next-Generation Sequencing Data
Publisher: Springer
Citation: In Algorithms for Next-Generation Sequencing Data, pp. 299-325, Springer
Date Published: 2017
Registered Mode: imported from a bibtex file
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Created: 7th Sep 2019 at 10:33
Last updated: 5th Mar 2024 at 21:23
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Research Groups: Computational Molecular Evolution
Institutions: Heidelberg Institute for Theoretical Studies
Research Groups: Scientific Databases and Visualisation, Astroinformatics, Data Mining and Uncertainty Quantification, Groups and Geometry, Natural Language Processing, Computational Molecular Evolution, Computational Carbon Chemistry, Physics of Stellar Objects, Playground, Molecular and Cellular Modeling, Stellar Evolution Theory, SIMPLAIX
Institutions: Heidelberg Institute for Theoretical Studies
The Computational Molecular Evolution (CME) group focuses on developing algorithms, computer architectures, and high-performance computing solutions for bioinformatics.
Public web page: Not specified
