Update README.md

tutorials/README.md

@@ -31,9 +31,9 @@ There are a lot of ways to run workflows on GCP. Here we list a few possibilitie
 - The simplest method is probably to spin up a Compute Engine instance, and run your command interactively, or using `screen` or, as a [startup script](https://cloud.google.com/compute/docs/instances/startup-scripts/linux) attached as metadata.
 - You could also run your pipeline via a Vertex AI notebook, either by splitting out each command as a different block, or by running a workflow manager (Nextflow etc.). [Schedule notebooks](https://codelabs.developers.google.com/vertex_notebook_executor#0) to let them run longer.
 You can find a nice tutorial for using managed notebooks [here](https://codelabs.developers.google.com/vertex_notebook_executor#0). Note that there is now a difference between `managed notebooks` and `user managed notebooks`. The `managed notebooks` have more features and can be scheduled, but give you less control about conda environments/install. 
-- You can interact with [Google Batch](https://cloud.google.com/batch/docs/get-started), or the [Google Life Sciences API](https://cloud.google.com/life-sciences/docs/reference/rest) using a workflow manager like [Nextflow](https://cloud.google.com/life-sciences/docs/tutorials/nextflow), [Snakemake](https://snakemake.readthedocs.io/en/stable/executing/cloud.html), or [Cromwell](https://github.com/GoogleCloudPlatform/rad-lab/tree/main/modules/genomics_cromwell). We currently have example notebooks for both [Nextflow and Snakemake that use the Life Sciences API](/tutorials/notebooks/LifeSciencesAPI/), as well as [Google Batch with Nextflow](/tutorials/notebooks/GoogleBatch/nextflow) as well as a [local version of Snakemake run via Pangolin](/tutorials/notebooks/pangolin).
+- You can interact with [Google Batch](https://cloud.google.com/batch/docs/get-started), or the [Google Life Sciences API](https://cloud.google.com/life-sciences/docs/reference/rest) using a workflow manager like [Nextflow](https://cloud.google.com/life-sciences/docs/tutorials/nextflow), [Snakemake](https://snakemake.github.io/snakemake-plugin-catalog/plugins/executor/googlebatch.html), or [Cromwell](https://github.com/GoogleCloudPlatform/rad-lab/tree/main/modules/genomics_cromwell). We currently have example notebooks for both [Nextflow and Snakemake that use the Life Sciences API](/tutorials/notebooks/LifeSciencesAPI/), as well as [Google Batch with Nextflow](/tutorials/notebooks/GoogleBatch/nextflow) as well as a [local version of Snakemake run via Pangolin](/tutorials/notebooks/pangolin).
 - You may find other APIs better suite your needs such as the [Google Cloud Healthcare Data Engine](https://cloud.google.com/healthcare).
-- Most of the notebooks below require just a few CPUs. Start small (maybe 4 CPUs), then scale up as needed. Likewise, when you need a GPU, start with a smaller or older generation GPU (e.g. T4) for testing, then switch to a newer GPU (A100/V100) once you know things will work or you need more horsepower. 
+- Most of the notebooks below require just a few CPUs. Start small (maybe 4 CPUs), then scale up as needed. Likewise, when you need a GPU, start with a smaller or older generation GPU (e.g. T4) for testing, then switch to a newer GPU (A100/V100) once you know things will work or you need more compute power. 
 
 ## **Artificial Intelligence and Machine Learning** <a name='ml'></a>
 Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Machine learning on GCP generally occurs within VertexAI. You can learn more about machine learning on GCP at this [Google Crash Course](https://developers.google.com/machine-learning/crash-course). For hands-on examples, try out [this module](https://github.com/NIGMS/COVIDMachineLearningSFSU) developed by San Francisco State University or [this one from the University of Arkasas](https://github.com/NIGMS/MachineLearningUA) developed for the NIGMS Sandbox Project.