NanoMark
NanoMark benchmarks de novo genome assembly methods for nanopore sequencing data to evaluate hybrid and non-hybrid pipeline performance using Escherichia coli K-12 MinION and Illumina datasets.
Key Features:
- Benchmarking Framework: Provides an extensible framework for systematic comparison of genome assemblers using standardized metrics.
- Hybrid and Non-Hybrid Evaluation: Evaluates five non-hybrid assembly pipelines and two hybrid assemblers that scaffold Illumina short-read assemblies with nanopore data.
- Dataset Utilization: Uses publicly available Escherichia coli K-12 datasets sequenced on Oxford Nanopore MinION and Illumina platforms.
- Coverage Analysis: Assesses nanopore coverages at 20×, 30×, 40×, and 50× to determine coverage effects on assembly accuracy.
- Performance Metrics: Reports that hybrid methods are less sensitive to nanopore data quality and perform well at lower coverages, whereas non-hybrid methods require >40× coverage but offer reduced computational time compared to methods designed for nanopore reads.
Scientific Applications:
- Assembly Strategy Selection: Guides choice between hybrid and non-hybrid assembly approaches for projects combining nanopore and Illumina data.
- Coverage Planning: Informs required nanopore coverage (20×–50×) for reliable de novo assembly of bacterial genomes such as Escherichia coli K-12.
- Comparative Performance Assessment: Enables evaluation of assembler sensitivity to nanopore read quality and sequencing coverage.
Methodology:
Tests five non-hybrid and two hybrid assembly pipelines on Escherichia coli K-12 MinION and Illumina datasets across nanopore coverages of 20×, 30×, 40×, and 50× and records standardized performance metrics.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Programming Languages:
- Python
- Added:
- 8/3/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Sović I, Križanović K, Skala K, Šikić M. Evaluation of hybrid and non-hybrid methods for <i>de novo</i> assembly of nanopore reads. Bioinformatics. 2016;32(17):2582-2589. doi:10.1093/bioinformatics/btw237. PMID:27162186.
PMID: 27162186