IWC-bench: A Benchmark for Evaluating Bioinformatics Agents Based on Galaxy Workflows

IWC-bench is a benchmark for evaluating bioinformatics agents derived from peer-reviewed Galaxy workflows in the IWC community, aiming to provide a standardized testing framework for AI applications in bioinformatics. It addresses the problem that existing AI evaluation benchmarks are too simplified and cannot truly reflect the complexity of bioinformatics tasks. By using validated high-quality workflows to construct evaluation tasks, it ensures the authenticity and reproducibility of the evaluation.

Bioinformatics is a data-intensive discipline. High-throughput sequencing technology has led to exponential growth in data scale and complexity, making traditional methods difficult to meet the demands. AI (especially LLMs) offers new possibilities, but evaluating AI capabilities faces challenges—bioinformatics tasks involve multi-step complex workflows, requiring professional knowledge and parameter settings, while existing benchmarks are too simplified.

IWC-bench originated from the idea of leveraging peer-reviewed Galaxy workflows in the IWC community. IWC maintains workflows on the Galaxy platform, which is an open web computing platform with features of ease of use, reproducibility, scalability, and community-driven nature. Its workflows represent best practices in bioinformatics. IWC-bench converts these workflows into AI evaluation benchmarks, providing a standardized and reproducible framework.

IWC-bench is designed following five core principles:

1. Authenticity: Based on real bioinformatics analysis scenarios, not artificially simplified problems;
2. Diversity: Covers multiple subfields such as genomics, transcriptomics, proteomics, etc.;
3. Scalability: The framework allows easy addition of new evaluation tasks and workflows;
4. Reproducibility: All tasks have clear inputs, expected outputs, and evaluation criteria;
5. Progressive Difficulty: Tasks are graded by difficulty, from basic data processing to complex multi-step analysis.

Evaluation Task Types:

• Data Preprocessing: Quality control, sequence trimming, format conversion, etc., testing AI's understanding of biological data formats and tools;
• Sequence Analysis: Sequence alignment, variant detection, genome assembly, etc., requiring understanding of algorithm principles and parameter tuning;
• Quantitative Analysis: Gene expression quantification, differential expression analysis, etc., involving statistical knowledge and specialized tools;
• Workflow Orchestration: Combining multiple steps into a complete workflow, testing overall understanding of the process;
• Result Interpretation: Explaining the biological significance of results, testing the ability to integrate computational and biological knowledge.

Evaluation Metrics:

• Correctness: Whether the generated workflow produces correct results;
• Efficiency: Running time and resource usage;
• Robustness: Ability to handle imperfect inputs and adapt to different data types;
• Interpretability: Explaining analysis decisions and providing biological context;
• Tool Selection: Whether appropriate tools are chosen and parameters are set reasonably.

IWC-bench poses unique challenges to AI:

1. Domain Knowledge: Requires deep biological background (genome structure, molecular biology principles, etc.);
2. Tool Proficiency: Bioinformatics has thousands of professional tools, each with specific uses and parameter requirements;
3. Workflow Understanding: Complex analysis involves multi-step coordination, requiring understanding of step dependencies;
4. Data Sensitivity: Biological data has special formats and quality characteristics, requiring careful handling;
5. Result Interpretation: The ultimate goal is to gain biological insights, not just computational results.

Application Value:

• AI R&D: Provides evaluation standards for bioinformatics-specific AI, identifying strengths and weaknesses;
• Model Comparison: Fairly compares the performance of different AI models;
• Capability Diagnosis: Locates AI capability gaps through fine-grained tasks;
• Educational Training: Serves as training data to learn best practices;
• Tool Integration: Promotes deep integration of AI with existing tool platforms.

Comparative Advantages: Compared with general benchmarks (such as MMLU, HumanEval), IWC-bench has characteristics like domain expertise (designed with participation of domain experts), practice orientation (based on real workflows), dynamic updates (expanding with IWC community workflows), and community validation (underlying workflows are peer-reviewed).

Future Directions:

• Integrate more IWC community workflows to cover a wider range of bioinformatics subfields;
• Expand multi-modal evaluation (image analysis, protein structure, etc.);
• Explore real-time biological data as evaluation inputs;
• Design complex tasks completed by multi-AI collaboration;
• Evaluate AI's ability to visualize generated results.

Conclusion: IWC-bench represents a new direction for AI evaluation benchmarks—using validated professional workflows to improve evaluation authenticity and provide guidance for AI applications in professional fields. For bioinformatics researchers, it is a standard to evaluate the reliability of AI tools; for AI researchers, it reveals the current AI's ability and limitations in handling complex scientific workflows. As AI applications in scientific research expand, such domain-specific benchmarks will become more important.