multitax 1.4.0

Python package to obtain, parse and explore biological and custom taxonomies.

MultiTax

A Python package for obtaining, parsing and exploring biological taxonomies.

Description

MultiTax is a Python package that provides a standardised set of functions for downloading, parsing, filtering, exploring, translating, converting and writing multiple taxonomies, including GTDB, NCBI, Silva, Greengenes and Open Tree Taxonomy, as well as custom-formatted taxonomies. The main goals are:

• to be fast, intuitive, generalised and easy to use
• explore different taxonomies using the same set of commands.
• enable integration and compatibility with multiple taxonomies
• translate taxonomies (only partially implemented between NCBI and GTDB).

MultiTax does not link sequence identifiers to taxonomic nodes; it only handles the taxonomy.

Installation

pip

pip install multitax

conda

conda install -c bioconda multitax

local

git clone https://github.com/pirovc/multitax.git
cd multitax
pip install .

API Documentation

https://pirovc.github.io/multitax/

Basic usage examples with GTDB

from multitax import GtdbTx

# Download and parse taxonomy
tax = GtdbTx()

# Get lineage for the Escherichia genus  
tax.lineage("g__Escherichia")
# ['1', 'd__Bacteria', 'p__Proteobacteria', 'c__Gammaproteobacteria', 'o__Enterobacterales', 'f__Enterobacteriaceae', 'g__Escherichia']

Load

from multitax import GtdbTx  # or NcbiTx, SilvaTx, ...

# Download and parse in memory
tax = GtdbTx()

# Parse local files
tax = GtdbTx(files=["bac120_taxonomy.tsv.gz", "ar122_taxonomy.tsv.gz"])

# Download, write and parse files
tax = GtdbTx(output_prefix="my/path/") 

# Download and filter only specific branch
tax = GtdbTx(root_node="p__Proteobacteria") 

Explore

# List parent node
tax.parent("g__Escherichia")
# f__Enterobacteriaceae

# List children nodes
tax.children("g__Escherichia")
# ['s__Escherichia coli',
# 's__Escherichia albertii',
# 's__Escherichia marmotae',
# 's__Escherichia fergusonii',
# 's__Escherichia sp005843885',
# 's__Escherichia ruysiae',
# 's__Escherichia sp001660175',
# 's__Escherichia sp004211955',
# 's__Escherichia sp002965065',
# 's__Escherichia coli_E']

# Get parent node from a defined rank
tax.parent_rank("s__Lentisphaera araneosa", "phylum")
# 'p__Verrucomicrobiota'

# Get the closest parent from a list of ranks
tax.closest_parent("s__Lentisphaera araneosa", ranks=["phylum", "class", "family"])
# 'f__Lentisphaeraceae'

# Get lineage
tax.lineage("g__Escherichia")
# ['1', 'd__Bacteria', 'p__Proteobacteria', 'c__Gammaproteobacteria', 'o__Enterobacterales', 'f__Enterobacteriaceae', 'g__Escherichia']

# Get lineage of names
tax.name_lineage("g__Escherichia")
# ['root', 'Bacteria', 'Proteobacteria', 'Gammaproteobacteria', 'Enterobacterales', 'Enterobacteriaceae', 'Escherichia']

# Get lineage of ranks
tax.rank_lineage("g__Escherichia")
# ['root', 'domain', 'phylum', 'class', 'order', 'family', 'genus']

# Get lineage with defined ranks and root node
tax.lineage("g__Escherichia", root_node="p__Proteobacteria", ranks=["phylum", "class", "family", "genus"])
# ['p__Proteobacteria', 'c__Gammaproteobacteria', 'f__Enterobacteriaceae', 'g__Escherichia']

# Build lineages in memory for faster access
tax.build_lineages()

# Get leaf nodes
tax.leaves("p__Hadarchaeota")
# ['s__DG-33 sp004375695', 's__DG-33 sp001515185', 's__Hadarchaeum yellowstonense', 's__B75-G9 sp003661465', 's__WYZ-LMO6 sp004347925', 's__B88-G9 sp003660555']

# Search names and filter by rank
tax.search_name("Escherichia", exact=False, rank="genus")
# ['g__Escherichia', 'g__Escherichia_C']

# Show stats of loaded tax
tax.stats()
#{'leaves': 31910,
# 'names': 45503,
# 'nodes': 45503,
# 'ranked_leaves': Counter({'species': 31910}),
# 'ranked_nodes': Counter({'species': 31910,
#                          'genus': 9428,
#                          'family': 2600,
#                          'order': 1034,
#                          'class': 379,
#                          'phylum': 149,
#                          'domain': 2,
#                          'root': 1}),
# 'ranks': 45503}

Filter

# Filter ancestors (desc=True for descendants)
tax.filter(["g__Escherichia", "s__Pseudomonas aeruginosa"])
tax.stats()
#{'leaves': 2,
# 'names': 11,
# 'nodes': 11,
# 'ranked_leaves': Counter({'genus': 1, 'species': 1}),
# 'ranked_nodes': Counter({'genus': 2,
#                          'family': 2,
#                          'order': 2,
#                          'class': 1,
#                          'phylum': 1,
#                          'domain': 1,
#                          'species': 1,
#                          'root': 1}),
# 'ranks': 11}

Add, remove, prune

# Add node to the tree
tax.add("my_custom_node", "g__Escherichia", name="my custom name", rank="strain")
tax.lineage("my_custom_node")
# ['1', 'd__Bacteria', 'p__Proteobacteria', 'c__Gammaproteobacteria', 'o__Enterobacterales', 'f__Enterobacteriaceae', 'g__Escherichia', 'my_custom_node']

# Remove node from tree (warning: removing parent nodes may break tree -> use check_consistency)
tax.remove("s__Pseudomonas aeruginosa", check_consistency=True)

# Prune (remove) full branches of the tree under a certain node
tax.prune("g__Escherichia")

Translate

# GTDB to NCBI
from multitax import GtdbTx, NcbiTx
ncbi_tax = NcbiTx()
gtdb_tax = GtdbTx()

# Build translation
gtdb_tax.build_translation(ncbi_tax)

# Check translated nodes
gtdb_tax.translate("g__Escherichia")
# {'1301', '547', '561', '570', '590', '620'}

Write

# Write tax to file
tax.write("custom_tax.tsv", cols=["node", "rank", "name_lineage"])

#g__Escherichia             genus    root|Bacteria|Proteobacteria|Gammaproteobacteria|Ent#erobacterales|Enterobacteriaceae|Escherichia
#f__Enterobacteriaceae      family   root|Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae
#o__Enterobacterales        order    root|Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales
#c__Gammaproteobacteria     class    root|Bacteria|Proteobacteria|Gammaproteobacteria
#...

The same applies to other taxonomies

# NCBI
from multitax import NcbiTx
tax = NcbiTx()
tax.lineage("561")    
# ['1', '131567', '2', '1224', '1236', '91347', '543', '561']

# Silva
from multitax import SilvaTx
tax = SilvaTx()
tax.lineage("46463")    
# ['1', '3', '2375', '3303', '46449', '46454', '46463']

# Open Tree taxonomy
from multitax import OttTx
tax = OttTx()
tax.lineage("474503")
# ['805080', '93302', '844192', '248067', '822744', '768012', '424023', '474503']

# GreenGenes
from multitax import GreengenesTx
tax = GreengenesTx()
tax.lineage("f__Enterobacteriaceae")
# ['1', 'd__Bacteria', 'p__Proteobacteria', 'c__Gammaproteobacteria', 'o__Enterobacteriales', 'f__Enterobacteriaceae']

LCA integration

Using pylca: https://github.com/pirovc/pylca

conda install -c bioconda pylca

from pylca.pylca import LCA
from multitax import GtdbTx

# Download and parse GTDB Taxonomy
tax = GtdbTx()

# Build LCA structure
lca = LCA(tax._nodes)

# Get LCA
lca("s__Escherichia dysenteriae", "s__Pseudomonas aeruginosa")
# 'c__Gammaproteobacteria'

Details

• After downloading and parsing the desired taxonomies, MultiTax works fully offline.
• Taxonomies are parsed into nodes. Each node is annotated with a name and a rank.
• Some taxonomies have a numeric taxonomic identifier (e.g. NCBI), while others use the rank and name as an identifier (e.g. GTDB). In MultiTax, all identifiers are treated as strings.
• A single root node is defined by default for each taxonomy (or 1 when not defined). This can be changed using the root_node parameter when loading the taxonomy, as well as the root_parent, root_name and root_rank parameters. If the root_node already exists, the tree will be filtered.
• Standard values for unknown or undefined nodes can be configured using the undefined_node, undefined_name and undefined_rank parameters. These are the default values returned when nodes, names or ranks are not found.
• Taxonomy files are automatically downloaded or can be loaded from disk using the files parameter. Alternative urls can be provided. When downloaded, files are handled in memory. It is possible to save the downloaded file to disk using the output_prefix.

Translation between taxonomies

This functionality is only partially implemented. The goal is to map different taxonomies if the linkage data is available. That's what is currently availble.

from/to	NCBI	GTDB	SILVA	OTT	GG
NCBI	-	PART	[part]	[part]	no
GTDB	FULL	-	[part]	no	[part]
SILVA	[full]	[part]	-	[part]	no
OTT	[part]	no	[part]	-	no
GG	no	[part]	no	no	-

Legend:

• full: complete translation available
• part: partial translation available
• no: no translation possible
• []: not yet implemented

Files and information about specific translations

• NCBI <-> GTDB
  • GTDB is a subset of the NCBI repository, so the translation from NCBI to GTDB can be only partial
  • Translation in both ways is based on: https://data.gtdb.ecogenomic.org/releases/latest/ar53_metadata.tsv.gz and https://data.gtdb.ecogenomic.org/releases/latest/bac120_metadata.tsv.gz

---

Further ideas to be implemented

• More translations
• Conversion between taxonomies (write on specific format)
• Sequence indetifier integration

Similar projects

• https://github.com/FOI-Bioinformatics/flextaxd
• https://github.com/shenwei356/taxonkit
• https://github.com/bioforensics/pytaxonkit
• https://github.com/chanzuckerberg/taxoniq
• https://github.com/sherrillmix/taxonomizr
• https://github.com/etetoolkit/ete
• https://github.com/apcamargo/taxopy