Creating a phylogenetic workflow

This tutorial dissects the single-build workflow used in the previous tutorial. We will first make the build step-by-step. Then we will automate this stepwise process in a workflow.


The difference between a workflow and build isn’t obvious with single-build workflows such as this example Zika workflow, but will become more distinct in multi-build workflows such as the SARS-CoV-2 workflow.


  1. Install Nextstrain.

  2. Run through the previous tutorial. This will verify your installation.


  1. Change directory to the Zika pathogen repository downloaded in the previous tutorial.

    cd zika-tutorial
  2. Create a folder for results.

    mkdir -p results/
  3. Enter a prompt with Nextstrain tools available. This varies by runtime.

    nextstrain shell .


    The dot (.) as the last argument indicates that your current directory (zika-tutorial/) is the working directory. Your command prompt will change to indicate you are in a Nextstrain shell, which provides access to commands such as augur and auspice. If you want to leave the Nextstrain shell, run the command exit.

Run a Nextstrain Build

Nextstrain builds typically require the following steps:

Prepare the Sequences

A Nextstrain build typically starts with a collection of pathogen sequences in a single FASTA file and a corresponding table of metadata describing those sequences in a tab-delimited text file. For this tutorial, we will use example data containing 34 virus sequences.

Each virus sequence record looks like the following, with the virus’s strain ID as the sequence name in the header line followed by the virus sequence.


Each sequence record’s virus strain ID links to the tab-delimited metadata file by the latter’s strain field. The metadata file contains a header of column names followed by one row per virus strain ID in the sequences file. An example metadata file looks like the following.

strain  virus   accession   date    region  country division    city    db  segment authors url title   journal paper_url
1_0087_PF   zika    KX447509    2013-12-XX  oceania french_polynesia    french_polynesia    french_polynesia    genbank genome  Pettersson et al   How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016)
1_0181_PF   zika    KX447512    2013-12-XX  oceania french_polynesia    french_polynesia    french_polynesia    genbank genome  Pettersson et al   How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016)
1_0199_PF   zika    KX447519    2013-11-XX  oceania french_polynesia    french_polynesia    french_polynesia    genbank genome  Pettersson et al   How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016)
Aedes_aegypti/USA/2016/FL05 zika    KY075937    2016-09-09  north_america   usa usa usa genbank genome  Grubaugh et al   Genomic epidemiology reveals multiple introductions of Zika virus into the United States    Nature (2017) In press

A metadata file must have the following columns:

  • Strain

  • Virus

  • Date

Builds using published data should include the following additional columns, as shown in the example above:

  • Accession (e.g., NCBI GenBank, EMBL EBI, etc.)

  • Authors

  • URL

  • Title

  • Journal

  • Paper_URL

Index the Sequences

Precalculate the composition of the sequences (e.g., numbers of nucleotides, gaps, invalid characters, and total sequence length) prior to filtering. The resulting sequence index speeds up subsequent filter steps especially in more complex workflows.

augur index \
  --sequences data/sequences.fasta \
  --output results/sequence_index.tsv

The first lines in results/sequence_index.tsv should look like this.

strain  length  A   C   G   T   N   other_IUPAC -   ?   invalid_nucleotides
PAN/CDC_259359_V1_V3/2015   10771   2952    2379    3142    2298    0   0   0   0   0
COL/FLR_00024/2015  10659   2921    2344    3113    2281    0   0   0   0   0
PRVABC59    10675   2923    2351    3115    2286    0   0   0   0   0
COL/FLR_00008/2015  10659   2924    2344    3110    2281    0   0   0   0   0

Filter the Sequences

Filter the parsed sequences and metadata to exclude strains from subsequent analysis and subsample the remaining strains to a fixed number of samples per group.

augur filter \
  --sequences data/sequences.fasta \
  --sequence-index results/sequence_index.tsv \
  --metadata data/metadata.tsv \
  --exclude config/dropped_strains.txt \
  --output results/filtered.fasta \
  --group-by country year month \
  --sequences-per-group 20 \
  --min-date 2012

Align the Sequences

Create a multi-sequence alignment using a custom reference. After this alignment, columns with gaps in the reference are removed. Additionally, the --fill-gaps flag fills gaps in non-reference sequences with “N” characters. These modifications force all sequences into the same coordinate space as the reference sequence.

augur align \
  --sequences results/filtered.fasta \
  --reference-sequence config/ \
  --output results/aligned.fasta \

Now the pathogen sequences are ready for analysis.

Construct the Phylogeny

Infer a phylogenetic tree from the multi-sequence alignment.

augur tree \
  --alignment results/aligned.fasta \
  --output results/tree_raw.nwk

The resulting tree is stored in Newick format. Branch lengths in this tree measure nucleotide divergence.

Get a Time-Resolved Tree

Augur can also adjust branch lengths in this tree to position tips by their sample date and infer the most likely time of their ancestors, using TreeTime. Run the refine command to apply TreeTime to the original phylogenetic tree and produce a “time tree”.

augur refine \
  --tree results/tree_raw.nwk \
  --alignment results/aligned.fasta \
  --metadata data/metadata.tsv \
  --output-tree results/tree.nwk \
  --output-node-data results/branch_lengths.json \
  --timetree \
  --coalescent opt \
  --date-confidence \
  --date-inference marginal \
  --clock-filter-iqd 4

In addition to assigning times to internal nodes, the refine command filters tips that are likely outliers and assigns confidence intervals to inferred dates. Branch lengths in the resulting Newick tree measure adjusted nucleotide divergence. All other data inferred by TreeTime is stored by strain or internal node name in the corresponding JSON file.

Annotate the Phylogeny

Reconstruct Ancestral Traits

TreeTime can also infer ancestral traits from an existing phylogenetic tree and the metadata annotating each tip of the tree. The following command infers the region and country of all internal nodes from the time tree and original strain metadata. As with the refine command, the resulting JSON output is indexed by strain or internal node name.

augur traits \
  --tree results/tree.nwk \
  --metadata data/metadata.tsv \
  --output-node-data results/traits.json \
  --columns region country \

Infer Ancestral Sequences

Next, infer the ancestral sequence of each internal node and identify any nucleotide mutations on the branches leading to any node in the tree.

augur ancestral \
  --tree results/tree.nwk \
  --alignment results/aligned.fasta \
  --output-node-data results/nt_muts.json \
  --inference joint

Identify Amino-Acid Mutations

Identify amino acid mutations from the nucleotide mutations and a reference sequence with gene coordinate annotations. The resulting JSON file contains amino acid mutations indexed by strain or internal node name and by gene name. To export a FASTA file with the complete amino acid translations for each gene from each node’s sequence, specify the --alignment-output parameter in the form of results/aligned_aa_%GENE.fasta.

augur translate \
  --tree results/tree.nwk \
  --ancestral-sequences results/nt_muts.json \
  --reference-sequence config/ \
  --output-node-data results/aa_muts.json

Export the Results

Finally, collect all node annotations and metadata and export it in Auspice’s JSON format. This refers to three config files to define colors via config/colors.tsv, latitude and longitude coordinates via config/lat_longs.tsv, as well as page title, maintainer, filters present, etc., via config/auspice_config.json. The resulting tree and metadata JSON files are the inputs to the Auspice visualization tool.

augur export v2 \
  --tree results/tree.nwk \
  --metadata data/metadata.tsv \
  --node-data results/branch_lengths.json \
              results/traits.json \
              results/nt_muts.json \
              results/aa_muts.json \
  --colors config/colors.tsv \
  --lat-longs config/lat_longs.tsv \
  --auspice-config config/auspice_config.json \
  --output auspice/zika.json


If you entered the Nextstrain Docker runtime using nextstrain shell at the beginning of this tutorial, leave it now using the exit command.

# Leave the Docker runtime you entered earlier.

Visualize the Results

Use nextstrain view to visualize the Zika dataset using Auspice.

nextstrain view auspice/

While Auspice is running, navigate to in your browser to view the dataset.

To stop Auspice and return to the command line when you are done viewing your data, press CTRL+C.

Automate the Build with Snakemake

While it is instructive to run all of the above commands manually, it is more practical to automate their execution with a workflow manager. Nextstrain implements these automated builds with Snakemake by defining a Snakefile like this Snakefile used in the previous tutorial.

From the zika-tutorial/ directory, delete the previously generated results.

rm -rf results/ auspice/

Run the automated build.

nextstrain build --cpus 1 .

This runs all of the manual steps above, up through augur export. View the results the same way you did before to confirm it produced the same dataset.

Note that Snakemake will only re-run rules when the data changes. This means workflows will pick up where they left off if they are restarted after being interrupted. If you want to force a re-run of the whole workflow, first remove any previous output with nextstrain build --cpus 1 . clean.

Next steps