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)