NucBreak

NucBreak detects structural errors in genome assemblies by analyzing alignments of correctly mapped reads to localize insertions, deletions, duplications, inversions, and inter- and intra-chromosomal rearrangements.


Key Features:

  • Structural Error Detection: Locates structural errors in genome assemblies, including insertions, deletions, duplications, inversions, and inter- and intra-chromosomal rearrangements.
  • Unique Methodology: Analyzes alignments of correctly mapped reads and leverages alternative read alignments rather than focusing on discordantly mapped or soft-clipped reads.
  • Comparative Performance: In benchmarking with simulated and real datasets, demonstrated high sensitivity and a lower false discovery rate compared to Pilon, REAPR, FRCbam, BreakDancer, Lumpy, and Wham.
  • No Error Annotation: Reports accurate localization of detected structural errors without providing annotations of the errors.
  • Technical Implementation: Implemented in Python and uses Bowtie2 for read alignment.

Scientific Applications:

  • Assembly accuracy assessment: Assessing and localizing structural errors to evaluate genome assembly quality.
  • Structural variant detection: Detecting structural variants and rearrangements in genome assemblies.
  • Downstream analysis reliability: Improving the reliability of downstream genomic analyses by identifying assembly errors.

Methodology:

Analyzes alignments of correctly mapped reads, leverages alternative read alignments instead of discordant or soft-clipped reads, performs read alignment with Bowtie2, and is implemented in Python.

Topics

Details

License:
MPL-2.0
Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
3/13/2021

Operations

Publications

Khelik K, Sandve GK, Nederbragt AJ, Rognes T. NucBreak: location of structural errors in a genome assembly by using paired-end Illumina reads. BMC Bioinformatics. 2020;21(1). doi:10.1186/s12859-020-3414-0. PMID:32085722. PMCID:PMC7035700.

PMID: 32085722
PMCID: PMC7035700
Funding: - Universitetet i Oslo: Computational Life Science initiative (CLSi)