chjp0632/Agent-eval-Effector-Hunt

Agent Eval: Effector Hunt

While AI scientist agents like Claude Science and Google's AI co-scientist highlight the potential of autonomous research, compact and reproducible datasets for evaluating these agents on real scientific workflows remain scarce.

Agent Eval: Effector Hunt is a genomics benchmark package designed around a real scientific discovery workflow from the Science paper Chen et al. 2017. It asks an AI agent, a computational biologist, or a hybrid human-agent workflow to analyze anonymized paired-end sequencing reads against an anonymized fungal reference genome and recover a biologically meaningful effector signal.

Task

Given two anonymized paired-end readsets and an anonymized reference genome with annotations, identify the key effector-region difference between the samples.

An agent should be able to:

1. Inspect the provided reference genome and annotation files.
2. Align or otherwise compare the anonymized reads to the reference.
3. Detect the major sample-specific genomic signal.
4. Prioritize candidate effector genes or regions.
5. Produce a concise evidence-backed report explaining the finding.

The dataset card includes an evaluator-facing rubric below so that users can score agent outputs without requesting a separate answer by email. For blind agent evaluation, do not include the dataset card or rubric in the agent prompt.

Dataset Contents

data/raw/fastq_anonymized/
  sample_1_R1.fastq.gz
  sample_1_R2.fastq.gz
  sample_2_R1.fastq.gz
  sample_2_R2.fastq.gz

reference/reference_anonymized/
  reference_genome.fna.gz
  reference_annotations.gff.gz
  reference_annotations.gtf.gz
  reference_cds.fna.gz
  reference_protein.faa.gz
  checksums.md5

data_manifest.md

The sample names, read names, contig identifiers, gene identifiers, transcript identifiers, and protein identifiers have been replaced with neutral names. Read sequences, quality strings, genome sequences, protein sequences, annotation coordinates, strands, feature types, and phases are preserved.

Suggested Evaluation Setup

For a clean agent evaluation, give the agent access to this dataset and a standard command-line bioinformatics environment, but do not provide the source paper, original sample names, original reference identifiers, or the private answer key.

Recommended evaluation criteria:

• Correctly identifies the major genomic difference between sample_1 and sample_2
• Localizes the relevant region in the anonymized reference
• Connects the region to plausible effector biology using the provided annotations
• Provides reproducible commands or analysis steps
• Separates evidence from speculation
• Reports uncertainty and checks alternative explanations

If internet access is enabled during evaluation, benchmark integrity may be weaker because an agent could search for external provenance clues instead of solving the task from the anonymized data.

Rubric

This rubric is intended for evaluators, not for the agent being tested. A strong submission should recover the anonymized target locus and explain the sample-specific evidence for it.

Expected Finding

The key finding is a loss-of-heterozygosity signal in sample_2 relative to sample_1 that affects an effector-region candidate on:

• Target contig: ref_contig_000132
• Target interval: approximately 2553060-2553458
• Strand: +
• Target gene: gene_027287
• Target transcript: transcript_028378
• Target protein: protein_027771
• Public alias: locus_X

The best answers should identify this region as the central candidate, describe the sample-specific haplotype/heterozygosity pattern, and avoid treating the signal as a simple loss of read depth unless their analysis supports that claim.

Intended Use

This dataset is intended for research and evaluation of AI agents in scientific workflows. It is especially useful for testing whether an agent can:

• plan a multi-step genomics analysis
• choose appropriate command-line tools
• recover from failed or uninformative analyses
• synthesize sequence-level evidence into a scientific claim
• write a report that a scientist can audit

It is not intended as a clinical, diagnostic, agricultural decision-making, or production pathogen-surveillance dataset.

Public-Safe Anonymization

The uploaded package is designed to be public-facing. Private mappings from neutral identifiers back to source identifiers are not included. Original raw downloads, source metadata, paper-derived notes, and answer-key files should be kept outside the Hugging Face dataset repository.

Citation And Provenance

This benchmark package is derived from publicly available genomics data associated with a published plant-pathogen study in Science. The public upload intentionally uses anonymized names so that the dataset can function as an evaluation task rather than a paper-reading exercise.

If you use this dataset in a paper, report, or benchmark suite, cite this dataset card and describe whether agents were allowed to use internet search, external biological databases, or only the files provided here.