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https://github.com/Illumina/paragraph
Graph realignment tools for structural variants
https://github.com/Illumina/paragraph
genotyping htslib structural-variation variant-calling vcf
Last synced: 3 months ago
JSON representation
Graph realignment tools for structural variants
- Host: GitHub
- URL: https://github.com/Illumina/paragraph
- Owner: Illumina
- License: other
- Created: 2017-11-24T17:39:25.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T18:33:38.000Z (almost 2 years ago)
- Last Synced: 2024-03-26T20:19:48.853Z (8 months ago)
- Topics: genotyping, htslib, structural-variation, variant-calling, vcf
- Language: C++
- Size: 30.8 MB
- Stars: 143
- Watchers: 20
- Forks: 28
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-pangenomes - Paragraph - based genotyping tools for short read data (A list of software capable of analyzing **eukaryotic** genomes for pangenomics)
README
# Paragraph: a suite of graph-based genotyping tools
* [Introduction](#Introduction)
* [Installation](#Installation)
* [Run Paragraph from VCF](#RunParagraphFromVCF)
* [Test example](#TestExample)
* [Input requirements](#InputRequirements)
* [Run time](#RunTime)
* [Population-scale genotyping](#PopulationScaleGenotyping)
* [Run Paragraph on complex variants](#RunParagraphOnComplexVariants)
* [Further Information](#FurtherInformation)
* [Documentation](#Documentation)
* [External links](#ExternalLinks)
* [License](#License)
Accurate genotyping of known variants is a critical for the analysis of whole-genome sequencing data. Paragraph aims to facilitate this by providing an accurate genotyper for Structural Variations with short-read data.
Please reference Paragraph using:
- Chen, et al (2019) [Paragraph: A graph-based structural variant genotyper for short-read sequence data](https://www.biorxiv.org/content/10.1101/635011v2). *bioRxiv*. doi: https://doi.org/10.1101/635011
Genotyping data in this paper can be found at [paper-data/download-instructions.txt](paper-data/download-instructions.txt)
For details of population genotyping, please also refer to:
- https://www.illumina.com/science/genomics-research/accurate-genotyping-of-structural-variant.html
Please check [doc/Installation.md](doc/Installation.md) for system requirements and installation instructions.
## Run Paragraph from VCF
### Test example
After installation, run `multigrmpy.py` script from the build/bin directory on an example dataset as follows:
```bash
python3 bin/multigrmpy.py -i share/test-data/round-trip-genotyping/candidates.vcf \
-m share/test-data/round-trip-genotyping/samples.txt \
-r share/test-data/round-trip-genotyping/dummy.fa \
-o test \
```
This runs a simple genotyping example for two test samples.
* **candidates.vcf**: this specifies candidate SV events in a vcf format.
* **samples.txt**: Manifest that specifies some test BAM files. Tab or comma delimited.
* **dummy.fa** a short dummy reference which only contains `chr1`
The output folder `test` then contains gzipped json for final genotypes:
```bash
$ tree test
```
```
test
├── grmpy.log # main workflow log file
├── genotypes.vcf.gz # Output VCF with individual genotypes
├── genotypes.json.gz # More detailed output than genotypes.vcf.gz
├── variants.vcf.gz # The input VCF with unique ID from Paragraph
└── variants.json.gz # The converted graphs from input VCF (no genotypes)
```
If successful, the last 3 lines of genotypes.vcf.gz will the same as in [expected file](share/test-data/round-trip-genotyping/expected-vcf-record.txt).
## Input requirements
### VCF format
paraGRAPH will independently genotype each entry of the input VCF. You can use either indel-style representation (full REF and ALT allele sequence in 4th and 5th columns) or symbolic alleles, as long as they meet the format requirement of VCF 4.0+.
Currently we support 4 symbolic alleles:
- `` for deletion
- Must have END key in INFO field.
- `` for insertion
- Must have a key in INFO field for insertion sequence (without padding base). The default key is SEQ.
- For blockwise swap, we strongly recommend using indel-style representation, other than symbolic alleles.
- `` for duplication
- Must have END key in INFO field. paraGRAPH assumes the sequence between POS and END being duplicated for one more time in the alternative allele.
- `` for inversion
- Must have END key in INFO field. paraGRAPH assumes the sequence between POS and END being reverse-complemented in the alternative allele.
### Sample Manifest
Must be tab-deliemited.
Required columns:
- id: Each sample must have a unique ID. The output VCF will include genotypes for all samples in the manifest
- path: Path to the BAM/CRAM file.
- depth: Average depth across the genome. Can be calculated with bin/idxdepth (faster than samtools).
- read length: Average read length (bp) across the genome.
Optional columns:
- depth sd: Specify standard deviation for genome depth. Used for the normal test of breakpoint read depth. Default is sqrt(5*depth).
- depth variance: Square of depth sd.
- sex: Affects chrX and chrY genotyping. Allow "male" or "M", "female" or "F", and "unknown" (quotes shouldn't be included in the manifest). If not specified, the sample will be treated as unknown.
- On a 30x HiSeqX sample, Paragraph typically takes 1-2 seconds to genotype a simple SV in confident regions.
- If the SV is in a low-complexity region with abnormal read pileups, the running time could vary.
- For efficiency, it is recommended to manually set the "-M" option (maximum allowed read count for a variant) to skip these high-depth regions. We recommend "-M" as 20 times of your mean sample depth.
## Population-scale genotyping
To efficiently genotype SVs across a population, we recommend doing single-sample mode as follows:
- Create a manifest for each single sample
- Run `multigrmpy.py` for each manifest. Be sure to set "-M" option for each sample according to its depth.
- Multithreading (option "-t") is highly recommended for population-scale genotyping
- Merge all `genotypes.vcf.gz` to create a big VCF of all samples. You can use either `bcftools merge` or your custom script.
## Run Paragraph on complex variants
For more complicated events (e.g. genotype a deletion together with its nearby SNP), you can provide a custimized JSON to paraGRAPH:
Please follow the pattern in [example JSON](share/test-data/paragraph/pg-het-ins/pg-het-ins.json) and make sure all required keys are provided. Here is a visualization of this [sample graph](share/test-data/paragraph/pg-het-ins/pg-het-ins.png).
To obtain graph alignments for this graph (including all reads), run:
```bash
bin/paragraph -b \
-r \
-g \
-o \
-E 1
```
To obtain the algnment summary, genotypes of each breakpoint, and the whole graph, run:
```bash
bin/grmpy -m \
-r \
-i \
-o \
-E 1
```
If you have multiple events listed in the input JSON, `multigrmpy.py` can help you to run multiple `grmpy` jobs together.
Please check github wiki for common usage questions and errors.
* More **information about all tools we provide in this package** can be found in
[doc/graph-tools.md](doc/graph-tools.md).
* In [doc/graph-models.md](doc/graph-models.md) we describe the graph and genotyping
models we implement.
* Some developer documentation about our code analysis and testing process can be found in
[doc/linting-and-testing.md](doc/linting-and-testing.md).
* Procedures for read level alignment validation
[doc/validation-with-simulated-reads.md](doc/validation-with-simulated-reads.md).
* How we count reads for variants and paths
[doc/graph-counting.md](doc/graph-counting.md).
* Documentation of genotyping model parameters
[doc/genotyping-parameters.md](doc/genotyping-parameters.md).
* [Doc/graphs-ashg-2017.pdf](doc/graphs-ashg-2017.pdf) contains the poster about this method we showed at
[ASHG 2017](http://www.ashg.org/2017meeting/)
* The [Illumina/Polaris](https://github.com/Illumina/Polaris) repository gives the
short-read sequencing data we used to test our method in population.
The [LICENSE](LICENSE) file contains information about libraries and other tools we use,
and license information for these.