Utilities for working with NCBI taxonomic data, accession-to-taxon mappings, taxonomy branches, corrected ranks, and pathogen target taxa.
conda install bioconda::taxutilspip install taxutilstaxutils stores downloaded taxonomy files (names.dmp, nodes.dmp), pathogen target metadata, and accession-to-taxon mappings in a global save directory. Set TAXUTILS_GLOBALS before importing the package if you want to control where these files live:
export TAXUTILS_GLOBALS=/path/to/taxutils/savesIf TAXUTILS_GLOBALS is not set, taxutils defaults to ./taxutils/ in the current working directory.
The first run downloads NCBI taxonomy files. Accession lookups also use the NCBI accession-to-taxon mapping, which is large. By default, taxutils uses low-memory mode and scans the compressed mapping directly. For faster repeated lookups, use low_memory=False to build or reuse a local SQLite database:
from taxutils import taxutils
tu = taxutils(low_memory=False)Core functions are listed here. See the example notebook for a fuller walkthrough.
# Build object
tu = taxutils(accessions=None, low_memory=True, targets_json=None, rebuild=False, wgs=False) # Build the taxonomy utility object.
# Accession parsing and mapping
tu.parse_accession(header_strings, version=True) # Extract one accession per string.
tu.load_a2t(accessions, low_memory=None, extend=False, wgs=None) # Load accession-to-taxon mappings into tu.a2t.
tu.get_t2a(taxa, low_memory=None, wgs=None) # Return accessions assigned to taxa.
# Tree queries
tu.get_branch(taxon) # Return the root-to-taxon branch.
tu.get_subtree(taxon) # Return taxon plus all descendants.
tu.get_ancestor(taxon, anchor_rank) # Return nearest ancestor at a rank.
tu.is_leaf(taxon_or_taxa) # Test whether taxa have no child nodes.
tu.is_child(taxon_a, taxon_b) # Test whether taxon_a is a direct child of taxon_b.
tu.is_descendent(taxon_a, taxon_b) # Test whether taxon_a is below taxon_b.
tu.get_lca(taxon_a, taxon_b) # Return the lowest common ancestor.
tu.get_distance(taxon_a, taxon_b) # Return tree edge distance through the LCA.
tu.sort_taxa(taxa) # Sort taxa in taxonomic order.
tu.format_tree(taxa) # Return an indented tree Series.
tu.topology(taxon, anchor_rank=None) # Return subtree topology metrics.
tu.topology(taxon, anchor_rank=None, stat="topology_scale") # Return one topology statistic.
# Rank utilities
tu.get_rank_order() # Return canonical rank codes.
tu.higher_than_rank(taxa, rank) # Test whether taxa are higher than a rank.In taxutils, accessions=list/of/accessions can be passed to call load_a2t on construction of the taxutils object. A custom targets_json can similarly be passed in lieu of the default json explained below. rebuild=True redownloads the managed taxonomy, target, and accession files and rebuilds the SQLite accession database. By default, accession lookups use nucl_gb.accession2taxid.gz; pass wgs=True to also download/use nucl_wgs.accession2taxid.gz for WGS/TSA accessions. SQLite mode always uses nucl.accession2taxid.db; if it was built GB-only, a later wgs=True call upgrades the same DB with WGS mappings. load_a2t overwrites tu.a2t by default; pass extend=True to add missing mappings without discarding existing ones. Method-level low_memory=None and wgs=None use the modes set when tu was built.
parse_accession accepts strings, lists, arrays, and pandas Series. It returns the first accession found from each string using the same container type where possible; missing accessions are returned as "NA".
get_lca(a, b) returns the lowest common ancestor of two taxa. get_distance(a, b) returns the edge distance between two taxa through their lowest common ancestor. Depths are cached lazily as these methods are called.
get_ancestor(taxon, anchor_rank) returns the nearest ancestor at the requested corrected rank. If the input taxon already has that rank, it returns the input taxon; if no ancestor has that rank, it returns the input taxon as a fallback. It accepts a single taxon, list-like input, NumPy arrays, or pandas Series and returns the same container type where possible.
is_leaf(taxon) returns whether a taxon has no child nodes in the taxonomy tree. It accepts a single taxon, list-like input, NumPy arrays, or pandas Series. A single taxon returns a bool; a list-like input returns a list of booleans; a NumPy array returns a boolean array with the original shape; and a pandas Series returns a boolean Series with the original index.
is_child(taxon_a, taxon_b) returns whether taxon_a is a direct child of taxon_b. It uses the parent lookup directly, so each pairwise check is O(1).
is_descendent(taxon_a, taxon_b) returns whether taxon_a is a strict descendant of taxon_b; a taxon is not considered a descendant of itself. The first call builds a cached tree interval index in O(n), and subsequent pairwise checks are O(1).
is_child and is_descendent accept either two scalar taxa or two list-like inputs of the same length. Scalar inputs return a bool; list-like inputs return a list of booleans; NumPy arrays return boolean arrays with the original taxon_a shape; and pandas Series return boolean Series with the original taxon_a index.
topology(taxon, anchor_rank=None, stat=None) returns subtree topology metrics such as taxon count, leaf fraction, depth, branchiness, and topology_scale. Pass anchor_rank="F" to summarize the nearest family-level ancestor. With stat=None, a single taxon returns a Series and a list, array, or Series returns a DataFrame.
Pass stat to return one topology metric. A single taxon returns a scalar; a list, array, or Series returns a Series indexed by taxon.
tu.topology(...) returns these columns:
taxon: input taxon.name: input taxon name.rank_code: corrected rank code for the input taxon.anchor_taxon: subtree root used for the topology summary.anchor_name: anchor taxon name.anchor_rank_code: corrected rank code for the anchor.n_taxa: number of taxa in the anchor subtree.n_leaves: number of terminal taxa in the anchor subtree.max_depth: maximum number of edges below the anchor.mean_depth: average number of edges below the anchor.topology_scale: 95th percentile descendant depth, with minimum value 1.max_children: largest number of direct children from any node in the subtree.branching_taxa_fraction: fraction of subtree taxa with at least one child.top_child_fraction: fraction of the anchor subtree contained in its largest immediate child branch.
tu.target_taxa contains the default pathogen-derived target taxa. Use it directly for target filtering or movement checks.
taxutils keeps the raw NCBI rank in rank and adds corrected rank columns. Canonical ranks (R, D, K, P, C, O, F, G, S) are used as anchors only when they move deeper than the corrected parent rank. Noncanonical ranks such as no rank, clade, and other unusual labels inherit position from the tree. If a child would be ranked at the same or a higher level than its parent, it is assigned a subrank such as S2, S3, or F2. The canonical name for the corrected rank is stored in new_rank.
In ZarLab, we are working on metagenomics in the clinical setting, with the goal of creating an "agnostic diagnostic". We often want to look at broad array of taxa (tu.target_taxa) that could cause harm to people. In June 2024, CZI did the work of compiling a list of pathogenic taxa. I did the easy work of turning this into a json and uploading it to my website, so that it is available and easily accessed for all time (in case that link ever breaks). taxutils will extend the taxa list to include subtrees of each of those pathogenic taxa. It will additionally include SARS-CoV2, since it was excluded from CZI's list. If you find any other obvious, missing pathogens, please send me a note, so I can update my json. You can also update the target_taxa member variable yourself, or store an entirely different set of targets, if you wanted.
Author: Will O'Brien
Affiliation: Computer Science Department, UCLA
Email: wob@cs.ucla.edu