CBBBioc
This package experimentally defines a Bioconductor interface to
Genentech’s
CompBioBench.
CompBioBench is a framework for evaluating agentic solutions to
computational biology questions.
CBBBioc includes a snapshot of the question set (v1qs) available
around October 2026. The package includes tooling to
- locate in cloud storage the input files each question needs
(
cbb_url()),
- stages per-question agent workspaces (
cbb_setup_workspace()) in the
same workspace/
- set up the
prompt.md needed by
compbiobench-runner.
See the package vignette for a worked example.
Installation
# install.packages("remotes")
remotes::install_github("vjcitn/CBBBioc")
Question catalog
v1qs currently holds 100 questions across these domains:
| Domain | Freq | File types used | Example question |
|---|
| Epigenomics | 20 | bigWig, csv, fasta, gz, tar | Consider chr11:124,738,681-124,738,772 (hg38). What is the likely purpose of this region? Choose only one. A. Active enhancer, Liver B. Active enhancer, Brain frontal lobe C. Active enhancer, ESC D. Active transcription, ubiquitous E. TSS poised or flanking, ubiquitous F. Polycomb repression, ubiquitous. Respond with a single letter. |
| Genomics | 20 | bam, fa, fna, gz | Convert all of the following to Ensembl gene IDs. Provide the ID without the Ensembl version (e.g., ENSGXXXXXXXXXXXX not ENSGXXXXXXXXXXXX.XX). Respond with semi-colon separated values, no spaces. Inputs: 1. NM_001276266.2 2. TERB2 3. ENST00000267814 4. chr15:45167214-45187966 (hg38) 5. GeneID:9153 6. NP_922946.1 7. MIM:617658 8. BEN domain containing 5, transcript variant 6. |
| Machine Learning | 7 | pdf, tsv | Run the DNA language model here: https://huggingface.co/kuleshov-group/caduceus-ph_seqlen-131k_d_model-256_n_layer-16 on the 1000 bp long DNA sequence at hg38 chr12:7792299-7793299. Specifically, mask each base one at a time, keeping all others unmasked, and compute the per position base 2 entropy of the predictions. Return the substring corresponding to the longest run with <0.5 entropy. |
| Population Genetics | 12 | gz | Identify the PubMed ID of the study from which this summary statistics in reverse.search.gwas.q1.tsv.gz are derived. Print only the PMID, e.g. 31510655. |
| Single-cell | 21 | bam, gz, h5ad, rds, sizes | differential.composition.q1.1.mtx.gz and differential.composition.q1.2.mtx.gz contain raw scRNA counts matrices derived from retinal samples of two individuals (one per file, each column is a unique barcode that passes QC). differential.composition.q1.genes.txt.gz is the list of common genes. One of the cell types is severely depleted in one of the two individuals. What is this cell type? Return only the name of the depleted cell type from this list: astrocyte, microglial cell, RPE cell, cone cell, rod cell, horizontal cell, bipolar cell, RGC, T cells, macrophage, Schwann cell, Pericyte, B cell, fibroblast, endothelial cell, muller glia cell. |
| Spatial | 2 | gz | You are given a set of single cell transcriptomic data and Visium spatial transcriptomic data (spatial.sim.tar.gz). The single cell counts matrix is found at spatial_q_sc_counts.csv, and accompanying cell type metadata is found at spatial_q_sc_metadata.csv. Spatial count data is found at spatial_q_matrix.mtx.gz, gene counts are found at spatial_q_features.tsv.gz, and are found at spot barcodes spatial_q_barcodes.tsv.gz. Finally scale factors are found at spatial_q_scalefactors_json.json, high and low resolution tissue images are found at spatial_q_tissue_hires_image.png and patial_q_tissue_hires_image.png respectively, and tissue positions are found at spatial_q_tissue_positions.csv. The spot-based data is comprised entirely of cell types found in the single cell data. Can you tell me what cell type or cell types, if any, are biased towards being in closer spatial proximity to spots containing Tumor_Core? If none, please return “None”. Please list these separated by commas in alphabetical order. Expected answer format: Endothelial,Macrophage. |
| Structure | 1 | pdb | Consider the PDB protein in the file protein.shape.q1.pdb. Which of the following uppercase letters does it resemble most closely: B, D, F, H, J, L, N, P, R, T, V, X, Z? Consider all possible projections and pick the best fit. |
| Transcriptomics | 17 | bam, fq, gz, tsv, txt | I have a bulk human RNA-seq fastq file cryptic.exon.q1.fq.gz. There is exactly one highly expressed coding gene that has a cryptic exon in it formed by two novel junctions. Report the HGNC gene symbol (uppercase) for that gene. |
73 of the 100 questions have one or more associated input files,
retrievable via cbb_url() and stageable with cbb_setup_workspace().
Browse the full catalog
Browse all questions (interactive
table)
(the site at https://vjcitn.github.io/CBBBioc/ is built with
pkgdown from this README, the vignettes,
and the function reference; regenerate it with
pkgdown::build_site().)
A plain-text preview of the first few questions, for quick browsing
here:
| question_id | domain | question_style | question |
|---|
| ensembl-grab-q1 | Genomics | Retrieval | Convert all of the following to Ensembl gene IDs. Provide the ID without the Ensembl ve... |
| bam-infer-read-length-q1 | Genomics | Metadata Recovery | For the given BAM file mt.sorted.bam, infer if it's paired or single ended reads and th... |
| differential-composition-q1 | Single-cell | Synthetic/Augmented Data | differential.composition.q1.1.mtx.gz and differential.composition.q1.2.mtx.gz contain r... |
| genomic-state-q1 | Epigenomics | Routine Analysis | Consider chr11:124,738,681-124,738,772 (hg38). What is the likely purpose of this regio... |
| cryptic-exon-q1 | Transcriptomics | Synthetic/Augmented Data | I have a bulk human RNA-seq fastq file cryptic.exon.q1.fq.gz. There is exactly one high... |
| read-paper-download-file-parse-q1 | Epigenomics | Retrieval | Take a look at the pdf here: https://www.biorxiv.org/content/10.1101/2023.10.04.560808v... |
| huggingface-entropy-q1 | Machine Learning | Tooling | Run the DNA language model here: https://huggingface.co/kuleshov-group/caduceus-ph_seql... |
| ml-model-track-overlap-q1 | Epigenomics | Retrieval | Borzoi (Linder et al., doi:10.1038/s41588-024-02053-6) and Sei (Chen et al., doi:10.103... |
| compute-gc-content-interval-q1 | Genomics | Routine Analysis | Using the GRCh38.p13 human reference genome, retrieve the reference DNA sequence for ch... |
Production of README.md:
rmarkdown::render("README.Rmd", output_format = "github_document", output_file = "README.md", quiet = TRUE)
