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Oct 5, 2024

Bioinformatics Pipeline Optimization with Xubit Agents

How AI agents optimize genomics pipelines across cloud providers.

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Bioinformatics Pipeline Optimization


Genomics workloads require careful orchestration across compute resources. This document explores how Xubit agents optimize these workflows.


The Challenge


  • **Massive data volumes**: TB per sample
  • **Complex multi-stage pipelines**
  • **Cost sensitivity**
  • **Regulatory compliance**

  • Agent-Based Optimization


    Our Bio Research Agent monitors pipeline execution and dynamically adjusts resource allocation based on:


  • Stage-specific compute requirements
  • Data locality
  • Provider pricing fluctuations
  • Deadline constraints

  • Case Study: Whole Genome Sequencing


    A 30x WGS pipeline optimized from $45 to $28 per sample through intelligent provider selection.


    Before Optimization


    | Stage | Provider | Cost |
    |-------|----------|------|
    | Alignment | AWS | $15 |
    | Variant Calling | AWS | $20 |
    | Annotation | AWS | $10 |
    | **Total** | | **$45** |

    After Agent Optimization


    | Stage | Provider | Cost |
    |-------|----------|------|
    | Alignment | DNAnexus | $10 |
    | Variant Calling | Seven Bridges | $12 |
    | Annotation | AWS | $6 |
    | **Total** | | **$28** |

    Key Learnings


  • Different stages have different optimal providers
  • Data transfer costs must be factored in
  • Spot instances can provide additional savings
  • Agent learning improves over time
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