Creating a pathogen 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.
Table of Contents
Change directory to the Zika workflow repository downloaded in the previous tutorial.
Create a folder for results.
mkdir -p results/
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
auspice. If you want to leave the Nextstrain shell, run the command
This varies depending on how your ambient runtime is set up. If you’ve installed tools into a custom Conda environment, activate it.
conda activate <your-environment-name>
Nextstrain builds typically require the following steps:
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.
>PAN/CDC_259359_V1_V3/2015 gaatttgaagcgaatgctaacaacagtatcaacaggttttattttggatttggaaacgag agtttctggtcatgaaaaacccaaaaaagaaatccggaggattccggattgtcaatatgc taaaacgcggagtagcccgtgtgagcccctttgggggcttgaagaggctgccagccggac ttctgctgggtcatgggcccatcaggatggtcttggcgattctagcctttttgagattca
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 https://www.ncbi.nlm.nih.gov/nuccore/KX447509 How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016) https://www.ncbi.nlm.nih.gov/pubmed/27729507 1_0181_PF zika KX447512 2013-12-XX oceania french_polynesia french_polynesia french_polynesia genbank genome Pettersson et al https://www.ncbi.nlm.nih.gov/nuccore/KX447512 How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016) https://www.ncbi.nlm.nih.gov/pubmed/27729507 1_0199_PF zika KX447519 2013-11-XX oceania french_polynesia french_polynesia french_polynesia genbank genome Pettersson et al https://www.ncbi.nlm.nih.gov/nuccore/KX447519 How Did Zika Virus Emerge in the Pacific Islands and Latin America? MBio 7 (5), e01239-16 (2016) https://www.ncbi.nlm.nih.gov/pubmed/27729507 Aedes_aegypti/USA/2016/FL05 zika KY075937 2016-09-09 north_america usa usa usa genbank genome Grubaugh et al https://www.ncbi.nlm.nih.gov/nuccore/KY075937 Genomic epidemiology reveals multiple introductions of Zika virus into the United States Nature (2017) In press https://www.ncbi.nlm.nih.gov/pubmed/28538723
A metadata file must have the following columns:
Builds using published data should include the following additional columns, as shown in the example above:
Accession (e.g., NCBI GenBank, EMBL EBI, etc.)
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 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
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/zika_outgroup.gb \ --output results/aligned.fasta \ --fill-gaps
Now the pathogen sequences are ready for analysis.
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.
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.
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 \ --confidence
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 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
augur translate \ --tree results/tree.nwk \ --ancestral-sequences results/nt_muts.json \ --reference-sequence config/zika_outgroup.gb \ --output-node-data results/aa_muts.json
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
# Leave the Docker runtime you entered earlier. exit
nextstrain view to visualize the Zika dataset using Auspice.
nextstrain view auspice/
While Auspice is running, navigate to http://127.0.0.1:4000/zika 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.
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.
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.