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_string(index=False))

# Optional: model-adjusted means (lsmeans) account for covariates fit in MPD's models.
# Use lsmeans when comparing strains across cohorts within a project.
r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json()
print("LS-mean measures available for Jaxwest1:", r.get("ls_measures", [])[:10])

Module 5: Per-Animal Values — /pheno/animalvals/{measnum}

Raw per-animal observations for one measure. Each row carries animal_id, animal_projid, measnum, projsym, sex, stocknum, strain, strainid, value, varname, zscore. Use this for QTL mapping, mixed-effects modeling, or distribution analysis.

import requests, pandas as pd

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

# Per-animal heart rate values
r = requests.get(f"{MPD}/pheno/animalvals/15101", timeout=30)
ad = pd.DataFrame(r.json()["animaldata"])
print(f"Animals measured: {len(ad)}")
print(ad[["animal_id", "strain", "sex", "value", "zscore"]].head(8).to_string(index=False))

# Compare male-only strain distributions
male = ad[ad["sex"] == "m"]
stats = male.groupby("strain")["value"].agg(["mean", "std", "count"]).round(2)
print(f"\nMale HR by strain (top 5 by mean):")
print(stats.sort_values("mean", ascending=False).head(5))

Module 6: Ontology-Based Measure Discovery — /pheno/measures_by_ontology/{ont_term}

Find every MPD measure annotated to a Mammalian Phenotype (MP), Vertebrate Trait (VT), or Mouse Anatomy (MA) ontology term. Optional this_term_only=yes disables descendant-term expansion; omit_baseline=yes filters out baseline measures; collapse_series=yes collapses repeated time-points.

import requests, pandas as pd

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

# MP:0001262 = "decreased body weight"
r = requests.get(f"{MPD}/pheno/measures_by_ontology/MP:0001262",
                 params={"omit_baseline": "yes", "collapse_series": "yes"},
                 timeout=30).json()
print(f"Measures mapped to MP:0001262 ('{r['ontology_terms'][0]['descrip']}'): {r['count']}")
if r["count"]:
    df = pd.DataFrame(r["measures"])
    print(df[["measnum", "varname", "descrip", "projsym"]].head(8).to_string(index=False))
else:
    print("(no direct measures; consider a broader parent term)")

Module 7: Strain Nomenclature — /straininfo

Validate and normalise strain names. Accepts name, stocknum, or mginum query params. Returns both jaxinfo[] (JAX availability/nomenclature) and mpdinfo[] (MPD's own metadata, including how many projects test this strain).

import requests

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

# Validate C57BL/6J — note `requests` URL-encodes the slash automatically in params
r = requests.get(f"{MPD}/straininfo", params={"name": "C57BL/6J"}, timeout=30).json()
jax = r["jaxinfo"][0]
mpd = r["mpdinfo"][0]
print(f"JAX:  {jax['nomenclature']}  stock={jax['stocknum']}  status={jax['avl_status']}")
print(f"MPD:  longname={mpd['longname']}  type={mpd['straintype']}  "
      f"projects={mpd['nproj']}  snp_projects={mpd['nsnpproj']}  MGI={mpd['mginum']}")

Module 8: Gene Info — /geneinfo/{symbol}

Mouse-gene coordinates (GRCm39, in bp), strand, MGI ID, and a short description. Note the response key is the literal string "gene info" (with a space).

import requests

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

r = requests.get(f"{MPD}/geneinfo/Lep", timeout=30).json()
for g in r["gene info"]:
    print(f"{g['descrip']}  chr{g['chrom']}:{g['startbp']:,}–{g['endbp']:,} ({g['strand']})")
    print(f"  type: {g['featuretype']}  MGI: {g['mginum']}  cM: {g['centimorgan']}")

Key Concepts

Measnums Are Project-Scoped, Not Global

Every measnum belongs to exactly one project. 15101 is "heart rate" only within Jaxwest1; the same physiological trait in another project has a different measnum (e.g., 35702 for body weight in Lightfoot1). Never hardcode measnums for a trait — always resolve them by:

1. Finding the project: GET /projects?panelsym=... or GET /projects?investigator=...
2. Listing its measures: GET /pheno/measureinfo/{projsym}
3. Picking the right varname / descrip / units triple from the response

Or go the other way and discover measures by ontology term first (Module 6).

Selectors: Projsym vs Measnum

Most /pheno/* endpoints take a "selector" path parameter that's overloaded:

Endpoint	Accepts as selector
/pheno/strainmeans/{selector}	projsym (e.g., Jaxwest1) or one or more comma-separated measnums
/pheno/lsmeans/{selector}	same
/pheno/measureinfo/{selector}	same
/pheno/animalvals/{measnum}	measnum only (use measureinfo to discover)
/pheno/animalvals/series/{measnum}	for timecourse/dose-response series

If you pass an unrecognised selector you get a 400 JSON response ({"error": "...selector arg must either be measure IDs or a project symbol"}), not an HTML 404 — those are diagnostic and worth surfacing.

Strain Means vs LS-Means

• strainmeans are unadjusted: simple per-strain × per-sex arithmetic means of the raw animal values.
• lsmeans are model-adjusted least-squares means from MPD's pre-fit ANOVA-style models (accounting for cohort, batch, or covariate effects when present).

For cross-project comparisons or analyses sensitive to batch effects, prefer lsmeans when available. For simple ranking and exploratory work, strainmeans is fine.

MPD Sectors (mpdsector filter)

Sector	Content
pheno	Standard inbred-strain phenotyping projects
qtla	QTL Archive — historical mapping studies with markers
komp	Knockout Mouse Project (KOMP / JaxLIMS) data
snp	SNP genotype panels (use /snpdata)
phenoarchive	Archived legacy phenotype projects
onestrain	Single-strain deep phenotyping

Discover the live list any time with GET /project_filters/mpdsector.

Common Workflows

Workflow 1: Pick a Trait → Find a Project → Plot Strain Means

Goal: From "I want to compare heart rate across inbred strains" → land on real data and produce a ranked barplot.

import requests, pandas as pd, matplotlib.pyplot as plt, time

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

# 1) Find candidate projects whose name/description hints at the trait
projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"]
candidates = [p for p in projects
              if any(kw in (p.get("title") or "").lower()
                     for kw in ["cardiovascular", "heart", "ekg", "ecg"])]
print(f"Candidate cardiovascular projects: {len(candidates)}")
for p in candidates[:5]:
    print(f"  {p['projsym']:<15}  nstrains={str(p.get('nstrains') or '--'):>3}  {(p.get('title') or '')[:60]}")

# 2) Inspect measures for the chosen project
projsym = "Jaxwest1"
mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"]
hr = next(m for m in mi if m["varname"] == "HR")
print(f"\nPicked: {projsym} measnum={hr['measnum']} varname={hr['varname']} ({hr['descrip']}, {hr['units']})")

# 3) Pull strain means, plot male strains ranked
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{hr['measnum']}", timeout=30).json()["strainmeans"])
male = sm[sm["sex"] == "m"].sort_values("mean", ascending=False).reset_index(drop=True)

fig, ax = plt.subplots(figsize=(9, 4))
bars = ax.bar(male["strain"], male["mean"], yerr=male["sem"],
              color="#1976D2", capsize=3, edgecolor="white")
ax.bar_label(bars, fmt="%.0f", padding=3, fontsize=8)
ax.set_xlabel("Strain")
ax.set_ylabel(f"Mean {hr['varname']} ({hr['units']})")
ax.set_title(f"{hr['descrip']} by inbred strain (male) — {projsym}")
plt.xticks(rotation=30, ha="right")
plt.tight_layout()
plt.savefig("mpd_strain_means.png", dpi=150, bbox_inches="tight")
print("Saved mpd_strain_means.png")

Workflow 2: Per-Animal Data → R/qtl2 CSV Export

Goal: Pull individual animal observations for a measure and shape them into a phenotype file ready for QTL mapping.

import requests, pandas as pd

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

measnum = 15101  # heart rate in Jaxwest1
mi = requests.get(f"{MPD}/pheno/measureinfo/{measnum}", timeout=30).json()["measures_info"][0]
ad = pd.DataFrame(requests.get(f"{MPD}/pheno/animalvals/{measnum}", timeout=30).json()["animaldata"])
print(f"measnum {measnum}: {mi['varname']} ({mi['descrip']}, {mi['units']}) — {len(ad)} animals")

# R/qtl2-ready phenotype CSV: rows = individuals, cols = id + covariates + phenotype
out = ad[["animal_id", "strain", "sex", "value"]].rename(
    columns={"animal_id": "id", "value": mi["varname"]}
)
out.to_csv(f"{measnum}_{mi['varname']}_qtl_pheno.csv", index=False)
print(f"Wrote {measnum}_{mi['varname']}_qtl_pheno.csv  ({len(out)} animals)")
print(out.head().to_string(index=False))

Workflow 3: Multi-Measure Heatmap Across a Strain Panel

Goal: Build a wide-format strain × measure table (z-scored) from one project for comparative visualisation.

import requests, pandas as pd, matplotlib.pyplot as plt

MPD = "https://phenome.jax.org/api"
projsym = "Jaxwest1"

# Pull all strain means for the project in one call
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{projsym}", timeout=30).json()["strainmeans"])

# Use the precomputed z-scores; pivot to strain × varname (male only for simplicity)
male = sm[sm["sex"] == "m"]
wide = male.pivot_table(index="strain", columns="varname", values="zscore", aggfunc="mean")
print(f"Shape: {wide.shape}  (strains × measures)")

# Subset to a handful of measures with full coverage
keep = wide.dropna(axis=1, thresh=int(0.8 * len(wide))).columns[:10]
wide = wide[keep].dropna()
print(f"After coverage filter: {wide.shape}")

fig, ax = plt.subplots(figsize=(8, max(3, 0.3 * len(wide))))
im = ax.imshow(wide.values, aspect="auto", cmap="RdBu_r", vmin=-2, vmax=2)
ax.set_xticks(range(len(wide.columns)))
ax.set_xticklabels(wide.columns, rotation=45, ha="right", fontsize=8)
ax.set_yticks(range(len(wide.index)))
ax.set_yticklabels(wide.index, fontsize=8)
fig.colorbar(im, ax=ax, label="z-score")
ax.set_title(f"{projsym} — strain × measure z-score heatmap (male)")
plt.tight_layout()
plt.savefig("mpd_strain_measure_heatmap.png", dpi=150, bbox_inches="tight")
print("Saved mpd_strain_measure_heatmap.png")

Key Parameters

Parameter	Endpoint	Default	Range / Options	Effect
panelsym	/projects	—	strain panel symbol (BXD, CC, DO, …)	Filter projects to one mouse panel
mpdsector	/projects	—	pheno, qtla, komp, snp, phenoarchive, onestrain	Filter projects by data sector
investigator	/projects, /investigators	—	investigator name substring	Filter projects by PI
csv	/projects, /investigators, /projects/{projsym}/dataset, etc.	no	yes	Return CSV instead of JSON
json	/projects/{projsym}/dataset	—	yes	Return JSON instead of default CSV
this_term_only	/pheno/measures_by_ontology/{ont_term}	no	yes	Disable descendant-term expansion
omit_baseline	/pheno/measures_by_ontology/{ont_term}	no	yes	Drop baseline measures from results
collapse_series	/pheno/measures_by_ontology/{ont_term}	no	yes	Collapse timecourse/dose series into single entries
region, dataset, strains	/snpdata	required	genomic region, dataset name, strain CSV	Pull SNP genotypes for region across strains
name / stocknum / mginum	/straininfo	one required	strain name, JAX stock #, or MGI ID	Validate / look up strain

Best Practices

1. Always discover measnums via /pheno/measureinfo/{projsym} before querying data. Measnums are project-scoped 5-digit integers; there is no global trait → measnum table. Hardcoding measnums you got from elsewhere will silently 404 or return "no data".

2. Use plural resource paths. MPD uses /projects, /projects/{projsym}/strains, /projects/{projsym}/dataset — not singular. Old MPD documentation and several third-party wrappers list singular paths that return HTML 404s.

3. Prefer the project-level dataset CSV for bulk analysis. When you want every animal × every measure for a project, GET /projects/{projsym}/dataset returns a single CSV in one request — much faster than looping animalvals per measnum.

4. Use lsmeans instead of strainmeans when MPD has fit a model. LS-means adjust for covariates (cohort, age, batch) baked into MPD's project-level statistical models. For comparative ranking across strains within a project, lsmeans is the more honest summary when available (/pheno/lsmeans/{projsym} returns the list of ls_measures).

5. Validate strain names with /straininfo before assuming a match. MPD uses strict JAX canonical nomenclature; nearby synonyms (B6, C57Bl/6, C57BL/6) won't always resolve. /straininfo?name=... returns both JAX and MPD records and tells you the canonical form.

6. Be polite — add time.sleep(0.3) in loops. MPD doesn't publish a hard rate limit, but the server runs on shared academic infrastructure. Keep bursts under ~5 req/s.

Common Recipes

Recipe: Find Projects Testing a Trait by Free-Text Search

import requests, pandas as pd

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

def find_projects(keyword):
    """Search project titles for a keyword (case-insensitive)."""
    projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"]
    kw = keyword.lower()
    hits = [p for p in projects if kw in (p.get("title") or "").lower()]
    return pd.DataFrame([{
        "projsym": p["projsym"],
        "panel": p.get("panelsym"),
        "nstrains": p.get("nstrains"),
        "year": p.get("projyear"),
        "title": (p.get("title") or "")[:80],
    } for p in hits])

print(find_projects("glucose").head(10).to_string(index=False))

Recipe: Pull a Whole Project as a DataFrame in One Call

import requests, io, pandas as pd

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

def load_project_dataset(projsym):
    """Fetch /projects/{projsym}/dataset CSV directly into a DataFrame."""
    r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120)
    r.raise_for_status()
    return pd.read_csv(io.StringIO(r.text))

df = load_project_dataset("Jaxwest1")
print(f"Jaxwest1: {df.shape[0]} animals × {df.shape[1]} columns")
print("Numeric columns:", df.select_dtypes("number").columns.tolist()[:8])

Recipe: Cross-Strain Comparison from Strain Means

import requests, pandas as pd

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

# Multiple measnums in one call (comma-separated selector)
selector = "15101,15102,15103"   # HR, QRS, PR from Jaxwest1
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"])
wide = (sm[sm["sex"] == "m"]
        .pivot_table(index="strain", columns="varname", values="mean", aggfunc="mean")
        .round(1))
print(wide.head(8).to_string())

Recipe: Resolve Gene → Coordinates → Adjacent Region

import requests

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

def gene_window(symbol, flank_kb=100):
    r = requests.get(f"{MPD}/geneinfo/{symbol}", timeout=30).json()
    if not r.get("gene info"):
        return None
    g = r["gene info"][0]
    return {
        "symbol": symbol,
        "chrom": g["chrom"],
        "start": max(0, g["startbp"] - flank_kb * 1000),
        "stop": g["endbp"] + flank_kb * 1000,
        "mgi": g["mginum"],
        "descrip": g["descrip"],
    }

print(gene_window("Lep", flank_kb=50))
# {'symbol': 'Lep', 'chrom': '6', 'start': 29010220, 'stop': 29123877, 'mgi': 'MGI:104663', ...}

Troubleshooting

Problem	Cause	Solution
HTTP 404 with HTML body on /strain/..., /procedure, /pheno/query, /measurement/..., /project/...	These paths don't exist — MPD's real API uses plural resource names and a different layout	Use /projects (plural), /projects/{projsym}/dataset, /pheno/strainmeans/{selector}, /pheno/measureinfo/{selector}, /straininfo
HTTP 400 with {"error": "...selector arg must either be measure IDs or a project symbol"}	The path's selector arg got something else (a strain name, a varname, a category)	Resolve the right projsym or measnum first via /projects or /pheno/measureinfo/{projsym}
HTTP 404 with {"error": "No strainmeans data found for {selector}"}	The selector is the right kind but has no data (e.g., a measnum from a different project, or a typo)	Confirm the measnum exists via /pheno/measureinfo/{measnum}; check it belongs to the project you think
KeyError: 'gene info' when parsing /geneinfo/{sym}	Response key has a literal space: "gene info", not gene_info	Access via r.json()["gene info"] exactly
dataset endpoint returns plain text instead of JSON	Default content type is CSV	Pass params={"json": "yes"} to force JSON; or parse the CSV with pd.read_csv(io.StringIO(r.text))
Strain name returns empty mpdinfo from /straininfo	Non-canonical name (e.g., B6, C57Bl/6)	Use exact JAX nomenclature (C57BL/6J); try stocknum= lookup if you have the JAX stock number
/pheno/measures_by_ontology/{term} returns count: 0 but the term exists	No direct mappings; the term is too specific	Re-query with the term's parent (the response includes ontology_terms[].parent); or drop this_term_only=yes
HTTP 5xx intermittently on large CSV pulls	MPD's per-project datasets can be tens of MB	Increase timeout=120; for very large projects use csv=yes + stream with requests.get(..., stream=True)

Related Skills

• monarch-database — disease-gene-phenotype knowledge graph with HPO ↔ MP cross-mappings; complement MPD's mouse-only data with human disease links
• ensembl-database — mouse genome annotation (GRCm39 coordinates, transcripts, VEP) — pairs with MPD /geneinfo for fine-grained gene-model details
• gwas-database — human GWAS Catalog SNP-trait associations; conceptual analogue of MPD's QTL projects for human populations
• clinvar-database — clinical variant interpretation; relevant when mapping a mouse QTL to a human disease gene

References

• Mouse Phenome Database portal — interactive dataset browser and download UI
• MPD REST API documentation — authoritative endpoint reference (the one used to build this skill)
• Bogue et al., Mammalian Genome 2020 — MPD overview paper (architecture, data content, strain coverage)
• Jackson Laboratory Inbred Strain Catalog — canonical strain nomenclature and stock numbers
• Mammalian Phenotype Ontology — MP term browser (used by /pheno/measures_by_ontology/{term})
• MPD Data Use Policy — CC-BY-4.0 license terms and citation requirements