Merge pull request #1 from gatk-workflows/wdlupdate

Wdlupdate
Added workflow and Json

README.md

@@ -1 +1,68 @@
-# gatk4-CNNVariant
+# gatk4-cnn-variant-filter
+
+### Purpose :
+This repo provides workflows that takes advangate of GATKs CNN tool which a deep learning 
+approach to filter variants based on Convolutional Neural Networks. 
+
+Please read the following discussion to learn more about the CNN tool: [Deep Learning in GATK4](https://gatkforums.broadinstitute.org/gatk/discussion/10996/deep-learning-in-gatk4).
+
+### cram2filtered.wdl
+This workflow takes an input CRAM/BAM to call variants with HaplotypeCaller
+then filters the calls with the CNNVariant neural net tool using the filtering model specified.
+
+The site-level scores are added to the `INFO` field of the VCF. The architecture arguments,
+`info_key` and `tensor_type` arguments MUST be in agreement (e.g. 2D models must have
+`tensor_type` of `read_tensor` and `info_key` of `CNN_2D`, 1D models have `tensor_type` of
+`reference` and `info_key` of `CNN_1D`). The `INFO` field key will be `1D_CNN` or `2D_CNN`
+depending on the neural net architecture used for inference. The architecture arguments
+specify pre-trained networks. New networks can be trained by the GATK tools: CNNVariantWriteTensors 
+and CNNVariantTrain. The CRAM could be generated by the [single-sample pipeline](https://github.com/gatk-workflows/gatk4-data-processing/blob/master/processing-for-variant-discovery-gatk4.wdl).
+If you would like test the workflow on a more representative example file, use the following 
+CRAM file as input and change the scatter count from 4 to 200: gs://gatk-best-practices/cnn-h38/NA12878_NA12878_IntraRun_1_SM-G947Y_v1.cram.
+
+#### Requirements/expectations :
+ - CRAM/BAM
+ - BAM Index (if input is BAM) 
+
+#### Output :
+ - Filtered VCF and its index. 
+
+### cram2model.wdl
+This optional workflow is for advanced users who would like to train a CNN model for filtering variants. 
+
+#### Requirements/expectations :
+ - CRAM
+ - Truth VCF and its index
+ - Truth Confidence Interval Bed
+
+#### Output :
+ - Model HD5
+ - Model JSON
+ - Model Plots PNG
+
+### run_happy.wdl
+This optional evaluation and plotting workflow runs a filtering model against truth data (e.g. [NIST Genomes in a Bottle](https://github.com/genome-in-a-bottle/giab_latest_release), [Synthic Diploid Truth Set](https://github.com/lh3/CHM-eval/releases) ) and plots the accuracy.
+
+#### Requirements/expectations :
+ - File of VCF Files
+ - Truth VCF and its index
+ - Truth Confidence Interval Bed
+
+#### Output :
+ - Evaluation summary
+ - Plots
+
+### Important Note :
+- Runtime parameters are optimized for Broad's Google Cloud Platform implementation. 
+- For help running workflows on the Google Cloud Platform or locally please
+view the following tutorial [(How to) Execute Workflows from the gatk-workflows Git Organization](https://software.broadinstitute.org/gatk/documentation/article?id=12521).
+- Please post questions to the [GATK forum](https://gatkforums.broadinstitute.org/gatk/categories/ask-the-team).
+- Please visit our [User Guide](https://software.broadinstitute.org/gatk/documentation/) site for further documentation on workflows and tools. 
+
+### LICENSING :
+ This script is released under the WDL source code license (BSD-3) (see LICENSE in
+ https://github.com/broadinstitute/wdl). Note however that the programs it calls may
+ be subject to different licenses. Users are responsible for checking that they are
+ authorized to run all programs before running this script. Please see the docker
+ page at https://hub.docker.com/r/broadinstitute/genomes-in-the-cloud/ for detailed
+ licensing information pertaining to the included programs.