mouse-phenome-database

mouse-phenome-database

Overview

The Mouse Phenome Database (MPD), maintained at the Jackson Laboratory, catalogs standardized phenotype measurements across inbred, recombinant inbred (e.g., BXD), and Collaborative Cross / Diversity Outbred mouse panels. It aggregates 520+ projects spanning metabolic, cardiovascular, behavioral, hematological, and immunological traits. The REST API at https://phenome.jax.org/api is free, requires no authentication, and is documented at <https://phenome.jax.org/about/api>. MPD measurement IDs (measnum) are project-scoped 5-digit integers — there is no global "measnum 10001 = body weight" mapping; valid measnums must be discovered per project via the measureinfo endpoint.

When to Use

• Selecting inbred strains with extreme phenotypes (highest/lowest fasted glucose, body weight, heart rate, etc.) as experimental models
• Pulling individual-animal data from BXD / CC / DO panels for QTL mapping with R/qtl2 or similar tools
• Comparing strain means and variance across metabolic, behavioral, or cardiovascular measures for genetic background studies
• Finding MPD projects that measure a trait of interest using ontology terms (MP, VT, MA) or free-text descriptions
• Validating mouse strain nomenclature (canonical JAX names ↔ stock numbers ↔ MGI IDs) before submitting orders or analyses
• Looking up coordinates and annotations for mouse genes in the MPD/MGI cross-reference
• Use monarch-database instead for disease-gene-phenotype knowledge graphs (HPO ↔ MP ↔ disease)
• Use ensembl-database instead for transcript-level mouse gene annotations and variant consequence prediction

Prerequisites

• Python packages: requests, pandas, matplotlib
• Data requirements: a project symbol (e.g., Jaxwest1, Auwerx1) or a measnum (e.g., 15101); strain names follow JAX canonical nomenclature (e.g., C57BL/6J, DBA/2J)
• Environment: internet connection; no API key required
• Rate limits: no published hard limit; keep bursts under ~5 requests/second and add time.sleep(0.3) between requests in loops

pip install requests pandas matplotlib

Quick Start

import requests

MPD = "https://phenome.jax.org/api"

# 1) Pick a project (Jaxwest1 — cardiovascular phenotyping on inbred panel)
r = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30)
strains = r.json()["strains"]
print(f"Jaxwest1: {len(strains)} strains tested")

# 2) Discover its measures
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30)
measures = r.json()["measures_info"]
print(f"Jaxwest1 measures: {len(measures)}; first: measnum={measures[0]['measnum']} "
      f"varname={measures[0]['varname']}  ({measures[0]['descrip']}, {measures[0]['units']})")

# 3) Pull strain means for heart rate (varname=HR, measnum=15101)
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30)
sm = r.json()["strainmeans"]
print(f"\nHeart rate strain means: {len(sm)} rows  (one per strain × sex)")
top = sorted(sm, key=lambda x: x["mean"], reverse=True)[:5]
for s in top:
    print(f"  {s['strain']:<20}  sex={s['sex']}  mean={s['mean']:.0f} {s.get('varname','')}  n={s['nmice']}")

Core API

Module 1: Browse Projects — /projects

Lists all MPD projects with full metadata. Filter via investigator, projsym, projid, mpdsector, largecollab, panelsym. Use /project_filters/{filtername} to see the allowed values of mpdsector, largecollab, or panelsym before filtering.

import requests, pandas as pd

MPD = "https://phenome.jax.org/api"

# List allowed panel symbols (e.g., BXD, CC, DO)
filters = requests.get(f"{MPD}/project_filters/panelsym", timeout=30).json()
print(f"Available panels ({filters['count']}):", [t['term'] for t in filters['terms']][:10])

# All projects in the BXD recombinant inbred panel
r = requests.get(f"{MPD}/projects", params={"panelsym": "BXD"}, timeout=30)
projects = r.json()["projects"]
print(f"BXD projects: {len(projects)}")
df = pd.DataFrame([{
    "projsym": p["projsym"],
    "pi": p.get("pistring", "")[:40],
    "nstrains": p.get("nstrains"),
    "ages": p.get("ages"),
    "sector": p.get("mpdsector"),
    "title": (p.get("title") or "")[:60],
} for p in projects])
print(df.head(10).to_string(index=False))

# Filter by MPD sector — komp, pheno, qtla, snp, onestrain, phenoarchive
r = requests.get(f"{MPD}/projects", params={"mpdsector": "qtla"}, timeout=30)
qtl_projects = r.json()["projects"]
print(f"QTL-archive projects: {len(qtl_projects)}")
for p in qtl_projects[:5]:
    print(f"  {p['projsym']:<15} panel={p.get('panelsym') or '--':<6} nstrains={str(p.get('nstrains') or '--'):>4}  {(p.get('title') or '')[:55]}")

Module 2: Project Detail — /projects/{projsym}/...

Each project has sub-resources for its dataset (CSV of every animal × every measure), the strain panel it tested, the publications it produced, and (for QTL projects) the genetic markers used.

import requests, io, pandas as pd

MPD = "https://phenome.jax.org/api"

# Full per-animal dataset as CSV (default). Use json=yes for JSON.
r = requests.get(f"{MPD}/projects/Jaxwest1/dataset", timeout=60)
df = pd.read_csv(io.StringIO(r.text))
print(f"Jaxwest1 dataset: {df.shape[0]} animals × {df.shape[1]} columns")
print(df.columns[:12].tolist())
print(df[["strain", "sex", "animal_id", "HR", "QRS", "bw"]].head(5).to_string(index=False))

# Strains tested in a project + publication list
strains = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30).json()
print(f"Jaxwest1 strains ({strains['count']}):")
for s in strains["strains"][:5]:
    print(f"  {s['strainname']:<20}  stock={s['stocknum']}  vendor={s['vendor']}")

pubs = requests.get(f"{MPD}/projects/Jaxwest1/publications", timeout=30).json()
print(f"\nPublications: {pubs['count']}")

Module 3: Measure Discovery — /pheno/measureinfo/{selector}

This is the canonical way to discover valid measnum values. The selector is either a project symbol (returns all measures in that project) or a measnum (returns metadata for one measure).

import requests, pandas as pd

MPD = "https://phenome.jax.org/api"

# All measures in the Jaxwest1 cardiovascular project
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30)
measures = r.json()["measures_info"]
df = pd.DataFrame([{
    "measnum": m["measnum"],
    "varname": m["varname"],
    "descrip": m["descrip"],
    "units": m.get("units"),
    "sex": m.get("sextested"),
    "age": m.get("ageweeks"),
} for m in measures])
print(f"Jaxwest1 has {len(df)} measures")
print(df.head(10).to_string(index=False))

# Single-measure metadata lookup (protocol + dimensional details)
r = requests.get(f"{MPD}/pheno/measureinfo/15101", timeout=30).json()
m = r["measures_info"][0]
print(f"measnum {m['measnum']} ({m['varname']}): {m['descrip']}")
print(f"  units: {m.get('units')}")
print(f"  project: {m.get('projsym')}  panel: {m.get('panelsym') or m.get('paneldesc')}")
print(f"  sex tested: {m.get('sextested')}  age: {m.get('ageweeks')}")
print(f"  method:  {(m.get('method') or '')[:120]}")

Module 4: Strain Means — /pheno/strainmeans/{selector}

Returns strain × sex summary statistics. The selector takes a project symbol (all strain means for the project) or one-or-more comma-separated measnums. Each row contains measnum, varname, strain, strainid, sex, mean, sd, sem, cv, minval, maxval, nmice, zscore.

import requests, pandas as pd

MPD = "https://phenome.jax.org/api"

# Strain means for one measure (heart rate, measnum=15101 from Jaxwest1)
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30)
sm = pd.DataFrame(r.json()["strainmeans"])
print(f"Rows: {len(sm)}  ({sm['strain'].nunique()} strains × {sm['sex'].nunique()} sexes)")

# Rank strains by male HR
male = sm[sm["sex"] == "m"].sort_values("mean", ascending=False)
print(male[["strain", "mean", "sd", "sem", "nmice", "zscore"]].head(8).to_strin