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NAIBR (Novel Adjacency Identification with Barcoded Reads) identifies novel adjacencies created by structural variation events such as deletions, duplications, inversions, and complex rearrangements using linked-read whole-genome sequencing data produced by 10X Genomics. Please refer to the publication for details about the method.

NAIBR takes as in put a BAM file produced by 10X Genomic's Long Ranger pipeline and outputs a BEDPE file containing predicted novel adjacencies and a likelihood score for each adjacency.

Installing NAIBR

git clone

NAIBR is written in python 2.7 and requires the following dependencies: pysam, numpy, scipy, subprocess, and matplotlib

Running NAIBR

NAIBR can be run using the following command:

python <configfile>

A template config file can be found in example/example.config. The following parameters can be set in the config file:

  • bam_file: Input BAM file < required >
  • min_mapq: Minimum mapping quality for a read to be included in analysis (default: 40)
  • outdir: Output directory (default: . )
  • d: The maximum distance between reads in a linked-read
  • blacklist: tap separated list of regions to be excluded from analysis (default: None)
  • candidates: List in BEDPE format of novel adjacencies to be scored by NAIBR. This will override automatic detection of candidate novel adjacencies.
  • threads: Number of threads (default: 1)
  • min_sv: Minimum size of a structural variant to be detected (default: lmax, the 95th percentile of the paired-end read insert size distribution)
  • k: minimum number of barcode overlaps supporting a candidate NA (default = 3)


NAIBR outputs a BEDPE file containing all novel scored novel adjacencies. Predicted novel adjacencies with scores greater than the threshold c are labelled 'PASS' and others are labelled 'FAIL'.


Example files are provided in the 'example' directory. Running

python example/example.config

will produce the file 'example/NAIBR_SVs.bedpe'.

Citing NAIBR

Elyanow, Rebecca, Hsin-Ta Wu, and Benjamin J. Raphael. "Identifying structural variants using linked-read sequencing data." Bioinformatics (2017).

  title={Identifying structural variants using linked-read sequencing data},
  author={Elyanow, Rebecca and Wu, Hsin-Ta and Raphael, Benjamin J},


Novel Adjacency Identification with Barcoded Reads




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