Benchmarking of de novo assembly algorithms for Nanopore data reveals optimal performance of OLC approaches
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2164
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Benchmarking of de novo assembly algorithms for Nanopore data reveals optimal performance of OLC approaches ; volume:17 ; number:7 ; day:22 ; month:8 ; year:2016 ; pages:95-105 ; date:8.2016
BMC genomics ; 17, Heft 7 (22.8.2016), 95-105, 8.2016
- Creator
-
Cherukuri, Yesesri
- Contributor
-
Janga, Sarath Chandra
SpringerLink (Online service)
- DOI
-
10.1186/s12864-016-2895-8
- URN
-
urn:nbn:de:1111-201609194243
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:01 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cherukuri, Yesesri
- Janga, Sarath Chandra
- SpringerLink (Online service)
Other Objects (12)
Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing
Benchmarking of Optimization Algorithms
Author Correction: Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking evolutionary multiobjective optimization algorithms
Benchmarking homogenization algorithms for monthly data
Benchmarking satellite-derived shoreline mapping algorithms
De novo Nanopore read quality improvement using deep learning
Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities
Nanopore sequencing and full genome de novo assembly of human cytomegalovirus TB40/E reveals clonal diversity and structural variations
Benchmarking evolutionary algorithms: Towards exploratory landscape analysis
A high-quality de novo genome assembly based on nanopore sequencing of a wild-caught coconut rhinoceros beetle (Oryctes rhinoceros)
Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing
Benchmarking of Optimization Algorithms
Author Correction: Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking evolutionary multiobjective optimization algorithms
Benchmarking homogenization algorithms for monthly data
Benchmarking satellite-derived shoreline mapping algorithms
De novo Nanopore read quality improvement using deep learning
Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities
Nanopore sequencing and full genome de novo assembly of human cytomegalovirus TB40/E reveals clonal diversity and structural variations
Benchmarking evolutionary algorithms: Towards exploratory landscape analysis
A high-quality de novo genome assembly based on nanopore sequencing of a wild-caught coconut rhinoceros beetle (Oryctes rhinoceros)
Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing
Benchmarking of Optimization Algorithms
Author Correction: Rapid de novo assembly of the European eel genome from nanopore sequencing reads
Benchmarking evolutionary multiobjective optimization algorithms
Benchmarking homogenization algorithms for monthly data
Benchmarking satellite-derived shoreline mapping algorithms
De novo Nanopore read quality improvement using deep learning
Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities
Nanopore sequencing and full genome de novo assembly of human cytomegalovirus TB40/E reveals clonal diversity and structural variations
Benchmarking evolutionary algorithms: Towards exploratory landscape analysis