## AnVILWorkflow We introduce the AnVILWorkflow package for R users with limited computing resources. This package allows users to run workflows implemented in [Terra][] without writing any workflows, installing software, or managing cloud resources. Terra's computing resources rely on Google Cloud Platform (GCP), and to use AnVILWorkflow, you only need to setup the Terra account once at the beginning. Along with the [AnVIL][] package, the AnVILWorkflow package allows users to access Terra and GCP through R session from a conventional laptop, significantly lowering the learning curve for high-performance, cloud-based genomics resources. [Terra]: https://app.terra.bio/# [AnVIL]: https://github.com/Bioconductor/AnVIL <img src="https://github.com/shbrief/AnVILWorkflow/raw/devel/vignettes/runnable_workflow.png" width="90%" height="90%"/> #### Example 1. Microbiome analysis [bioBakery workflows][] is a collection of workflows and tasks for executing common microbial community analyses using standardized, validated tools and parameters. bioBakery is built on Python and maintained by [Huttenhower lab][]. This workflow uses call caching and preemptive instances by default for cost efficiency. Processing six paired-end demo samples (mean file size ~380MB) with the optimized default setting without using preemptive instances took about 5 hours and cost around $6.50. #### Example 2. Bulk RNAseq analysis [Salmon][] is a command-line tool for quantifying the expression of transcripts using RNA-seq data. Salmon workflow uses AnVIL’s data model and requires four essential inputs - fastq1, fastq2, fasta, and transcriptome index name. This workflow can be easily applied to the consortium data hosted in AnVIL, which follows AnVIL’s data model. With the default runtime environment configured for this workflow (1 CPU, 2GB memory, and 10GB SSD disk), processing 16 demo samples (32 fastq files, ~1GB per file) took about 30 minutes and cost $0.12. #### Example 3. Histopathology image analysis We implemented the hematoxylin-eosin (HE) stain normalization process of [PathML][] as an AnVIL workspace. This workflow accepts an SVS file as input and returns original and normalized images as PNG files. There are two required inputs - Google Cloud Storage URI, where the input SVS image file is stored, and the sample name. Processing one publicly available image (CMU-1_Small_Region.svs, 1.8MB) with the default runtime (4 CPU, 16GB memory) took about 8 minutes and cost $0.01. This simple but robust analysis setup can support clinical use cases, such as pathologists who process a large number of images in a short time, by offering guidance and cross-validation options. [bioBakery workflows]: https://github.com/biobakery/biobakery_workflows [Huttenhower lab]: http://huttenhower.sph.harvard.edu/ [Salmon]: https://combine-lab.github.io/salmon/ [PathML]: https://pubmed.ncbi.nlm.nih.gov/34880124/