https://github.com/raphael-group/NAIBR

Novel Adjacency Identification with Barcoded Reads
https://github.com/raphael-group/NAIBR

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Novel Adjacency Identification with Barcoded Reads

• Host: GitHub
• URL: https://github.com/raphael-group/NAIBR
• Owner: raphael-group
• License: mit
• Created: 2017-05-09T01:01:51.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-04-19T09:40:25.000Z (over 2 years ago)
• Last Synced: 2024-07-31T20:29:55.626Z (5 months ago)
• Language: Python
• Size: 7.72 MB
• Stars: 14
• Watchers: 13
• Forks: 4
• Open Issues: 19
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-linked-reads - NAIBR - commit/raphael-group/NAIBR?label=%20)<br /> (Tools)

README

        
## Overview

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](https://doi.org/10.1093/bioinformatics/btx712) 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 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

## Running NAIBR

NAIBR can be run using the following command:

```

python NAIBR.py 

```

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)

## Output

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

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

```

python NAIBR.py 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).

```

@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}

}

```