Name: NAIBR
Owner: raphael-group
Description: Novel Adjacency Identification with Barcoded Reads
Created: 2017-05-09 01:01:51.0
Updated: 2018-01-02 02:59:52.0
Pushed: 2017-12-07 16:58:21.0
Homepage: null
Size: 8093
Language: Python
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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.
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:
on NAIBR.py <configfile>
A template config file can be found in example/example.config. The following parameters can be set in the config file:
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
on 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).
icle{elyanow2017identifying,
tle={Identifying structural variants using linked-read sequencing data},
thor={Elyanow, Rebecca and Wu, Hsin-Ta and Raphael, Benjamin J},
urnal={Bioinformatics},
ar={2017}