diff --git a/README.md b/README.md
index f27b28b..2284581 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,8 @@ auspice -help
 ```
 
 ## Run the build
-The build can be run at once or by workflows. Running the build by workflows can be helpful when troubleshooting or when testing modifications.
+This build can process and output global or Washington state focused WNV information. The build can also be run by workflows which is helpful when troubleshoting or all at once.
+
 To run the build by workflows first run the ingest workflow
 ```bash
 nextstrain build ingest
@@ -31,18 +32,46 @@ nextstrain build ingest
 Inside the ingest folder there should be two output files: metadata_all.tsv and sequences_all.tsv
 
 Run the phylogenetic workflow
+Execute the global build
 ```bash
 nextstrain build phylogenetic
 ```
+Or execute the Washington focused build 
+```bash
+nextstrain build phylogenetic --configfile build-configs/washington-state/config.yaml
+```
 Inside the phylogenetic folder there should be at least one output file: WNV-nextstrain_NA.json
 
-Run the build all at once
+Run the build all at once. This option defaults to the global build.
 ```bash
-nextstrain build
+nextstrain build phylogenetic
 ```
+
 ## File Structure
-This Nextstrain build follows the structure detailed in the [Pathogen Repo Guide] (https://github.com/nextstrain/pathogen-repo-guide)
+This Nextstrain build follows the structure detailed in the [Pathogen Repo Guide](https://github.com/nextstrain/pathogen-repo-guide)
+
+## Decision Points
+The following are critical decisions that were made during the development of the WNV build that should be kept in mind when analyzing the data.
+
+### Global and Washington Focused Outputs
+This build can process and output global or Washington state focused WNV information. To accomplish this, a washington-state.yaml file was added to the build-configs which specifies Washington subsampling preferences. This file can be adopted and mofidied to accomodate other sampling references appropiate to other regions or states.
+
+### Root Selection
+The Global and the Washington focused WNV builds use different roots.
+
+The Global WNV build uses the sequence [AF260968](https://www.ncbi.nlm.nih.gov/nuccore/AF260968.1) which is the first WNV L1 (cluster 1) strain recovered in Egypt from 1951.
+_Mencattelli, G., Ndione, M.H.D., Silverj, A. et al. Spatial and temporal dynamics of West Nile virus between Africa and Europe. Nat Commun 14, 6440 (2023). https://doi.org/10.1038/s41467-023-42185-7_
+
+The Washington focused WNV build uses the sequence [AF481864](https://www.ncbi.nlm.nih.gov/nuccore/AF481864) as this is the sequence that is most closely related to the sequences isolated from New York in 1999. 
+_Hadfield J, Brito AF, Swetnam DM, Vogels CBF, Tokarz RE, Andersen KG, Smith RC, Bedford T, Grubaugh ND. Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain. PLoS Pathog. 2019 Oct 31;15(10):e1008042. doi: 10.1371/journal.ppat.1008042. PMID: 31671157; PMCID: PMC6822705._
+
+### Subsampling
+The Washington focused WNV build pulls all the WNV sequences available in NCBI and filters the data in the phylogenetic workflow based on criteria defined in the config.yml file that is located inside the build-configs/washington-state folder. The subsampling criteria focuses on geographic location selecting all sequences from Washington, neighboring states, and region but up to a maximum of 5,000 sequences; and up to 300 sequences selected randomly from the rest of the states. All sequences have to meet a minimum genome length that is also specified as part of the subsampling criteria. There is more information about how to subsample data in Nextstrain here [Filter and Subsampling](https://docs.nextstrain.org/en/latest/guides/bioinformatics/filtering-and-subsampling.html)
 
+### Lineage Designation
+For global lineage designations, we query [pathoplexus](https://pathoplexus.org/)
 
+### Host mapping to Host Genus and Host Type
+We further refined the information in the NCBI Host column by categorizing it into **Host_Genus** and **Host_Type**, creating broader groupings for more effective data analysis. For example, the **Host** _Homo sapiens_ is classified under **Host_Genus** as _Homo_ and **Host_Type** as Human. This broader categorization is particularly useful for visualizing the phylogenetic tree. Instead of distinguishing between individual mosquito species, you can use the broader categories like **Host_Genus** _Culex_ or the higher-level category **Host_Type** Mosquito to color the tips of the tree.