usher-exploring/.planning/research/FEATURES.md

Feature Landscape

Domain: Gene Candidate Discovery and Prioritization for Rare Disease / Ciliopathy Research Researched: 2026-02-11 Confidence: MEDIUM

Table Stakes

Features users expect. Missing = pipeline is not credible.

Feature	Why Expected	Complexity	Notes
Multi-evidence scoring	Standard in modern gene prioritization; single-source approaches insufficient for rare disease	Medium	Requires weighting scheme, score normalization across evidence types
Reproducibility documentation	Required for publication and scientific validity; FDA/NIH standards emphasize reproducible pipelines	Low-Medium	Parameter logging, version tracking, seed control, execution environment capture
Data provenance tracking	W3C PROV standard; required to trace analysis steps and validate results	Medium	Track all data transformations, source versions, timestamps, intermediate results
Known gene validation	Benchmarking against established disease genes is standard practice; without this, no confidence in results	Low-Medium	Positive control set, recall metrics at various rank cutoffs (e.g., recall@10, recall@50)
Quality control checks	Standard for NGS and bioinformatics pipelines; catch data issues early	Low-Medium	Missing data detection, outlier identification, distribution checks, data completeness metrics
Structured output format	Machine-readable outputs enable downstream analysis and integration	Low	CSV/TSV for tabular data, JSON for metadata, standard column naming
Basic visualization	Visual inspection of scores, distributions, and rankings is expected	Medium	Score distribution plots, rank visualization, evidence contribution plots
Literature evidence	Gene function annotation incomplete; literature mining standard for discovery pipelines	High	PubMed/literature search integration, manual or automated; alternatively curated disease-gene databases
HPO/phenotype integration	Standard for rare disease gene prioritization since tools like Exomiser, LIRICAL	Medium	Human Phenotype Ontology term matching, phenotype similarity scoring if applicable
API-based data retrieval	Manual downloads don't scale; automated retrieval from gnomAD, UniProt, GTEx, etc. is expected	Medium	Rate limiting, error handling, caching, retry logic
Batch processing	Single-gene analysis doesn't scale to 20K genes	Low-Medium	Parallel execution, progress tracking, resume-from-checkpoint
Parameter configuration	Hard-coded parameters prevent adaptation; config files standard	Low	YAML/JSON config, CLI arguments, validation

Differentiators

Features that set product apart. Not expected, but valued.

Feature	Value Proposition	Complexity	Notes
Explainable scoring	SHAP/attribution methods show WHY genes rank high; critical for discovery (not just diagnosis)	High	SHAP-style contribution analysis, per-gene evidence breakdown, visual explanations
Systematic under-annotation bias handling	Novel: most tools favor well-studied genes; correcting publication bias is research advantage	High	Annotation completeness score as evidence layer; downweight literature-heavy features for under-studied candidates
Cilia-specific knowledgebase integration	Leverage CilioGenics, CiliaMiner, ciliopathy databases for domain-focused scoring	Medium	Custom evidence layer; API/download from specialized databases
Sensitivity analysis	Systematic parameter tuning rare in discovery pipelines; shows robustness	Medium-High	Grid search or DoE-based parameter sweep; rank stability metrics across configs
Tiered output with rationale	Not just ranked list but grouped by confidence/evidence type; aids hypothesis generation	Low-Medium	Tier classification logic (e.g., high/medium/low confidence), evidence summary per tier
Multi-modal evidence weighting	Naive Bayesian integration or optimized weights outperform equal weighting	Medium	Weight optimization using known positive controls, cross-validation
Negative control validation	Test against known non-disease genes to assess specificity; rare in discovery pipelines	Low-Medium	Negative gene set (e.g., housekeeping genes), precision metrics
Evidence conflict detection	Flag genes with contradictory evidence (e.g., high expression but low constraint)	Medium	Rule-based or correlation-based conflict identification
Interactive HTML report	Modern tools (e.g., MultiQC-style) provide browsable results; better than static CSV	Medium	HTML generation, embedded plots, sortable tables, linked evidence
Incremental update capability	Re-run with new data sources without full recomputation	Medium-High	Modular pipeline, cached intermediate results, dependency tracking
Cross-species homology scoring	Animal model phenotypes critical for ciliopathy; ortholog-based evidence integration	Medium	DIOPT/OrthoDB integration, phenotype transfer from model organisms
Automated literature scanning with LLM	Emerging: LLM-based RAG for literature evidence extraction and validation	High	LLM API integration, prompt engineering, faithfulness checks, cost management

Anti-Features

Features to explicitly NOT build.

Anti-Feature	Why Avoid	What to Do Instead
Real-time web dashboard	Overkill for research tool; adds deployment complexity, security concerns	Static HTML reports + CLI; Jupyter notebooks for interactive exploration if needed
GUI for parameter tuning	Research pipelines need reproducible command-line execution; GUIs hinder automation	YAML config files + CLI; document parameter rationale in config comments
Variant-level analysis	Out of scope for gene-level discovery; conflates discovery with diagnostic prioritization	Focus on gene-level evidence aggregation; refer users to Exomiser/LIRICAL for variant work
Custom alignment/variant calling	Well-solved problem; reinventing the wheel wastes time	Use standard BAM/VCF inputs from established pipelines; focus on gene prioritization logic
Social features (sharing, comments)	Research tool, not collaboration platform	File-based outputs (shareable via Git/email); documentation in README
Real-time database sync	Bioinformatics data versions change slowly; real-time sync unnecessary	Versioned data snapshots with documented download dates; update quarterly or as needed
One-click install for all dependencies	Bioinformatics tools have complex dependencies; false promise	Conda environment.yml or Docker container; document setup steps clearly
Machine learning model training	Small positive control set insufficient for robust ML; rule-based more transparent	Weighted scoring with manually tuned/optimized weights; reserve ML for future with larger training data

Feature Dependencies

Parameter Configuration
    └──requires──> Quality Control Checks
                      └──requires──> Data Provenance Tracking

Multi-Evidence Scoring
    ├──requires──> API-Based Data Retrieval
    ├──requires──> Literature Evidence
    └──requires──> Structured Output Format

Explainable Scoring
    └──requires──> Multi-Evidence Scoring
                      └──enhances──> Interactive HTML Report

Known Gene Validation
    └──requires──> Multi-Evidence Scoring
                      └──enables──> Sensitivity Analysis

Tiered Output with Rationale
    └──requires──> Explainable Scoring
                      └──enhances──> Interactive HTML Report

Batch Processing
    └──requires──> Parameter Configuration
                      └──enables──> Sensitivity Analysis

Negative Control Validation
    └──requires──> Known Gene Validation (uses similar metrics)

Evidence Conflict Detection
    └──requires──> Multi-Evidence Scoring

Incremental Update Capability
    └──requires──> Data Provenance Tracking
                      └──requires──> Batch Processing

Cross-Species Homology Scoring
    └──requires──> API-Based Data Retrieval

Automated Literature Scanning with LLM
    └──requires──> Literature Evidence
                      └──conflicts──> Manual curation (choose one approach per evidence layer)

Dependency Notes

• Parameter Configuration → QC Checks → Data Provenance: Foundation stack; parameters must be logged, QC applied, and tracked before any analysis
• Multi-Evidence Scoring requires API-Based Data Retrieval: Can't score without data; retrieval must be robust and cached
• Explainable Scoring requires Multi-Evidence Scoring: Can't explain scores that don't exist; explanations decompose composite scores
• Known Gene Validation enables Sensitivity Analysis: Positive controls provide ground truth for parameter tuning
• Automated Literature Scanning conflicts with Manual Curation: Choose one approach per evidence layer to avoid redundancy and conflicting evidence

MVP Recommendation

Launch With (v1)

Minimum viable pipeline for ciliopathy gene discovery.

• Multi-evidence scoring (6 layers: annotation, expression, sequence, localization, constraint, phenotype)
• API-based data retrieval with caching (gnomAD, GTEx, HPA, UniProt, Model Organism DBs)
• Known gene validation (Usher syndrome genes, known ciliopathy genes as positive controls)
• Reproducibility documentation (parameter logging, versions, timestamps)
• Data provenance tracking (source file versions, processing steps, intermediate results)
• Structured output format (CSV with ranked genes, evidence scores per column)
• Quality control checks (missing data detection, outlier flagging, distribution checks)
• Batch processing (parallel execution across 20K genes)
• Parameter configuration (YAML config for weights, thresholds, data sources)
• Tiered output with rationale (High/Medium/Low confidence tiers, evidence summary)
• Basic visualization (score distributions, top candidate plots)

Rationale: These features enable credible, reproducible gene prioritization at scale. Without validation, explainability, and QC, results are untrustworthy. Without tiered output, generating hypotheses from 20K genes is overwhelming.

Add After Validation (v1.x)

Features to add once core pipeline is validated against known ciliopathy genes.

• Interactive HTML report (browsable results with sortable tables, linked evidence) — Trigger: After v1 produces validated candidate list; when sharing results with collaborators
• Explainable scoring (per-gene evidence contribution breakdown, SHAP-style attribution) — Trigger: After identifying novel candidates; when reviewers ask "why is this gene ranked high?"
• Negative control validation (test against housekeeping genes to assess specificity) — Trigger: After positive control validation succeeds; to quantify false positive rate
• Evidence conflict detection (flag genes with contradictory evidence patterns) — Trigger: After observing unexpected high-ranking genes; to catch data quality issues
• Sensitivity analysis (systematic parameter sweep, rank stability metrics) — Trigger: When preparing for publication; to demonstrate robustness
• Cross-species homology scoring (zebrafish/mouse phenotype evidence integration) — Trigger: If animal model evidence proves valuable in initial analysis

Future Consideration (v2+)

Features to defer until core pipeline is published and validated.

• Automated literature scanning with LLM (RAG-based PubMed evidence extraction) — Why defer: High complexity, cost, and uncertainty; manual curation sufficient for initial discovery
• Incremental update capability (re-run with new data without full recomputation) — Why defer: Overkill for one-time discovery project; valuable if pipeline becomes ongoing surveillance tool
• Multi-modal evidence weighting optimization (Bayesian integration, cross-validation) — Why defer: Requires larger training set of known genes; manual weight tuning sufficient initially
• Systematic under-annotation bias handling — Why defer: Novel research question; defer until after initial discovery validates approach
• Cilia-specific knowledgebase integration (CilioGenics, CiliaMiner) — Why defer: Nice-to-have; primary evidence layers sufficient for initial analysis; add if reviewers request

Feature Prioritization Matrix

Feature	User Value	Implementation Cost	Priority
Multi-evidence scoring	HIGH	MEDIUM	P1
Known gene validation	HIGH	LOW-MEDIUM	P1
Reproducibility documentation	HIGH	LOW-MEDIUM	P1
Data provenance tracking	HIGH	MEDIUM	P1
Structured output format	HIGH	LOW	P1
Quality control checks	HIGH	LOW-MEDIUM	P1
API-based data retrieval	HIGH	MEDIUM	P1
Batch processing	HIGH	LOW-MEDIUM	P1
Parameter configuration	HIGH	LOW	P1
Tiered output with rationale	HIGH	LOW-MEDIUM	P1
Basic visualization	HIGH	MEDIUM	P1
Explainable scoring	HIGH	HIGH	P2
Interactive HTML report	MEDIUM	MEDIUM	P2
Sensitivity analysis	MEDIUM	MEDIUM-HIGH	P2
Evidence conflict detection	MEDIUM	MEDIUM	P2
Negative control validation	MEDIUM	LOW-MEDIUM	P2
Cross-species homology	MEDIUM	MEDIUM	P2
Incremental updates	LOW	MEDIUM-HIGH	P3
LLM literature scanning	LOW	HIGH	P3
Multi-modal weight optimization	MEDIUM	MEDIUM	P3
Under-annotation bias correction	MEDIUM	HIGH	P3
Cilia knowledgebase integration	LOW-MEDIUM	MEDIUM	P3

Priority key:

• P1: Must have for credible v1 pipeline
• P2: Should have; add after v1 validation
• P3: Nice to have; future consideration

Competitor Feature Analysis

Feature	Exomiser	LIRICAL	CilioGenics Tool	Usher Pipeline Approach
Multi-evidence scoring	Yes (variant+pheno combined score)	Yes (LR-based)	Yes (ML predictions)	Yes (6-layer weighted scoring)
Phenotype integration	HPO-based (strong)	HPO-based (strong)	Not primary	HPO-compatible but not required (sensory cilia focus)
Known gene validation	Benchmarked on Mendelian diseases	Benchmarked on Mendelian diseases	Validated on known cilia genes	Validate on Usher + known ciliopathy genes
Explainable scoring	Limited	Post-test probability (interpretable)	ML black box	SHAP-style per-gene evidence breakdown (planned P2)
Variant-level analysis	Primary focus	Primary focus	No (gene-level only)	No (out of scope; gene-level discovery)
Literature evidence	Automated (limited)	Limited	Text mining used in training	Manual/automated (planned P3)
Tiered output	Yes (rank-ordered)	Yes (post-test prob tiers)	Yes (confidence scores)	Yes (High/Medium/Low tiers + rationale)
Under-annotation bias	Not addressed	Not addressed	Not addressed	Explicitly addressed (novel)
Domain-specific focus	Mendelian disease diagnosis	Mendelian disease diagnosis	Cilia biology	Usher syndrome / ciliopathy discovery
Reproducibility	Config files, versions logged	Config files, versions logged	Not emphasized	Extensive provenance tracking

Key Differentiators for Usher Pipeline:

1. Discovery vs. Diagnosis: Exomiser/LIRICAL prioritize variants in patient genomes (diagnosis); Usher pipeline screens all genes for under-studied candidates (discovery)
2. Under-annotation bias handling: Explicitly score annotation completeness and de-bias toward under-studied genes
3. Explainable scoring for hypothesis generation: Per-gene evidence breakdown to guide experimental follow-up, not just rank genes
4. Sensory cilia focus: Retina/cochlea expression, ciliopathy phenotypes, subcellular localization evidence tailored to Usher biology

Sources

Tool Features and Benchmarking

• Survey and improvement strategies for gene prioritization with LLMs (2026)
• Clinical and Cross-Domain Validation of LLM-Guided Gene Prioritization (2026)
• Automating candidate gene prioritization with LLMs (2025)
• Explicable prioritization with rule-based and ML algorithms (2024)
• Phenotype-driven approaches to enhance variant prioritization (2022)
• Evaluation of phenotype-driven gene prioritization methods (2022)
• Add LIRICAL and Exomiser scores to seqr (GitHub issue) (2021)
• Large-scale benchmark of gene prioritization methods (2017)

Reproducibility and Standards

• Rare disease gene discovery in 100K Genomes Project (2025)
• Standards for validating NGS bioinformatics pipelines (AMP/CAP) (2018)
• Genomics pipelines and data integration challenges (2017)
• Experiences with workflows for bioinformatics (2015)

Parameter Tuning and Sensitivity Analysis

• Algorithm sensitivity analysis for tissue segmentation pipelines (2017)
• doepipeline: systematic approach to optimizing workflows (2019)

Multi-Evidence Integration

• Multi-dimensional evidence-based candidate gene prioritization (2009)
• Extensive analysis of disease-gene associations with network integration (2014)
• Survey of gene prioritization tools for Mendelian diseases (2020)

Explainability and Interpretability

• Explainable deep learning for cancer target prioritization (2024)
• Interpretable machine learning for genomics (2021)
• SeqOne's DiagAI Score with xAI (recent)
• Spectrum of explainable and interpretable ML for genomics (2023)

Cilia Biology and Ciliopathy Tools

• Prioritization tool for cilia-associated genes (2024)
• CilioGenics: integrated method for predicting ciliary genes (2024)
• CiliaMiner: integrated database for ciliopathy genes (2023)
• Systems-biology approach to ciliopathy disorders (2011)

Visualization and Reporting

• Computational pipeline for functional gene discovery (2021)
• JWES: pipeline for gene-variant discovery and annotation (2021)

Data Quality and Provenance

• Data quality assurance in bioinformatics (2025)
• Bioinformatics pipeline for data quality control (recent)
• Bionitio: demonstrating best practices for bioinformatics CLI (2019)

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Feature research for: Bioinformatics Gene Candidate Discovery and Prioritization (Rare Disease / Ciliopathy) Researched: 2026-02-11 Confidence: MEDIUM — based on WebSearch findings verified across multiple sources; Context7 not available for specialized bioinformatics tools; recommendations synthesized from peer-reviewed publications and established tool documentation