Filtering and Subsampling

Below are some examples of using augur filter to sample data.

Filtering

The filter command allows you to select various subsets of your input data for different types of analysis. A simple example use of this command would be

augur filter \
  --sequences data/sequences.fasta \
  --metadata data/metadata.tsv \
  --min-date 2012 \
  --output-sequences filtered_sequences.fasta \
  --output-metadata filtered_metadata.tsv

This command will select all sequences with collection date in 2012 or later. The filter command has a large number of options that allow flexible filtering for many common situations. One such use-case is the exclusion of sequences that are known to be outliers (e.g. because of sequencing errors, cell-culture adaptation, …). These can be specified in a separate text file (e.g. exclude.txt):

BRA/2016/FC_DQ75D1
COL/FLR_00034/2015
...

To drop such strains, you can pass the filename to --exclude:

augur filter \
  --sequences data/sequences.fasta \
  --metadata data/metadata.tsv \
  --min-date 2012 \
  --exclude exclude.txt \
  --output-sequences filtered_sequences.fasta \
  --output-metadata filtered_metadata.tsv

Subsampling within augur filter

Another common filtering operation is subsetting of data to a achieve a more even spatio-temporal distribution or to cut-down data set size to more manageable numbers. The filter command allows you to select a specific number of sequences from specific groups, for example one sequence per month from each country:

augur filter \
  --sequences data/sequences.fasta \
  --metadata data/metadata.tsv \
  --min-date 2012 \
  --exclude exclude.txt \
  --group-by country year month \
  --sequences-per-group 1 \
  --output-sequences subsampled_sequences.fasta \
  --output-metadata subsampled_metadata.tsv

Subsampling using multiple augur filter commands

There are some subsampling strategies in which a single call to augur filter does not suffice. One such strategy is “tiered subsampling”. In this strategy, mutually exclusive sets of filters, each representing a “tier”, are sampled with different subsampling rules. This is commonly used to create geographic tiers. Consider this subsampling scheme:

Sample 100 sequences from Washington state and 50 sequences from the rest of the United States.

This cannot be done in a single call to augur filter. Instead, it can be decomposed into multiple schemes, each handled by a single call to augur filter. Additionally, there is an extra step to combine the intermediate samples.

  1. Sample 100 sequences from Washington state.

  2. Sample 50 sequences from the rest of the United States.

  3. Combine the samples.

Calling augur filter multiple times

A basic approach is to run the augur filter commands directly. This works well for ad-hoc analyses.

# 1. Sample 100 sequences from Washington state
augur filter \
  --sequences sequences.fasta \
  --metadata metadata.tsv \
  --query "state == 'WA'" \
  --subsample-max-sequences 100 \
  --output-strains sample_strains_state.txt

# 2. Sample 50 sequences from the rest of the United States
augur filter \
  --sequences sequences.fasta \
  --metadata metadata.tsv \
  --query "state != 'WA' & country == 'USA'" \
  --subsample-max-sequences 50 \
  --output-strains sample_strains_country.txt

# 3. Combine using augur filter
augur filter \
  --sequences sequences.fasta \
  --metadata metadata.tsv \
  --exclude-all \
  --include sample_strains_state.txt \
            sample_strains_country.txt \
  --output-sequences subsampled_sequences.fasta \
  --output-metadata subsampled_metadata.tsv

Each intermediate sample is represented by a strain list file obtained from --output-strains. The final step uses augur filter with --exclude-all and --include to sample the data based on the intermediate strain list files. If the same strain appears in both files, augur filter will only write it once in each of the final outputs.

Generalizing subsampling in a workflow

The approach above can be cumbersome with more intermediate samples. To generalize this process and allow for more flexibility, a workflow management system can be used. The following examples use Snakemake.

  1. Add a section in the config file.

subsampling:
  state: --query "state == 'WA'" --subsample-max-sequences 100
  country: --query "state != 'WA' & country == 'USA'" --subsample-max-sequences 50

  1. Add two rules in a Snakefile. If you are building a standard Nextstrain workflow, the output files should be used as input to sequence alignment. See Parts of a whole to learn more about the placement of this step within a workflow.

# 1. Sample 100 sequences from Washington state
# 2. Sample 50 sequences from the rest of the United States
rule intermediate_sample:
    input:
        metadata = "data/metadata.tsv",
    output:
        strains = "results/sample_strains_{sample_name}.txt",
    params:
        augur_filter_args = lambda wildcards: config.get("subsampling", {}).get(wildcards.sample_name, "")
    shell:
        """
        augur filter \
            --metadata {input.metadata} \
            {params.augur_filter_args} \
            --output-strains {output.strains}
        """

# 3. Combine using augur filter
rule combine_intermediate_samples:
    input:
        sequences = "data/sequences.fasta",
        metadata = "data/metadata.tsv",
        intermediate_sample_strains = expand("results/sample_strains_{sample_name}.txt", sample_name=list(config.get("subsampling", {}).keys()))
    output:
        sequences = "results/subsampled_sequences.fasta",
        metadata = "results/subsampled_metadata.tsv",
    shell:
        """
        augur filter \
            --sequences {input.sequences} \
            --metadata {input.metadata} \
            --exclude-all \
            --include {input.intermediate_sample_strains} \
            --output-sequences {output.sequences} \
            --output-metadata {output.metadata}
        """

  1. Run Snakemake targeting the second rule.

snakemake combine_intermediate_samples

Explanation:

  • The configuration section consists of one entry per intermediate sample in the format sample_name: <augur filter arguments>.

  • The first rule is run once per intermediate sample using wildcards and an input function. The output of each run is the sampled strain list.

  • The second rule uses expand() to define input as all the intermediate sampled strain lists, which are passed directly to --include as done in the previous example.

It is easy to add or remove intermediate samples. The configuration above can be updated to add another tier in between state and country:

subsampling:
  state: --query "state == 'WA'" --subsample-max-sequences 100
  neighboring_states: --query "state in {'CA', 'ID', 'OR', 'NV'}" --subsample-max-sequences 75
  country: --query "country == 'USA' & state not in {'WA', 'CA', 'ID', 'OR', 'NV'}" --subsample-max-sequences 50