https://github.com/edawson/svaha
Convert structural variants to sequence graphs [ VCF + FASTA ---> GFA ]
https://github.com/edawson/svaha
gfa graph variation-graph vcf
Last synced: 30 days ago
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Convert structural variants to sequence graphs [ VCF + FASTA ---> GFA ]
- Host: GitHub
- URL: https://github.com/edawson/svaha
- Owner: edawson
- License: mit
- Created: 2016-08-14T13:30:17.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-03-06T15:32:03.000Z (about 1 year ago)
- Last Synced: 2025-04-03T10:38:05.116Z (about 2 months ago)
- Topics: gfa, graph, variation-graph, vcf
- Language: C++
- Size: 10.6 MB
- Stars: 12
- Watchers: 1
- Forks: 1
- Open Issues: 5
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
svaha - generate variation graphs for structural variants.
-----------
Make variation graphs from structural variants:
[x] Deletions
[x] Inversions
[x] Insertions
[x] SNPs
[ ] Duplications
[ ] Transversions
[ ] Breakpoints
Don't worry: we'll be adding these as time permits.
## What is svaha?
svaha is a small program that converts Variant Call Format (VCF) records into [Graphical Fragment Assembly](https://github.com/pmelsted/GFA-spec) format (i.e. sequence graphs like those in [vg](https://github.com/vgteam/vg)). It does so using a minimal single-base graph representation, the world's smallest and least-safe VCF parser (well, probably), and almost no dependencies.
## Build it
svaha brings in its own libraries, except for zlib. Make sure to have zlib installed.
It uses a frozen version of htslib and floating versions of gfakluge. To build svaha:
git clone --recursive https://github.com/edawson/svaha
make
and that should do it.
## Run svaha
svaha takes a FASTA file and a VCF as arguments:
```./svaha -r MYFASTA.fa -v MYVARIATION.vcf```
and outputs sorted GFA, which is text-based and easily exchangeable to other, more useful programs (like vg).
## Options
`-r`: a fasta reference
`-v`: a vcf containing variants (must be relative to the given fasta)
`-m`: maximum node size. When creating graphs for vg, make sure to use a maximum node size of between 32 and 1023.
1023 is a hard limit (nothing 1024 or over will be indexable) and below 32 the graph begins to eat tons of memory. I tend to use `-m 64` or `-m 128`.
## Workflows
1. Build a variation graph with svaha containing structural variants
2. Reduce node size with a ``cat result.gfa | vg view -F -v - | vg mod -X 1000 - > new_graph.vg`` to make the resulting graph indexable with GCSA2.
3. Map reads to that graph using ```vg map```
4. Call variants using ```vg call``` or ```vg genotype```
## Get help
Reach out to me (@edawson) on GitHub and I'll do my best to help!