Run using a genomic surveillance configuration
In the previous tutorial, you learned how to analyze a small set of GISAID (“custom”) data in the context of a small set of reference data. For genomic surveillance applications, you will often focus your analysis on a set of data specific to your question of interest. For example, an analysis of SARS-CoV-2 circulation in a specific geographic area requires a focal set of sequences and metadata from that area.
In this tutorial, you will learn to define and analyze a focal set of data from a geographic division in the United States using a global genetic context. You will also learn how to define a genetic context that prioritizes sequences that are genetically similar to your focal set.
Table of Contents
Run using custom data. This tutorial introduces concepts expanded by the following tutorial.
Register for a GISAID account, if you do not have one yet. However, registration may take a few days. Follow alternative data preparation methods in place of Curate data from GISAID, if you wish to continue the following tutorial in the meantime.
If you are not already there, change directory to the
and activate the
nextstrain conda environment:
conda activate nextstrain
We will download a focal set of Idaho sequences from GISAID’s EpiCoV database.
Navigate to GISAID, Login, and go to EpiCoV > Search.
Filter to sequences that pass the following criteria:
From Idaho, USA
Collected within the last month (e.g. 2022-06-03 to 2022-07-01)
Has a complete genome
Has an exact collection date
If your selection has more than 200 sequences, adjust the minimum date until it has 200 sequences or less. This ensures the tutorial does not take too long to run.
Select the topmost checkbox in the first column to select all sequences that match the filters.
Select Download > Input for the Augur pipeline > Download.
.tarfile into the
Extract by opening the downloaded
.tarfile in your file explorer. It contains a folder prefixed with
gisaid_auspice_input_hcov-19_containing two files: one ending with
.metadata.tsvand another with
Rename the files as
Move the files up to the
Delete the empty
gisaid_auspice_input_hcov-19_-prefixed folder and the
.tarfile if it is still there.
From within the
ncov/ directory, run the
ncov workflow using a pre-written
nextstrain build . --cores all --configfile ncov-tutorial/genomic-surveillance.yaml
The workflow can take several minutes to run. While it is running, you can investigate the contents of
genomic-surveillance.yaml (comments excluded):
inputs: - name: reference_data metadata: https://data.nextstrain.org/files/ncov/open/reference/metadata.tsv.xz aligned: https://data.nextstrain.org/files/ncov/open/reference/aligned.fasta.xz - name: custom_data metadata: data/idaho.metadata.tsv sequences: data/idaho.sequences.fasta - name: background_data metadata: https://data.nextstrain.org/files/ncov/open/north-america/metadata.tsv.xz aligned: https://data.nextstrain.org/files/ncov/open/north-america/aligned.fasta.xz refine: root: "Wuhan-Hu-1/2019" builds: idaho: title: "Idaho-specific genomic surveillance build" subsampling_scheme: idaho_scheme auspice_config: ncov-tutorial/auspice-config-custom-data.json subsampling: idaho_scheme: custom_sample: query: --query "(custom_data == 'yes')" max_sequences: 50 usa_context: query: --query "(custom_data != 'yes') & (country == 'USA')" max_sequences: 10 group_by: division year month priorities: type: proximity focus: custom_sample global_context: query: --query "(custom_data != 'yes')" max_sequences: 10 priorities: type: proximity focus: custom_sample
This configuration file is similar to the previous file. Differences are outlined below, broken down per configuration section.
The file paths in the second input are changed to
There is an additional input
background_datafor a regional North America dataset built by the Nextstrain team, for additional context.
The output dataset is renamed
idaho, representative of the new custom data in the second input.
The title is updated.
There is a new entry
subsampling_scheme: idaho_scheme. This is described in the following section.
This is a new section that provides a subsampling scheme
idaho_scheme consisting of three subsamples. Without this, the output dataset would use all the provided data, which in this case is thousands of sequences that are often disproportionally representative of the underlying population.
This selects at most 50 sequences from the
This selects at most 10 sequences from the USA from the
Sequences are subsampled evenly across all combinations of
month, with sequences genetically similar to
custom_sampleprioritized over other sequences.
This selects at most 10 sequences outside the USA from the
As with the
usa_contextabove, this rule prioritizes sequences for the global context that are genetically similar to sequences in the
Run this command to start the Auspice server, providing
auspice/ as the directory containing output dataset files:
nextstrain view auspice/