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.
git clone https://github.com/raphael-group/NAIBR.git
NAIBR is written in python 2.7 and requires the following dependencies: pysam, numpy, scipy, subprocess, and matplotlib
NAIBR can be run using the following command:
python NAIBR.py <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 NAIBR.py example/example.config
will produce the file 'example/NAIBR_SVs.bedpe'.
Elyanow, Rebecca, Hsin-Ta Wu, and Benjamin J. Raphael. "Identifying structural variants using linked-read sequencing data." Bioinformatics (2017).
@article{elyanow2017identifying,
title={Identifying structural variants using linked-read sequencing data},
author={Elyanow, Rebecca and Wu, Hsin-Ta and Raphael, Benjamin J},
journal={Bioinformatics},
year={2017}
}