Autocycler table

Basics

The autocycler table command generates a TSV line from the various metrics stored in YAML files during an Autocycler assembly.

When conducting many Autocycler assemblies, you can use Autocycler cluster to build a

Example command

For this example, I assume you have conducted many Autocycler assemblies, where each sample is in a directory that starts with SAM and there is an autocycler directory in each of those:

autocycler table > metrics.tsv  # create the TSV header
for sample in SAM*; do
    autocycler table -a "$sample"/autocycler -n "$sample" >> metrics.tsv  # append a TSV row
done

When you run autocycler table, you will likely need to change the SAM* glob to whatever will catch your samples.

Full usage

Usage: autocycler table [OPTIONS]

Options:
  -a, --autocycler_dir <AUTOCYCLER_DIR>  Autocycler directory (if absent, a header line will be output)
  -n, --name <NAME>                      Sample name [default: blank]
  -f, --fields <FIELDS>                  Comma-delimited list of YAML fields to include [default:
                                         "pass_cluster_count, fail_cluster_count,
                                         overall_clustering_score, untrimmed_cluster_size,
                                         untrimmed_cluster_distance, trimmed_cluster_size,
                                         trimmed_sequence_length_mad, consensus_assembly_total_length,
                                         consensus_assembly_total_unitigs,
                                         consensus_assembly_fully_resolved"]
  -s, --sigfigs <SIGFIGS>                Significant figures to use for floating point numbers
                                         [default: 3]
  -h, --help                             Print help
  -V, --version                          Print version

Notes

• The default value for --fields includes metrics that are useful for judging how well the assembly went, but you can choose other metrics, if you want.
• You can run all your assemblies using the fully automated method, build a TSV to assess how well they went, and then manually assess any samples which seem to have assembled poorly.