add kylees suggestions and RWBs struggles

scripts/variantstore/beta_docs/gvs-troubleshooting.md

@@ -5,17 +5,18 @@ Generally, if you have started the GVS workflow and it failed after ingestion, o
 ## Access and auth errors
 1. `Failed to create dataset: Access Denied: User does not have bigquery.datasets.create permission.`
    1. See steps 5 and 6 in the [quickstart](./gvs-quickstart.md) to assign the right permissions on your google project for your Terra proxy user.
-2. `BadRequestException: 400 Bucket is a requester pays bucket but no user project provided.`
+1. `BadRequestException: 400 Bucket is a requester pays bucket but no user project provided.`
    1. GVS can ingest data from a requester pays bucket by setting the optional `billing_project_id` input variable. This variable takes a string of a Google project ID to charge for the egress of the GVCFs and index files.
-
-## Ingestion-Specific Issues"
-1. GVS is running very slowly!
-   1. If your GVS workflow is running very slowly compared to the example runtimes in the workspace, you may have run GVS on GVCFs that have not been reblocked. Confirm your GVCFs are reblocked.
-1. My workflow failed during ingestion, can I restart it?
-   1. If it fails during ingestion, yes, the GvsBeta workflow is restartable and will pick up where it left off.
-
+1. `BigQuery error in mk operation: Not found: Dataset <project>:<dataset>`
+   Make sure there is a dataset in BQ and set up the proper permissions
 
 ## Runtime errors
+### Ingestion-Specific Issues
+These are issues that occur almost immediately --- often because of mis-named headers or incorrect paths
+Note that if your workflow failed during ingestion, generally, the GvsBeta workflow is restartable and will pick up where it left off.
+
+1. GVS is running very slowly!
+   1. Confirm your GVCFs are reblocked. If your GVS workflow is running very slowly compared to the example runtimes in the workspace, you may have run GVS on GVCFs that have not been reblocked. 
 1.  `Duplicate sample names error: ERROR: The input file ~{sample_names_file} contains the following duplicate entries:`
    1. The GVS requires that sample names are unique because the sample names are used to name the samples in the VCF, and VCF format requires unique sample names.
    1. After deleting or renaming the duplicate sample, you can restart the workflow without any clean up.
@@ -26,18 +27,33 @@ Generally, if you have started the GVS workflow and it failed after ingestion, o
 1. Ingest failure with error message: `raise ValueError("vcf column not in table")`
    1. if you have given an incorrect name for the vcf column or the vcf index column
    1. You can simply restart the workflow with the correct names
-1. Ingest failure with error message: `Invalid resource name projects/gvs_internal; Project id: gvs_internal.`
+1. Ingest failure with error message: `Invalid resource name projects/{your_project_id}; Project id: {your_project_id}.`
    1. This occurs if you have given the incorrect name of the project.
    1. Restart the workflow with the correct name
 1. Ingest failure with `Max id is 0. Exiting.`
-   1. You will want to completely start over and delete your BQ dataset--and then re-create it. It can have the exact same name.
+   1. Please fully delete the GVS BigQuery dataset and recreate it as you did originally. Then kick off the ingest just as you did before--Make sure call caching is turned off.
 1. Ingest failure: There is already a list of sample names. This may need manual cleanup. Exiting.
    1. Clean up the BQ dataset manually by deleting it and recreating it fresh
    1. Make sure to keep the call caching on and run it again
-1. Ingest failure with error message: `A USER ERROR has occurred: Cannot be missing required value for `___
+1. Ingest failure with error message: `A USER ERROR has occurred: Cannot be missing required value for <a required annotation>`
    1. (e.g. alternate_bases.AS_RAW_MQ, RAW_MQandDP or RAW_MQ)
    1. This means that there is at least one incorrectly formatted sample in your data model. Confirm your GVCFs are reblocked. If the incorrectly formatted samples are a small portion of your callset and you wish to just ignore them, simply delete the from the data model and restart the workflow without them. There should be no issue with starting from here as none of these samples were loaded.
-1. Extract failure with OSError: Is a directory. If you point your extract to a directory that doesn’t already exist, it will not be happy about this. Simply make the directory and run the workflow again.
+   1. Please see the full list of [required information in a GVCF to work in GVS](https://github.com/broadinstitute/gatk/blob/ah_var_store/scripts/variantstore/beta_docs/run-your-own-samples.md#gvcf-annotations)
 1. Ingest failure with: `Lock table error`
-   1. This means that the lock table has been created, but that the ingest has failed soon after or that perhaps during manual cleanup from another failure, some underlying data was deleted
-   1. The lock table can simply be deleted -- `sample_id_assignment_lock` -- and the ingest can be kicked off again
+   1. This error usually occurs when a previous error has been fixed but perhaps not completely and it is best to start with a clean slate.
+   1. The lock table can be found inside your GVS BigQuery dataset  -- `{your_dataset_id}.sample_id_assignment_lock`. 
+   1. It is created to prevent duplicate sample errors and if you see this, it's better to start fresh. 
+   1. Please fully delete the GVS BigQuery dataset and recreate it as you did originally. Then kick off the ingest just as you did before--Make sure call caching is turned off.
+
+
+### Troubleshooting other pipeline failures
+1. Extract failure with OSError: Is a directory.
+   1. If you point your extract to a directory that doesn’t already exist, the workflow fails. Make the directory and run the workflow again.
+
+
+
+
+
+
+
+