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.

Documentation

Links