Our list of external solutions is out of date and not as easy to maintain. Remove for now.

PiperOrigin-RevId: 415383144

Author: pichuan

README.md

@@ -82,8 +82,7 @@ google/deepvariant:"${BIN_VERSION}"`
 
 If you're using GPUs, or want to use Singularity instead, see
 [Quick Start](docs/deepvariant-quick-start.md) for more details or see all the
-[setup options](#deepvariant_setup) available including solutions on external
-platforms.
+[setup options](#deepvariant_setup) available.
 
 For more information, also see:
 
@@ -108,8 +107,8 @@ cite:
 
 [Accurate, scalable cohort variant calls using DeepVariant and GLnexus.
 _Bioinformatics_ (2021).](https://doi.org/10.1093/bioinformatics/btaa1081)<br/>
-Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory Y.
-McLean.<br/>
+Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory
+Y. McLean.<br/>
 doi: https://doi.org/10.1093/bioinformatics/btaa1081
 
 ## Why Use DeepVariant?
@@ -141,8 +140,7 @@ doi: https://doi.org/10.1093/bioinformatics/btaa1081
     30x whole genome and $0.21 for whole exome (not considering preemption).
 *   **Speed** - See [metrics](docs/metrics.md) for the runtime of all supported
     datatypes on a 64-core CPU-only machine</sup>. Multiple options for
-    acceleration exist, taking the WGS pipeline to as fast as 40 minutes
-    (see [external solutions](#external-solutions)).
+    acceleration exist.
 *   **Usage options** - DeepVariant can be run via Docker or binaries, using
     both on-premise hardware or in the cloud, with support for hardware
     accelerators like GPUs and TPUs.
@@ -183,21 +181,6 @@ Name
 [Build from source](docs/deepvariant-build-test.md) | DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts.
 Prebuilt Binaries                                                                                   | Available at [`gs://deepvariant/`](https://console.cloud.google.com/storage/browser/deepvariant). These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the `/proc/cpuinfo` file on your computer, which lists these features under "flags".
 
-### External Solutions
-
-The following pipelines are not created or maintained by the
-[Genomics team in Google Health](https://health.google/health-research/).
-Please contact the relevant teams if you have any questions or concerns.
-
-Name                                                                                                                                                                                                                                                                                                                                                                                                                     | Description
-:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -----------
-[Running DeepVariant on Google Cloud Platform](https://cloud.google.com/genomics/docs/tutorials/deepvariant)                                                                                                                                                                                                                                                                                                             | Docker-based pipelines optimized for cost and speed. Code can be found [here](https://github.com/googlegenomics/gcp-deepvariant-runner).
-[DeepVariant-on-spark from ATGENOMIX](https://github.com/atgenomix/deepvariant-on-spark)                                                                                                                                                                                                                                                                                                                                 | A germline short variant calling pipeline that runs DeepVariant on Apache Spark at scale with support for multi-GPU clusters (e.g. NVIDIA DGX-1).
-[NVIDIA Clara Parabricks](https://www.nvidia.com/en-us/docs/parabricks/variant-callers/deepvariant/)                                                                                                                                                                                                                                                                                                                     | An accelerated DeepVariant pipeline with multi-GPU support that runs our WGS pipeline in just 40 minutes, at a cost of $2-$3 per sample. This provides a 7.5x speedup over a 64-core CPU-only machine at lower cost.
-[DNAnexus DeepVariant App](https://platform.dnanexus.com/app/deepvariant_germline)                                                                                                                                                                                                                                                                                                                                       | Offers parallelized execution with a GUI interface (requires platform account).
-[Nextflow Pipeline](https://github.com/nf-core/deepvariant)                                                                                                                                                                                                                                                                                                                                                              | Offers parallel processing of multiple BAMs and Docker support.
-[DNAstack Pipeline](https://app.dnastack.com/auth/realms/DNAstack/protocol/openid-connect/auth?client_id=dnastack-client&redirect_uri=https%3A%2F%2Fapp.dnastack.com%2F%3Fredirect_fragment%3D%252Forg%252F473079%252Fproj%252F473096%252Fapp%252Fworkflow%252F425685%252Frun&state=42231553-9fbc-4d71-a10e-d6ce42415c01&nonce=daf2568d-4fe7-48e2-ab60-858937244a87&response_mode=query&response_type=code&scope=openid) | Cost-optimized DeepVariant pipeline (requires platform account).
-
 ## Contribution Guidelines
 
 Please [open a pull request](https://github.com/google/deepvariant/compare) if

docs/deeptrio-quick-start.md

@@ -27,9 +27,6 @@ If you want to compile the binaries for yourself, we also have a [Dockerfile]
 that you can use to build your own Docker image. You can read the [docker build]
 documentation on how to build.
 
-For production use cases on larger datasets, we recommend looking into the
-[External Solutions] section.
-
 ## Get Docker image, models, and test data
 
 ### Get Docker image
@@ -345,7 +342,6 @@ INDEL   PASS            2         2         0            2         0          0
 [BWA]: https://academic.oup.com/bioinformatics/article/25/14/1754/225615/Fast-and-accurate-short-read-alignment-with
 [docker build]: https://docs.docker.com/engine/reference/commandline/build/
 [Dockerfile]: https://github.com/google/deepvariant/blob/r1.3/Dockerfile.deeptrio
-[External Solutions]: https://github.com/google/deepvariant#external-solutions
 [FASTA]: https://en.wikipedia.org/wiki/FASTA_format
 [VCF]: https://samtools.github.io/hts-specs/VCFv4.3.pdf
 [run_deeptrio.py]: ../scripts/run_deeptrio.py

docs/deepvariant-details.md

@@ -248,8 +248,7 @@ You can read more about samtools and bcftools here: http://www.htslib.org/doc/.
 We report runtime in our case studies documentation. In order to make sure the
 results we report are reproducible without too much variation, we provide the
 commands we used here to show you what kind of machines we ran the case studies
-on. This is NOT the fastest or cheapest configuration. For more scalable
-execution of DeepVariant see the [External Solutions] section.
+on. This is NOT the fastest or cheapest configuration.
 
 ### Command for a CPU-only machine on Google Cloud Platform.
 

docs/deepvariant-quick-start.md

@@ -28,9 +28,6 @@ If you want to compile the DeepVariant binaries for yourself, we also have a
 [Dockerfile] that you can use to build your own Docker image. You can read the
 [docker build] documentation on how to build.
 
-For production use cases on larger scale of dataset, we recommend looking into
-the [External Solutions] section.
-
 ## Get Docker image, models, and test data
 
 ### Get Docker image
@@ -258,7 +255,6 @@ INDEL   PASS            4         4         0           13         0          9
 [BWA]: https://academic.oup.com/bioinformatics/article/25/14/1754/225615/Fast-and-accurate-short-read-alignment-with
 [docker build]: https://docs.docker.com/engine/reference/commandline/build/
 [Dockerfile]: https://github.com/google/deepvariant/blob/r1.3/Dockerfile
-[External Solutions]: https://github.com/google/deepvariant#external-solutions
 [FASTA]: https://en.wikipedia.org/wiki/FASTA_format
 [Quick Start in r0.7]: https://github.com/google/deepvariant/blob/r0.7/docs/deepvariant-quick-start.md
 [VCF]: https://samtools.github.io/hts-specs/VCFv4.3.pdf

docs/metrics-deeptrio.md

@@ -130,8 +130,7 @@ truth), which was held out while training.
 For simplicity and consistency, we report runtime with a
 [CPU instance with 64 CPUs](deepvariant-details.md#command-for-a-cpu-only-machine-on-google-cloud-platform)
 For bigger datasets (WGS and PACBIO), we used bigger disk size (900G).
-This is NOT the fastest or cheapest configuration. For more scalable execution,
-see the [External Solutions] section.
+This is NOT the fastest or cheapest configuration.
 
 Use `gcloud compute ssh` to log in to the newly created instance.
 
@@ -156,5 +155,4 @@ DeepTrio. The runtime numbers reported above are the average of 5 runs each.
 The accuracy metrics come from the hap.py summary.csv output file.
 The runs are deterministic so all 5 runs produced the same output.
 
-[External Solutions]: https://github.com/google/deepvariant#external-solutions
 [CPU instance with 64 CPUs]: deepvariant-details.md#command-for-a-cpu-only-machine-on-google-cloud-platform

docs/metrics.md

@@ -99,8 +99,7 @@ out while training the hybrid model.
 
 For simplicity and consistency, we report runtime with a
 [CPU instance with 64 CPUs](deepvariant-details.md#command-for-a-cpu-only-machine-on-google-cloud-platform)
-This is NOT the fastest or cheapest configuration. For more scalable execution
-of DeepVariant see the [External Solutions] section.
+This is NOT the fastest or cheapest configuration.
 
 Use `gcloud compute ssh` to log in to the newly created instance.
 
@@ -128,5 +127,4 @@ DeepVariant. The runtime numbers reported above are the average of 5 runs each.
 The accuracy metrics come from the hap.py summary.csv output file.
 The runs are deterministic so all 5 runs produced the same output.
 
-[External Solutions]: https://github.com/google/deepvariant#external-solutions
 [CPU instance with 64 CPUs]: deepvariant-details.md#command-for-a-cpu-only-machine-on-google-cloud-platform

docs/trio-merge-case-study.md

@@ -25,10 +25,6 @@ See this workflow:
 
 A few things to note before we start:
 
-*   If you are looking for ways to run DeepVariant in larger batches, please
-    refer to the
-    [third party solutions](https://github.com/google/deepvariant#external-solutions)
-    section.
 *   It is recommended to use BAM files with original quality scores. In the case
     that BAM files went through recalibration, optional DV flags can be used in
     order to use original scores: `--parse_sam_aux_fields`,