From e77f54fb89d203ea2158d760e9ebbf9510ba6f30 Mon Sep 17 00:00:00 2001 From: Kishwar Date: Tue, 15 Mar 2022 22:08:19 -0700 Subject: [PATCH] documentation updates --- .../Oxford_nanopore_r9_whole_genome.md | 8 ++++---- .../img/pepper_r8_ont_HG003_wgs.png | Bin 334988 -> 0 bytes .../img/pmdv_r8_ont_HG003_wgs.png | Bin 0 -> 334736 bytes docs/pipeline_docker/ONT_variant_calling.md | 4 ++-- .../ONT_variant_calling_r10_q20.md | 4 ++-- .../ONT_variant_calling_gpu.md | 4 ++-- .../ONT_variant_calling_r10_q20_gpu.md | 4 ++-- .../ONT_variant_calling_singularity.md | 4 ++-- .../ONT_variant_calling_singularity_r10_q20.md | 4 ++-- .../how_to_train_deepvariant.md | 7 +++++++ 10 files changed, 23 insertions(+), 16 deletions(-) delete mode 100644 docs/performance_evaluation/img/pepper_r8_ont_HG003_wgs.png create mode 100644 docs/performance_evaluation/img/pmdv_r8_ont_HG003_wgs.png diff --git a/docs/performance_evaluation/Oxford_nanopore_r9_whole_genome.md b/docs/performance_evaluation/Oxford_nanopore_r9_whole_genome.md index cac35d84..acef8ae9 100644 --- a/docs/performance_evaluation/Oxford_nanopore_r9_whole_genome.md +++ b/docs/performance_evaluation/Oxford_nanopore_r9_whole_genome.md @@ -34,20 +34,20 @@ run_pepper_margin_deepvariant call_variant \ In all stratified coverages `(30x, 60x, 85x)`, PEPPER-Margin-DeepVariant r0.8 shows increased accuracy:

-PEPPER performance whole genome +PEPPER performance whole genome

##### HG003 30x performance:

-
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 30xr0.7INDEL504501317621186880350840.6295750.9027140.7418
SNP3327495331000217493119860.9947430.9963930.995567
r0.8INDEL504501337206167295536740.6683950.8658630.754422
SNP3327495331304314452124510.9956570.9962570.995957
+
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 30xr0.7INDEL504501317621186880350840.6295750.9027140.7418
SNP3327495331000217493119860.9947430.9963930.995567
r0.8INDEL504501345384159117518420.6846050.8724810.767209
SNP332749533090381845791730.9944530.9972360.995843

##### HG003 60x performance:

-
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 60xr0.7INDEL504501366144138357334840.7257550.918270.810741
SNP332749533174921000385480.9969940.997430.997212
r0.8INDEL504501390595113906471180.774220.8950660.830269
SNP33274953318785871092120.9973820.9972330.997308
+
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 60xr0.7INDEL504501366144138357334840.7257550.918270.810741
SNP332749533174921000385480.9969940.997430.997212
r0.8INDEL504501394987109514446780.7829260.900910.837785
SNP33274953317515998071200.9970010.9978590.99743

##### HG003 85x performance:

-
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 85xr0.7INDEL504501383384121117305950.7599270.9279820.835588
SNP33274953318437905880320.9972780.9975860.997432
r0.8INDEL50450140909695405405390.8108920.9122010.858568
SNP33274953319475802084490.997590.9974620.997526
+
SampleVersionTypeTruth
total
True
positives
False
negatives
False
positives
RecallPrecisionF1-Score
HG003 85xr0.7INDEL504501383384121117305950.7599270.9279820.835588
SNP33274953318437905880320.9972780.9975860.997432
r0.8INDEL50450141216992332386330.8169840.916510.86389
SNP33274953318308918767330.9972390.9979760.997607

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    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:--------:|:---------:|:--------:| -| INDEL | 11256 | 8906 | 2350 | 904 | 0.791222 | 0.909942 | 0.846440 | -| SNP | 71333 | 71257 | 67 | 98 | 0.999061 | 0.998627 | 0.998844 | +| INDEL | 11256 | 8981 | 2275 | 837 | 0.797886 | 0.916692 | 0.853172 | +| SNP | 71333 | 71257 | 94 | 68 | 0.998682 | 0.999047 | 0.998864 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/pipeline_docker/ONT_variant_calling_r10_q20.md b/docs/pipeline_docker/ONT_variant_calling_r10_q20.md index f4b81d7f..f90d6619 100644 --- a/docs/pipeline_docker/ONT_variant_calling_r10_q20.md +++ b/docs/pipeline_docker/ONT_variant_calling_r10_q20.md @@ -152,8 +152,8 @@ ${OUTPUT_DIR}/${OUTPUT_VCF} \ | Type | Truth
    total | True
    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:---------:|:---------:|:--------:| -| INDEL | 11256 | 9442 | 1779 | 821 | 0.841951 | 0.921973 | 0.880147 | -| SNP | 71333 | 71288 | 56 | 56 | 0.999215 | 0.999215 | 0.999215 | +| INDEL | 11256 | 9442 | 1724 | 774 | 0.846837 | 0.926517 | 0.884887 | +| SNP | 71333 | 71288 | 60 | 51 | 0.999159 | 0.999285 | 0.999222 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/pipeline_docker_gpu/ONT_variant_calling_gpu.md b/docs/pipeline_docker_gpu/ONT_variant_calling_gpu.md index a572ca2e..f9b5a53a 100644 --- a/docs/pipeline_docker_gpu/ONT_variant_calling_gpu.md +++ b/docs/pipeline_docker_gpu/ONT_variant_calling_gpu.md @@ -193,8 +193,8 @@ ${OUTPUT_DIR}/${OUTPUT_VCF} \ | Type | Truth
    total | True
    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:--------:|:---------:|:--------:| -| INDEL | 11256 | 8906 | 2350 | 904 | 0.791222 | 0.909942 | 0.846440 | -| SNP | 71333 | 71257 | 67 | 98 | 0.999061 | 0.998627 | 0.998844 | +| INDEL | 11256 | 8981 | 2275 | 837 | 0.797886 | 0.916692 | 0.853172 | +| SNP | 71333 | 71257 | 94 | 68 | 0.998682 | 0.999047 | 0.998864 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/pipeline_docker_gpu/ONT_variant_calling_r10_q20_gpu.md b/docs/pipeline_docker_gpu/ONT_variant_calling_r10_q20_gpu.md index 3f0032b8..811b830d 100644 --- a/docs/pipeline_docker_gpu/ONT_variant_calling_r10_q20_gpu.md +++ b/docs/pipeline_docker_gpu/ONT_variant_calling_r10_q20_gpu.md @@ -193,8 +193,8 @@ ${OUTPUT_DIR}/${OUTPUT_VCF} \ | Type | Truth
    total | True
    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:---------:|:---------:|:--------:| -| INDEL | 11256 | 9442 | 1779 | 821 | 0.841951 | 0.921973 | 0.880147 | -| SNP | 71333 | 71288 | 56 | 56 | 0.999215 | 0.999215 | 0.999215 | +| INDEL | 11256 | 9442 | 1724 | 774 | 0.846837 | 0.926517 | 0.884887 | +| SNP | 71333 | 71288 | 60 | 51 | 0.999159 | 0.999285 | 0.999222 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/pipeline_singularity/ONT_variant_calling_singularity.md b/docs/pipeline_singularity/ONT_variant_calling_singularity.md index 195a5dcc..cb91728f 100644 --- a/docs/pipeline_singularity/ONT_variant_calling_singularity.md +++ b/docs/pipeline_singularity/ONT_variant_calling_singularity.md @@ -148,8 +148,8 @@ ${OUTPUT_DIR}/${OUTPUT_VCF} \ | Type | Truth
    total | True
    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:--------:|:---------:|:--------:| -| INDEL | 11256 | 8906 | 2350 | 904 | 0.791222 | 0.909942 | 0.846440 | -| SNP | 71333 | 71257 | 67 | 98 | 0.999061 | 0.998627 | 0.998844 | +| INDEL | 11256 | 8981 | 2275 | 837 | 0.797886 | 0.916692 | 0.853172 | +| SNP | 71333 | 71257 | 94 | 68 | 0.998682 | 0.999047 | 0.998864 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/pipeline_singularity/ONT_variant_calling_singularity_r10_q20.md b/docs/pipeline_singularity/ONT_variant_calling_singularity_r10_q20.md index 5b2f6ab9..6dee08dc 100644 --- a/docs/pipeline_singularity/ONT_variant_calling_singularity_r10_q20.md +++ b/docs/pipeline_singularity/ONT_variant_calling_singularity_r10_q20.md @@ -149,8 +149,8 @@ ${OUTPUT_DIR}/${OUTPUT_VCF} \ | Type | Truth
    total | True
    positives | False
    negatives | False
    positives | Recall | Precision | F1-Score | |:-----:|:--------------:|:-----------------:|:------------------:|:------------------:|:---------:|:---------:|:--------:| -| INDEL | 11256 | 9442 | 1779 | 821 | 0.841951 | 0.921973 | 0.880147 | -| SNP | 71333 | 71288 | 56 | 56 | 0.999215 | 0.999215 | 0.999215 | +| INDEL | 11256 | 9442 | 1724 | 774 | 0.846837 | 0.926517 | 0.884887 | +| SNP | 71333 | 71288 | 60 | 51 | 0.999159 | 0.999285 | 0.999222 | ### Authors: This pipeline is developed in a collaboration between UCSC genomics institute and the genomics team at Google health. diff --git a/docs/training_pepper_margin_deepvariant/how_to_train_deepvariant.md b/docs/training_pepper_margin_deepvariant/how_to_train_deepvariant.md index 7a228951..72bfdd98 100644 --- a/docs/training_pepper_margin_deepvariant/how_to_train_deepvariant.md +++ b/docs/training_pepper_margin_deepvariant/how_to_train_deepvariant.md @@ -6,6 +6,13 @@ In this walkthrough, we will see how to train `DeepVariant` and replace default **NOTE:** This tutorial goes through the basic steps required for training DeepVariant. However, there are many more options available to ease the training process of DeepVariant. [This documentation](https://github.com/google/deepvariant/blob/r1.3/docs/deepvariant-training-case-study.md) has a much more detailed explanation of the training process. If you have followed the official tutorial then you can use the `make_examples` stage from this tutorial and the rest of the steps should be exactly the same. +## Setup +In this setup we train one model with `--alt_aligned_pileup "diff_channels"` for calling SNPs and INDELs. However, you can train a `rows` model for INDEL calling and `none` model for SNP calling. + +To achieve two model setup, please repeat the DeepVariant training step with `--alt_aligned_pileup "none"` and `--alt_aligned_pileup "rows"` respectively. +Once you have the models, you can then use `--dv_model_snp ` and `--dv_model_indel ` to specify the model path for SNP and INDEL +independently. You can also select the alt alignment by `--dv_alt_aligned_pileup_snp` or `--dv_alt_aligned_pileup_indel` where `alt_aligned_pileup` can be `diff_channels`, `none` or `rows`. + ## Training DeepVariant We will now train `DeepVariant` that we use to genotype the candidates proposed by `PEPPER`.

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