21 releases

0.9.1 Mar 30, 2024
0.8.3 Mar 24, 2024
0.8.1 Jun 25, 2023
0.6.1 Mar 30, 2023
0.2.0 Dec 27, 2022

#14 in Science

35 downloads per month
Used in viguno

MIT license



This library is a Rust implementation of PyHPO.

What is this?

HPO, the Human Phenotype Ontology is a standard vocabulary of phenotypic abnormalities in human diseases. It is an Ontology, so all terms are connected to each other, similar to a directed graph.

This library provides convenient APIs to work with the ontology. The main goals are to compare terms - or sets of terms - to each other and run statistics for enrichment analysis.


  • Calculate the similarity of HPO terms
  • Calculate the similarity of multiple sets of HPO terms (e.g. a patient's clinical information)
  • Enrichment analysis of genes and diseases in sets of HPO terms
  • Compare different HPO versions
  • Graph based analysis of the ontology
  • Completely written in Rust, so it's 🚀blazingly fast🚀TM (Benchmarks)

What is the current state?

The library is pretty much feature-complete, at least for my use-cases. If you have any feature-requests, please open an Issue or get in touch. I'm very much interested in getting feedback and new ideas what to improve.

The API is mostly stable, but I might refactor some parts a bit for easier use and performance gain.

If you find this project interesting and want to contribute, please get in touch, I could definitely need some help.


The public API is fully documented on docs.rs

The main structs used in hpo are:

  • The Ontology is the main struct and entrypoint in hpo.
  • HpoTerm represents a single HPO term and contains plenty of functionality around them.
  • HpoSet is a collection of HpoTerms, like a patient's clinical information.
  • Gene represents a single gene, including information about associated HpoTerms.
  • OmimDisease represents a single OMIM-diseases, including information about associated HpoTerms.

The most relevant modules are:

  • annotations contains the Gene and OmimDisease structs, and some related important types.
  • similarity contains structs and helper functions for similarity comparisons for HpoTerm and HpoSet.
  • stats contains functions to calculate the hypergeometric enrichment score of genes or diseases.


Some (more or less random) examples are included in the examples folder.

HPO data must be downloaded first from Jax HPO itself. You need the following files:

  • phenotype.hpoa available as "Download HPO annotations" (Required to connect OmimDisease to HpoTerms
  • genes_to_phenotype.txt available as "Genes to Phenotype" (Required to connect Gene to HpoTerm)
  • hp.obo (Required for HpoTerms and their connection to each other)
  1. Data can be loaded directly from the code with Ontology::from_standard:
    use hpo::Ontology;
    let ontology = Ontology::from_standard("/path/to/master-data/").unwrap();
  1. Or it can be converted to a localy binary by copy examples/obo_to_bin.rs into your project, then run . cargo run --example --release obo_to_bin <PATH TO FOLDER WITH JAX DATA> <OUTPUT FILENAME> Finally, load the data using Ontology::from_binary:
    use hpo::Ontology;
    let ontology = Ontology::from_binary("your-hpo-binary.hpo").unwrap();
  1. Another possibility is to use the snapshot from the Github repository of this crate which contains a binary build of the ontology https://github.com/anergictcell/hpo/blob/main/tests/ontology.hpo. IT will not always be up to date, so please double-check yourself.


use hpo::{Ontology, HpoTermId};
use hpo::annotations::{GeneId, OmimDiseaseId};

fn example() {
    let ontology = Ontology::from_standard("/path/to/master-data/").unwrap();

    // iterate HPO terms
    for term in &ontology {
        // do something with term

    // iterate Genes
    for gene in ontology.genes() {
        // do something with gene

    // iterate omim diseases
    for disease in ontology.omim_diseases() {
        // do something with disease

    // get a single HPO term using HPO ID
    let hpo_id = HpoTermId::try_from("HP:0000123").unwrap();
    let term = ontology.hpo(hpo_id);

    // get a single HPO term using `u32` part of HPO ID
    let term = ontology.hpo(123u32);

    // get a single Omim disease
    let disease_id = OmimDiseaseId::from(12345u32);
    let disease = ontology.omim_disease(&disease_id);

    // get a single Gene
    let hgnc_id = GeneId::from(12345u32);
    let gene = ontology.gene(&hgnc_id);

    // get a single Gene by its symbol
    let gene = ontology.gene_by_name("GBA");


HPO term

use hpo::Ontology;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();

    let term = ontology.hpo(123u32).unwrap();

    assert_eq!("Abnormality of the nervous system", term.name());
    assert_eq!("HP:000123".to_string(), term.id().to_string());

    // iterate all parents
    for p in term.parents() {
        println!("{}", p.name())

    // iterate all children
    for p in term.children() {
        println!("{}", p.name())

    let term2 = ontology.hpo(1u32).unwrap();



use hpo::Ontology;
use hpo::similarity::GraphIc;
use hpo::term::InformationContentKind;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();
    let term1 = ontology.hpo(123u32).unwrap();
    let term2 = ontology.hpo(1u32).unwrap();

    let ic = GraphIc::new(InformationContentKind::Omim);
    let similarity = term1.similarity_score(&term2, &ic);


Identify which genes (or diseases) are enriched in a set of HpoTerms, e.g. in the clinical information of a patient or patient cohort

use hpo::Ontology;
use hpo::{HpoSet, term::HpoGroup};
use hpo::stats::hypergeom::gene_enrichment;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();

    let mut hpos = HpoGroup::new();
    let patient_ci = HpoSet::new(&ontology, hpos);

    let mut enrichments = gene_enrichment(&ontology, &patient_ci);

    // the results are not sorted by default
    enrichments.sort_by(|a, b| {

    for gene in enrichments {
        println!("{}\t{}\t({})", gene.id(), gene.pvalue(), gene.enrichment());


As the saying goes: "Make it work, make it good, make it fast". The work and good parts are realized in PyHPO. And even though I tried my best to make it fast, I was still hungry for more. So I started developing the hpo Rust library in December 2022. Even without micro-benchmarking and tuning performance as much as I did for PyHPO, hpo is indeed much much faster already now.

The below benchmarks were run non scientificially and your mileage may vary. I used a MacBook Air M1, rustc 1.68.0, Python 3.9 and /usr/bin/time for timing.

Benchmark PyHPO hpo (single-threaded) hpo (multi-threaded)
Read and Parse Ontology 6.4 s 0.22 s 0.22 s
Similarity of 17,245 x 1,000 terms 98.5 s 4.6 s 1.0 s
Similarity of GBA1 to all Diseases 380 s 15.8 s 3.0 s
Disease enrichment in all Genes 11.8 s 0.4 s 0.3 s
Common ancestors of 17,245 x 10,000 terms 225.2 s 10.5 2.1

Technical design

There is some info about the plans for the implementation in the Technical Design document


~160K SLoC