update diagrams to display recommended invocation order.

website/docs/modules/evidence_qc.md

@@ -19,8 +19,10 @@ for further guidance on creating batches.
 We also recommend using sex assignments generated from the ploidy 
 estimates and incorporating them into the PED file, with sex = 0 for sex aneuploidies.
 
-The upstream and downstream dependencies of the EvidenceQC workflow 
-are illustrated in the following diagram.
+The following diagram illustrates the upstream and downstream workflows of the `EvidenceQC` workflow 
+in the recommended invocation order. You may refer to 
+[this diagram](https://github.com/broadinstitute/gatk-sv/blob/main/terra_pipeline_diagram.jpg) 
+for the overall recommended invocation order.
 
 <br/>
 
@@ -34,16 +36,15 @@ stateDiagram
   classDef outModules stroke-width:0px,fill:#caf0f8,color:#00509d
 
   gse: GatherSampleEvidence
-  gbe: GatherBatchEvidence
   eqc: EvidenceQC
-  t: TrainGCNV
+  batching: Batching, sample QC, and sex assignment
+  
   gse --> eqc
-  eqc --> t
-  eqc --> gbe
+  eqc --> batching
   
   class eqc thisModule
   class gse inModules
-  class t, gbe outModules
+  class batching outModules
 ```
 
 <br/>

website/docs/modules/gather_batch_evidence.md

@@ -8,6 +8,10 @@ slug: gbe
 Runs CNV callers ([cn.MOPS](https://academic.oup.com/nar/article/40/9/e69/1136601), GATK gCNV) 
 and combines single-sample raw evidence into a batch.
 
+The following diagram illustrates the downstream workflows of the `GatherBatchEvidence` workflow 
+in the recommended invocation order. You may refer to 
+[this diagram](https://github.com/broadinstitute/gatk-sv/blob/main/terra_pipeline_diagram.jpg) 
+for the overall recommended invocation order.
 
 ```mermaid
 
@@ -18,19 +22,15 @@ stateDiagram
   classDef thisModule font-weight:bold,stroke-width:0px,fill:#ff9900,color:white
   classDef outModules stroke-width:0px,fill:#caf0f8,color:#00509d
 
-  gse: GatherSampleEvidence
-  eqc: EvidenceQC
-  gcnv: TrainGCNV
   gbe: GatherBatchEvidence
-  cbe: ClusterBatch
-  gse --> gbe
-  eqc --> gbe
-  gcnv --> gbe
-  gbe --> cbe
+  t: TrainGCNV
+  cb: ClusterBatch
+  t --> gbe
+  gbe --> cb
   
   class gbe thisModule
-  class gse, eqc, gcnv inModules
-  class cbe outModules
+  class t inModules
+  class cb outModules
 ```
 
 ## Inputs

website/docs/modules/gather_sample_evidence.md

@@ -9,8 +9,11 @@ Runs raw evidence collection on each sample with the following SV callers:
 Manta, Wham, Scramble, and/or MELT. For guidance on pre-filtering prior to GatherSampleEvidence, 
 refer to the Sample Exclusion section.
 
-The downstream dependencies of the GatherSampleEvidence workflow 
-are illustrated in the following diagram.
+The following diagram illustrates the downstream workflows of the `GatherSampleEvidence` workflow 
+in the recommended invocation order. You may refer to 
+[this diagram](https://github.com/broadinstitute/gatk-sv/blob/main/terra_pipeline_diagram.jpg) 
+for the overall recommended invocation order.
+
 
 ```mermaid
 
@@ -23,14 +26,10 @@ stateDiagram
 
   gse: GatherSampleEvidence
   eqc: EvidenceQC
-  gcnv: TrainGCNV
-  gbe: GatherBatchEvidence
   gse --> eqc
-  gse --> gcnv
-  gse --> gbe
   
   class gse thisModule
-  class eqc, gcnv, gbe outModules
+  class eqc outModules
 ```
 
 

website/docs/modules/train_gcnv.md

@@ -12,9 +12,27 @@ is a method for detecting rare germline copy number variants (CNVs)
 from short-read sequencing read-depth information.
 The [TrainGCNV](https://github.com/broadinstitute/gatk-sv/blob/main/wdl/TrainGCNV.wdl)
 module trains a gCNV model for use in the [GatherBatchEvidence](./gbe) workflow. 
-The upstream and downstream dependencies of the TrainGCNV module are illustrated in the following diagram. 
+The upstream and downstream dependencies of the TrainGCNV module are illustrated in the following diagram.
 
-<br/>
+
+The samples used for training should be homogeneous and similar 
+to the samples on which the model will be applied in terms of sample type, 
+library preparation protocol, sequencer, sequencing center, and etc.
+
+
+For small, relatively homogeneous cohorts, a single gCNV model is usually sufficient. 
+However, for larger cohorts, especially those with multiple data sources, 
+it is necessary to train a separate model for each batch or group of batches 
+with a similar dosage score (WGD). 
+The model can be trained on all or a subset of the samples to which it will be applied. 
+A subset of 100 randomly selected samples from the batch is a reasonable 
+input size for training the model; also, the `TrainGCNV` workflow can automatically select 
+a given number of random samples through the `n_samples_subsample` parameter.
+
+The following diagram illustrates the upstream and downstream workflows of the `TrainGCNV` workflow 
+in the recommended invocation order. You may refer to 
+[this diagram](https://github.com/broadinstitute/gatk-sv/blob/main/terra_pipeline_diagram.jpg) 
+for the overall recommended invocation order.
 
 ```mermaid
 
@@ -25,37 +43,18 @@ stateDiagram
   classDef thisModule font-weight:bold,stroke-width:0px,fill:#ff9900,color:white
   classDef outModules stroke-width:0px,fill:#caf0f8,color:#00509d
 
-  gse: GatherSampleEvidence
-  eqc: EvidenceQC
+  batching: Batching, sample QC, and sex assignment
   t: TrainGCNV
-  gse --> t
-  eqc --> t
   gbe: GatherBatchEvidence
+  
+  batching --> t
   t --> gbe 
   
   class t thisModule
-  class gse, eqc inModules
+  class batching inModules
   class gbe outModules
 ```
 
-<br/>
-
-
-The samples used for training should be homogeneous and similar 
-to the samples on which the model will be applied in terms of sample type, 
-library preparation protocol, sequencer, sequencing center, and etc.
-
-
-For small, relatively homogeneous cohorts, a single gCNV model is usually sufficient. 
-However, for larger cohorts, especially those with multiple data sources, 
-it is necessary to train a separate model for each batch or group of batches 
-with a similar dosage score (WGD). 
-The model can be trained on all or a subset of the samples to which it will be applied. 
-A subset of 100 randomly selected samples from the batch is a reasonable 
-input size for training the model; also, the `TrainGCNV` workflow can automatically select 
-a given number of random samples through the `n_samples_subsample` parameter.
-
-
 ## Inputs
 
 This section provides a brief description on the _required_ inputs of the TrainGCNV workflow.