# Call Structural Variants using NAIBR

(like indels, insertions, duplications)

sample1 pop1
sample2 pop1
sample3 pop2
sample4 pop1
sample5 pop3
#sample6    pop4

After reads have been aligned, e.g. with align bwa , you can use those alignment files (.bam) to call structural variants in your data using NAIBR. While our testing shows that NAIBR tends to find known inversions that LEVIATHAN misses, the program requires haplotype phased bam files as input. That means the alignments have a PS or HP tag that indicate which haplotype the read/alignment belongs to. If your alignments don't have phasing tags (none of the current aligners in Harpy do this), then you will need to do a little extra work for NAIBR to work best with your data. This process is described below.

harpy sv naibr OPTIONS... INPUTS...

# input bams already phased
harpy sv naibr --threads 20 --genome genome.fasta Align/bwa

# input bams require phasing
harpy sv naibr --threads 20 --genome genome.fasta --vcf Variants/data.vcf.gz Align/bwa

In addition to the common runtime options , the sv naibr module is configured using these command-line arguments:

The --molecule-distance option is used to let the program determine how far apart alignments on a contig with the same barcode can be from each other and still considered as originating from the same DNA molecule. See haplotag data for more information on what this value does.

When not using a population grouping file via --populations, variants will be called per-sample. Due to the nature of structural variant VCF files, there isn't an entirely fool-proof way of combining the variants of all the samples into a single VCF file, therefore the output will be a VCF for every sample.

With the inclusion of a population grouping file via --populations, Harpy will merge the bam files of all samples within a population and call variants on these alignment pools. Preliminary work shows that this way identifies more variants and with fewer false positives. However, individual-level information gets lost using this approach, so you will only be able to assess group-level variants, if that's what your primary interest is.

In order to get the best variant calling performance out of NAIBR, it requires phased bam files as input. Using --vcf is optional and not used by NAIBR directly. However, to use sv naibr with bam files that are not phased, you will need to include a phased VCF file with --vcf, which Harpy uses with whatshap haplotag to phase your input BAM files prior to variant calling. See the whatshap documentation for more details on that process.

This file can be in vcf/vcf.gz/bcf format and most importantly it must be phased haplotypes. There are various ways to haplotype SNPs, but you can use harpy phase to phase your SNPs into haplotypes using the haplotag barcode information. The resulting phased VCF file can then be used as input here. Your VCF file should be filtered in some capacity to keep high quality data.

---
title: Calling variants with NAIBR, starting with unphased alignments
---
graph LR
    subgraph id2 ["You do this part"]
        aln[alignments]:::clean-->|harpy snp|snps([SNPs]):::clean
        snps-->|bcftools filter -i 'QUAL>95' ...|filt([filtered SNPs]):::clean
        filt-->|harpy phase|phase([phased haplotypes]):::phase
    end
    id2-->|harpy sv naibr|id1
    subgraph id1 ["Harpy does this part"]
        phase2([phased haplotypes]):::phase-->|whatshap haplotag|aln2:::clean
        aln2([phased alignments])-->|NAIBR|results((structural variants)):::clean
    end
    style id1 fill:#f0f0f0,stroke:#e8e8e8,stroke-width:2px
    style id2 fill:#dfe3ee,stroke:#c8ccd6,stroke-width:2px
    classDef phase fill:#b7c9ef,stroke:#dfe3ee,stroke-width:2px
    classDef clean fill:#f5f6f9,stroke:#b7c9ef,stroke-width:2px

Naibr is a variant caller that uses linked read barcode information to call structural variants (indels, inversions, etc.) exclusively, meaning it does not call SNPs. The original authors of Naibr have not been updating or improving it, so Harpy uses an active fork of it that is available on Bioconda under the name naibr-plus. This fork includes improved accuracy as well as quality-of-life updates.

graph LR
    subgraph id1 ["Phase"]
        aln[unphased alignments]:::clean---vcf[phased VCF]:::clean
    end
    id1-->phased([phased alignments]):::clean
    subgraph id2 ["Population calling"]
        popsplit([merge by population]):::clean
    end
    phased-->id2
    popsplit-->A
    phased-->A
    A([index alignments]):::clean --> B([NAIBR]):::clean
    Z([create config file]):::clean --> B
    popsplit --> Z
    phased --> Z
    
    style id2 fill:#f0f0f0,stroke:#e8e8e8,stroke-width:2px
    style id1 fill:#f0f0f0,stroke:#e8e8e8,stroke-width:2px
    classDef clean fill:#f5f6f9,stroke:#b7c9ef,stroke-width:2px

The default output directory is SV/naibr with the folder structure below. sample1 and sample2 are generic sample names for demonstration purposes. The resulting folder also includes a workflow directory (not shown) with workflow-relevant runtime files and information.

SV/naibr
├── deletions.bedpe
├── duplications.bedpe
├── inversions.bedpe
├── bedpe
│   ├── sample1.bedpe
│   └── sample2.bedpe
├── configs
│   ├── sample1.config
│   └── sample2.config
├── filtered
│   ├── sample1.fail.bedpe
│   └── sample2.fail.bedpe
├── IGV
│   ├── sample1.reformat.bedpe
│   └── sample2.reformat.bedpe
├── logs
│   ├── sample1.log
│   └── sample2.log
├── reports
│   ├── sample1.naibr.html
│   └── sample2.naibr.html
└── vcf
    ├── sample1.vcf
    └── sample2.vcf

item	description
deletions.bedpe	an aggregation of all the deletions identified by NAIBR
duplications.bedpe	an aggregation of all the duplications identified by NAIBR
inversions.bedpe	an aggregation of all the inversions identified by NAIBR
bedpe/	structural variants identified by NAIBR
configs/	the configuration files harpy generated for each sample
filtered/	the variants that failed NAIBR's internal filters
IGV/	same as the output .bedpe files but in IGV format
logs/*.log	what NAIBR writes to stderr during operation
reports/	summary reports with interactive plots of detected SV
vcf/	the resulting variants, but in .VCF format

By default, Harpy runs naibr with these parameters (excluding inputs and outputs):

min_mapq = 30
min_sv   = 100000
k        = 2
d        = 100000

Below is a list of all naibr runtime options, excluding those Harpy already uses or those made redundant by Harpy's implementation of NAIBR. These are taken directly from the NAIBR documentation. If adding these arguments, do so in quotes:

harpy sv naibr -x "candidates duplications.bedpe" data/alignments/*

NAIBR arguments

-blacklist: BED-file with regions to be excluded from analysis
-candidates: BEDPE-file with novel adjacencies to be scored by NAIBR. This will override automatic detection of candidate novel adjacencies

These are the summary reports Harpy generates for this workflow. You may right-click the image and open it in a new tab if you wish to see the example in better detail.

Variant stats

Summarizes the count and type of structural variants and visualizes their locations on the chromosomes. Calling variants on population-pooled samples will instead report on populations.