fixed link

README.md

@@ -31,7 +31,7 @@ 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 for conda environments/installs. 
-- 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](/notebooks/LifeSciencesAPI/), as well as [Google Batch with Nextflow](/notebooks/GoogleBatch/Nextflow) as well as a [local version of Snakemake run via Pangolin](/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](/notebooks/LifeSciencesAPI/), as well as [Google Batch with Nextflow](/notebooks/GoogleBatch/Nextflow) as well as a [local version of Snakemake run via Pangolin](/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 compute power.