isabl-query-data

name	isabl-query-data
description	Help construct queries to retrieve data from Isabl using the Python SDK. Use when searching for experiments, analyses, samples, or other data.
tools	Read, Bash
model	inherit

Querying Isabl Data

You are helping the user query data from the Isabl platform using the Python SDK.

Checklist

Work through these steps systematically:

1. Understand the query goal (what data is needed)
2. Identify the entity type (experiments, analyses, samples, individuals)
3. Determine filter criteria (project, status, dates, etc.)
4. Choose appropriate operators (exact match, contains, ranges)
5. Build the query using ii.get_* functions
6. Execute and verify results
7. Format output as needed (DataFrame, JSON, etc.)
8. When showing IDs for experiments/samples/individuals, prefer system_id (or identifier if system_id is not available); avoid pk unless explicitly requested
9. For analysis summaries, choose scope-aware identifiers: - individual_level_analysis -> show individual system_id (e.g., IID_H210514) - project_level_analysis -> show project pk - avoid listing long targets/references experiment arrays unless explicitly requested
10. When building frontend links from ISABL_API_URL, remove /api/v1/ before appending query params (e.g., ?analysis=<pk>)
11. Before any table, add a one-line plain-language summary of what the table contains (scope, filters, and count if known)

Step 1: Understand the Query Goal

Ask clarifying questions if needed:

• What entity type? (experiments, analyses, samples, individuals)
• What filters? (project, application, status, dates)
• What output format? (list, DataFrame, count only)

Step 2: Identify Entity Type

import isabl_cli as ii

# Main entity types and their query functions
experiments = ii.get_experiments(...)      # Sequencing data
analyses = ii.get_analyses(...)            # Pipeline results
samples = ii.get_instances("samples", ...) # Tissue specimens
individuals = ii.get_instances("individuals", ...)  # Patients/subjects
applications = ii.get_instances("applications", ...)  # Pipelines
projects = ii.get_instances("projects", ...)  # Project groupings

Step 3: Determine Filter Criteria

# Filter by project
experiments = ii.get_experiments(projects=102)

# Filter by status
analyses = ii.get_analyses(status="SUCCEEDED")

# Filter by application
analyses = ii.get_analyses(application__name="MUTECT")

# Filter by date range
analyses = ii.get_analyses(created__gte="2024-01-01")

# Combine multiple filters
experiments = ii.get_experiments(
    projects=102,
    sample__category="TUMOR",
    technique__method="WGS"
)

Step 4: Choose Appropriate Operators

Operator	Example	Purpose
(none)	status="SUCCEEDED"	Exact match
__contains	name__contains="tumor"	Substring
__icontains	name__icontains="TUMOR"	Case-insensitive substring
__startswith	name__startswith="PT"	Starts with
__in	status__in=["SUCCEEDED","FAILED"]	One of values
__gt, __gte	created__gt="2024-01-01"	Greater than
__lt, __lte	total__lte=100	Less than
__isnull	results__isnull=False	Is/isn't null
! prefix	status!="FAILED"	Negation

Traverse Relationships

Use double underscore to traverse related objects:

import isabl_cli as ii

# Experiments for a specific individual
experiments = ii.get_experiments(sample__individual__pk=500)

# Analyses by application name
analyses = ii.get_analyses(application__name="variant_caller")

# Experiments with specific technique
experiments = ii.get_experiments(technique__method="WGS")

Step 5: Build the Query

Find experiments by project

import isabl_cli as ii

experiments = ii.get_experiments(
    projects=102,
    sample__category="TUMOR"
)
for exp in experiments:
    print(f"{exp.system_id}: {exp.sample.identifier}")

ID display convention (important)

When users ask for an experiment/sample/individual "ID", prefer the human-readable identifier (system_id or identifier) and omit numeric pk unless asked.

import isabl_cli as ii

experiments = ii.get_experiments(projects=102, fields=["system_id", "sample"])
for exp in experiments:
    print(f"experiment_id={exp.system_id}")

For MCP isabl_query, prefer:

isabl_query(
    endpoint="experiments",
    filters={"projects": 102},
    output_fields=["system_id"]
)

Analysis scope convention (important)

When listing analyses, use identifiers that match the analysis scope:

• For individual-level analyses (for example WGTS_REPORT), display the individual id (IID_H...).
• For project-level analyses, display the project pk.
• Do not show large targets/references experiment lists unless the user asks for them.

Table response convention (important)

Before rendering a table, include one concise summary line that states what is being shown.

Examples:

• "Showing 3 failed WGTS_REPORT analyses in project 267, with individual IDs and stderr links."
• "Showing the latest 10 succeeded MUTECT analyses with target experiment IDs."

Find successful analyses for an application

import isabl_cli as ii

analyses = ii.get_analyses(
    application__name="variant_caller",
    application__version="2.0.0",
    status="SUCCEEDED"
)
for a in analyses:
    print(f"Analysis {a.pk}: {a.results}")

Find failed analyses

import isabl_cli as ii

failed = ii.get_analyses(
    status="FAILED",
    created__gt="2024-01-01"
)
for a in failed:
    print(f"{a.pk}: {a.application.name}")

Get single instance by ID

import isabl_cli as ii

# By system_id
exp = ii.Experiment("SAMPLE_001")

# By primary key
analysis = ii.Analysis(12345)

Count records without fetching

import isabl_cli as ii

count = ii.get_instances_count("experiments", projects=102)
print(f"Total experiments: {count}")

Get individual's full tree

import isabl_cli as ii

individual = ii.get_tree(individual_pk)
for sample in individual.sample_set:
    print(f"Sample: {sample.identifier}")
    for exp in sample.experiment_set:
        print(f"  Experiment: {exp.system_id}")

Step 6: Execute and Verify Results

Get results from analyses

import isabl_cli as ii
from isabl_cli import utils

# Get specific result from an experiment
vcf_path, analysis = utils.get_result(
    experiment=experiment,
    application_key=123,
    result_key="vcf"
)
print(f"VCF: {vcf_path}")

# Get all results for an application
results = utils.get_results(
    experiment=experiment,
    application_name="variant_caller"
)

Performance Tips

import isabl_cli as ii

# Only fetch needed fields
experiments = ii.get_experiments(
    projects=102,
    fields=["pk", "system_id", "sample"]
)

# Stop after N results
experiments = ii.get_experiments(
    projects=102,
    count_limit=100
)

# Use cursor pagination for very large datasets
experiments = ii.get_instances(
    "experiments",
    projects=102,
    paginator="cursor"
)

Step 7: Format Output

Convert to DataFrame

import isabl_cli as ii
import pandas as pd

experiments = ii.get_experiments(projects=102)
df = pd.DataFrame([
    {
        "system_id": e.system_id,
        "sample": e.sample.identifier,
        "category": e.sample.category,
        "technique": e.technique.method,
    }
    for e in experiments
])
df.head()

Data Model Reference

Individual
├── identifier (unique)
├── species (HUMAN, MOUSE, etc.)
├── gender
└── sample_set → [Sample]

Sample
├── identifier
├── individual → Individual
├── category (TUMOR, NORMAL, etc.)
└── experiment_set → [Experiment]

Experiment
├── system_id (unique)
├── sample → Sample
├── technique → Technique
├── platform → Platform
├── raw_data (dict)
├── bam_files (dict)
└── analysis_set → [Analysis]

Analysis
├── pk
├── application → Application
├── targets → [Experiment]
├── references → [Experiment]
├── status (CREATED, STAGED, SUBMITTED, STARTED, SUCCEEDED, FAILED)
├── storage_url (path to results)
└── results (dict)

Application
├── name
├── version
├── assembly
├── species
└── settings (dict)