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Lab 7 - Xenomorph Genome Repository

3 min read

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Executive Summary

  • This MongoDB implementation establishes a Xenomorph Genome Repository to manage biological data for xenomorph species. The solution uses MongoDB’s document model to store and query complex, nested data, enabling efficient analysis of specimen characteristics, threat levels, and genetic markers. The design prioritizes flexibility, scalability, and actionable biological insights for research and risk management.

Data Ingestion

Collection Creation

  • Created the xenomorph_research database and specimens collection to centralize xenomorph data, using db.createCollection('specimens') to initialize the collection
  • Dropped existing data with db.specimens.drop() to ensure the database was clean before data insertion (so I could mess with the content iteratively)

Data Insertion

  • Inserted specimen records using db.specimens.insertMany(), including fields specimen_id, species_type, threat_level, discovery_date, genetic_markers, and unique_characteristics
  • Data includes diverse specimen types (Xenomorph Prime, Facehugger, Queen, Neomorph) with nested attributes (such as genetic_markers.primary_dna, unique_characteristics.height)

Document Schema Design

  • The document-based schema centralizes specimen data in the specimens collection, reducing complexity compared to relational multi-table designs.
  • It supports scalable expansion for additional specimen types and attributes

Core Fields

  • Stores key identifiers and metrics: specimen_id (unique identfier), species_type (such as Xenomorph Prime), threat_level (0-10) and discovery_date for temporal tracking
  • Centralizes queryable data for efficient filtering & sorting, with threat_level to enable risk prioritization

Nested Fields

genetic_markers

  • primary_dna, mutation_rate, adaptation_rate (optional)
  • Captures genetic data critical for biological analysis, with flexible structure to accommodate varying attributes across specimen types
  • Enables nested queries (such asgenetic_markers.mutation_rate) for targeted genetic research

unique_characteristics

  • Type-specific attributes (such as height for Xenomorph Prime, leg_span for Facehugger, crown_height for Queen)
  • Provides descriptive metadata tailored to each species, enhancing analysis specificity without schema rigidity

Biological Data Insights

Species Distribution Report

  • Aggregates specimen counts by species_type using db.specimens.aggregate([{$group: {_id: "$species_type", specimen_count: {$sum: 1}}}, {$sort: {specimen_count: -1}}]). Highlights population trends across xenomorph types
  • Reveals balanced distribution, informing research focus on prevalent species like Neomorphs and Facehuggers

Threat Level Analysis

  • Calculates average threat_level by species_type with db.specimens.aggregate([{$group: {_id: "$species_type", average_threat_level: {$avg: "$threat_level"}}}, {$sort: {average_threat_level: -1}}]). Identifies high-risk species (e.g., Queen, Neomorph) for containment prioritization
  • Queries like db.specimens.find({threat_level: {$gt: 8}}) pinpoint high-threat specimens, guiding operational strategies

Genetic Mutation Analysis

  • Computes average mutation_rate by species_type using db.specimens.aggregate([{$group: {_id: "$species_type", average_mutation_rate: {$avg: "$genetic_markers.mutation_rate"}}}, {$sort: {average_mutation_rate: -1}}]). Highlights Neomorphs’ high mutation rates (0.22–0.24), suggesting rapid evolution
  • Supports targeted genetic sequencing to monitor evolutionary risks

Research Implications

  • High-threat specimens (e.g., threat_level ≥ 9) indicate a need for enhanced containment protocols and security measures
  • High mutation rates in Neomorphs suggest potential for rapid adaptation, requiring continuous genetic monitoring
  • Cross-species analysis (e.g., Xenomorph Prime vs. Facehugger) informs resource allocation for research and risk mitigation

Collaboration Reflection

  • I collaborated with Riker on this implementation, and we both worked on different trigger components. We created our own versions and then we combined them together to see what would be the best solution. I also used Claude AI as well as ChatGPT to fix up some of my grammar and help out with my documentation and code styling.

Lessons Learned

Data Modeling

  • Designing a flexible document structure for diverse specimen data emphasized the importance of schema adaptability in NoSQL systems. Consistent field naming (e.g., specimen_id, species_type) ensured query reliability
  • Nested fields like genetic_markers and unique_characteristics simplified data representation but required careful validation to prevent inconsistencies
  • The specimens collection’s extensible design allows future inclusion of new specimen types without major refactoring

Analysis

  • Nested queries (genetic_markers.mutation_rate) performed well with proper indexing, but complex aggregations required optimization to avoid performance bottlenecks.
  • The document schema enabled straightforward reporting, with aggregation pipelines providing clear metrics for biological research and risk management.
  • Using $group and $sort in aggregations improved interpretability, while early filtering in queries (such as threat_level: {$gt: 8}) reduced execution time.

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