A machine learning model for accurate and efficient germline small-variants detection
Sentieon DNAscope combines the robust and well-established preprocessing and assembly mathematics of the GATK’s HaplotypeCaller with a machine-learned genotyping model, achieving superb SNP and insertion/deletion accuracy as compared to state-of-the-art tools, while using much reduced computational cost.
- Goal of a machine learning model in DNAscope
- Highlights
- DNAscope machine learning pipeline
- Performance on Whole Genome Sequencing (WGS) data
From Sentieon software version 201808.01 onwards, DNAscope allows you to use a model to perform variant calling with higher accuracy by improving the candidate detection and filtering.
Sentieon can provide you with a model trained using a subset of the data from the GIAB truth-set found in https://github.com/genome-in-a-bottle. The model was created by processing samples HG001 and HG005 through a pipeline consisting of Sentieon BWA-mem alignment and Sentieon deduplication, and using the variant calling results to calibrate a model to fit the truth-set. Sentieon also provides DNAscope model training tool for you to create your model based on your own data.
- High Accuracy
- Rapid Turnaround Time
- Cost Reduction
- Easy Deployment
- Customizable Model
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Sentieon license
Update Sentieon packages location and license file in
dnascope.sh.export SENTIEON_INSTALL_DIR=/home/release/sentieon-genomics-201808.06 #your Sentieon package location export SENTIEON_LICENSE=/home/bundle/sentieon.lic #your license file location
If you do not have a Sentieon license/package yet, please feel free to request a free trial at sentieon.com.
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Location of input files
Before running, you need to set the following variables in
dnascope.sh.-
fastq_folder: fastq file(s) folder -
fastq_1: fastq file name -
fastq_2: second fastq file, if using Illumina paired data -
model: DNAscope model file -
PCRFREE: boolean to indicate whether the sample is PCR Free or not. Set totruefor PCR Free samples. -
fasta: reference file -
dbsnp: dbSNP file
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Running the pipeline
sh dnascope.sh
If you have any further question, please refer to Sentieon's Appnotes for DNAscope Machine Learning Model and DNAseq pipeline example script in the manual.
Here we demonstrate DNAscope's performance on PrecisionFDA Truth Challenge HG002 sample.
Sentieon packages/models are stored on AWS. You could get Sentieon tools by running:
SENTIEON_VERSION="version-you-want" #For example 201808.07, Find released versions here https://support.sentieon.com/manual/appendix/releasenotes/
INSTALL_DIR="your-install-dir"
wget -nv -O - "https://s3.amazonaws.com/sentieon-release/software/sentieon-genomics-${SENTIEON_VERSION}.tar.gz" | tar -zxf - -C ${INSTALL_DIR} Sentieon Models:
https://s3.amazonaws.com/sentieon-release/other/SentieonDNAscopeModel1.0.model- Reference Genome: hs37d5.fa.
- Test Data: NIST HG002 from PrecisionFDA Truth Challenge.
- Model File: Sentieon DNAscope Machine Learning Model version 0.5.
- Truth VCF and BED file for evaluation: NIST GIAB project, version v3.3.2. (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/)
You could access these data from our google cloud bucket:
| File | Location |
|---|---|
| Reference Genome | gs://sentieon-test/pipeline_test/reference/ |
| PrecisionFDA Truth Challenge HG002 FASTQs | gs://sentieon-dnascope-model/data/ |
| DNAscope Model | gs://sentieon-dnascope-model/models/SentieonModelBeta0.5.model |
| Truth VCF and Bed files | gs://sentieon-dnascope-model/truth/ |
We use RTG's vcfeval + hap.py (https://github.com/Illumina/hap.py) for variants evaluation, same comparison methodology as used in precisionFDA Truth Challenge.
HAPPY="/opt/hap.py/bin/hap.py"
OUTPREFIX="happy_eval"
$HAPPY truth.vcf query.vcf -f truth.bed -o $OUTPREFIX -r hs37d5.fa --engine=vcfeval --engine-vcfeval-template hs37d5.sdfYou could find DNAscope output as well as the hap.py evaluation results published on Google Storage Buckets gs://sentieon-dnascope-model/
under output/ and happy_eval/ directories.
| Type | TP | FN | FP | Recall | Precision | F1_score | PrecisionFDA Truth Challenge Winning Fscore* |
|---|---|---|---|---|---|---|---|
| SNP | 3046378 | 1459 | 700 | 0.999521 | 0.99977 | 99.9646% | 99.9587% |
| INDEL | 463754 | 1010 | 685 | 0.997827 | 0.998585 | 99.8206% | 99.4009% |
*PrecisionFDA Truth Challege Result taken from https://precision.fda.gov/challenges/truth/results
If you are interested in other Sentieon Products, please visit www.sentieon.com for more information.