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  > **A growing benchmark for evaluating LLM agents on complex bioinformatics workflows.**
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  [![Dataset on HF](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench)
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- [![Tasks](https://img.shields.io/badge/Tasks-41-blue)]()
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  [![Based on](https://img.shields.io/badge/Based%20on-BioAgentBench-green)](https://github.com/bioagent-bench/bioagent-bench)
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- Building on [BioAgentBench](https://arxiv.org/abs/2601.21800) (10 tasks), this benchmark adds **41 new tasks** that test LLM agents on increasingly complex, multi-tool bioinformatics pipelines. Tasks span 6 domains and range from simple linear workflows to depth-8 diamond DAGs with 16+ CLI tools.
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  **This benchmark is actively growing** — new tasks are added continuously to cover more domains, increase complexity, and push the boundaries of what AI agents can do in bioinformatics.
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@@ -21,7 +21,7 @@ Building on [BioAgentBench](https://arxiv.org/abs/2601.21800) (10 tasks), this b
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  - **Domain-specific traps**: Tasks include steps where default parameters silently produce wrong results (e.g., Tn5 shift correction in ATAC-seq, Medaka model selection for Nanopore)
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  - **Real public data**: Every task uses published datasets with ground truth generated by validated reference pipelines
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- ## Tasks (41 total)
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  | # | Task ID | Name |
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  |---|---------|------|
@@ -66,6 +66,7 @@ Building on [BioAgentBench](https://arxiv.org/abs/2601.21800) (10 tasks), this b
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  | 49 | `mhc-immunopeptidomics` | MHC Immunopeptidomics: Peptide Identification and Quant |
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  | 50 | `riboseq` | Ribosome Profiling Translation Analysis |
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  | 51 | `neoantigen-prediction` | Neoantigen Prediction: Tumor-Normal Somatic Analysis |
 
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  ## Quick start
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  > **A growing benchmark for evaluating LLM agents on complex bioinformatics workflows.**
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  [![Dataset on HF](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench)
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+ [![Tasks](https://img.shields.io/badge/Tasks-42-blue)]()
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  [![Based on](https://img.shields.io/badge/Based%20on-BioAgentBench-green)](https://github.com/bioagent-bench/bioagent-bench)
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+ Building on [BioAgentBench](https://arxiv.org/abs/2601.21800) (10 tasks), this benchmark adds **42 new tasks** that test LLM agents on increasingly complex, multi-tool bioinformatics pipelines. Tasks span 6 domains and range from simple linear workflows to depth-8 diamond DAGs with 16+ CLI tools.
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  **This benchmark is actively growing** — new tasks are added continuously to cover more domains, increase complexity, and push the boundaries of what AI agents can do in bioinformatics.
16
 
 
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  - **Domain-specific traps**: Tasks include steps where default parameters silently produce wrong results (e.g., Tn5 shift correction in ATAC-seq, Medaka model selection for Nanopore)
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  - **Real public data**: Every task uses published datasets with ground truth generated by validated reference pipelines
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+ ## Tasks (42 total)
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  | # | Task ID | Name |
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  |---|---------|------|
 
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  | 49 | `mhc-immunopeptidomics` | MHC Immunopeptidomics: Peptide Identification and Quant |
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  | 50 | `riboseq` | Ribosome Profiling Translation Analysis |
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  | 51 | `neoantigen-prediction` | Neoantigen Prediction: Tumor-Normal Somatic Analysis |
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+ | 52 | `somatic-germline-dual` | Somatic+Germline Dual Analysis: Hereditary Cancer Varia |
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  ## Quick start
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