{
  "schema_version": 1,
  "project": "GenomicTouchstone",
  "updated": "2026-10-19",
  "description": "Papers and released model resources for the 63 models and methods in the revised study. Evaluation modes apply to their respective task subsets.",
  "counts": {
    "Genomic LMs": 32,
    "RNA LMs": 6,
    "Protein LMs": 11,
    "Task-specific": 10,
    "Baselines": 4
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      "note": "",
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        "venue": "Bioinformatics",
        "venue_year": "2021",
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        "note": ""
      },
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        "note": ""
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      "specifications": {
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        "input_length": "515",
        "tokenizer": "Overlapping 4-mer",
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        "note": ""
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        "input_length": "≈4,500",
        "tokenizer": "BPE",
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        "note": ""
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      "specifications": {
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        "parameters": "110M",
        "input_length": "≈36,000",
        "tokenizer": "BPE",
        "pretraining_objective": "MLM",
        "pretraining_data": "T2T + 1000G SNPs",
        "input_length_unit": "nt"
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    },
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      "id": "nucleotide-transformer-500m-human-ref",
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      "aliases": [
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      "note": "",
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        "kind": "weights"
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        "note": "NT and NTv2 variants share this reference paper; this date does not identify an individual checkpoint release."
      },
      "specifications": {
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        "parameters": "485M",
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    },
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      "id": "nucleotide-transformer-500m-1000g",
      "name": "NT-500M-1000G",
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        "note": "NT and NTv2 variants share this reference paper; this date does not identify an individual checkpoint release."
      },
      "specifications": {
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        "parameters": "485M",
        "input_length": "5,994",
        "tokenizer": "6-mer",
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        "note": "NT and NTv2 variants share this reference paper; this date does not identify an individual checkpoint release."
      },
      "specifications": {
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        "note": "NT and NTv2 variants share this reference paper; this date does not identify an individual checkpoint release."
      },
      "specifications": {
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    },
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        "first_public_date": "2025-02-21",
        "first_public_url": "https://doi.org/10.1101/2025.02.18.638918",
        "first_public_platform": "bioRxiv",
        "venue": "Nature",
        "venue_year": "2026",
        "venue_url": "https://doi.org/10.1038/s41586-026-10176-5",
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    },
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      "evaluation": "LP",
      "leaderboard_id": null,
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      "note": "Evaluated by linear probing; outside the 43-model main fine-tuning leaderboard.",
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        "title": "Genome modelling and design across all domains of life with Evo 2"
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        "label": "Weights",
        "kind": "weights"
      },
      "source_url": "https://huggingface.co/arcinstitute/evo2_40b",
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        "first_public_date": "2025-02-21",
        "first_public_url": "https://doi.org/10.1101/2025.02.18.638918",
        "first_public_platform": "bioRxiv",
        "venue": "Nature",
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        "pretraining_data": "OpenGenome2",
        "input_length_unit": "nt"
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    },
    {
      "id": "rnafm",
      "name": "RNAFM",
      "category": "RNA LMs",
      "evaluation": "FT",
      "leaderboard_id": "rnafm",
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        "RNAFM"
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      "note": "",
      "paper": {
        "url": "https://arxiv.org/abs/2204.00300",
        "label": "Paper",
        "title": "Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions"
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      "download": {
        "url": "https://huggingface.co/cuhkaih/rnafm",
        "label": "Weights",
        "kind": "weights"
      },
      "source_url": "https://huggingface.co/cuhkaih/rnafm",
      "publication": {
        "first_public_date": "2022-04-01",
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        "first_public_platform": "arXiv",
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        "venue_year": "2022",
        "venue_url": "https://arxiv.org/abs/2204.00300",
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    },
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      "id": "rnaernie",
      "name": "RNAErnie",
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      "leaderboard_id": "rnaernie",
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      "note": "Original RNAErnie release and download instructions; the newer RNAErnie2 model is a separate release.",
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      "download": {
        "url": "https://github.com/CatIIIIIIII/RNAErnie#3-download-pre-trained-models",
        "label": "Weight downloads",
        "kind": "weights"
      },
      "source_url": "https://github.com/CatIIIIIIII/RNAErnie",
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      "note": "Model archive includes multiple variants; use the vertebrate-pretrained model described in the paper.",
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        "tokenizer": "Char",
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    },
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      "leaderboard_id": "rinalmo",
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      "note": "Use rinalmo_giga_pretrained.pt for the 650M model.",
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        "label": "Paper",
        "title": "Rinalmo: General-purpose rna language models can generalize well on structure prediction tasks"
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        "kind": "weights"
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        "first_public_platform": "arXiv",
        "venue": "Nature Communications",
        "venue_year": "2025",
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        "pretraining_objective": "MLM",
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    },
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      "id": "orthrus-4track",
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        "kind": "weights"
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    },
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      "leaderboard_id": "rnabert",
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        "url": "https://doi.org/10.1093/nargab/lqac012",
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        "kind": "weights"
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        "url": "https://doi.org/10.1073/pnas.2016239118",
        "label": "Paper",
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      "download": {
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        "kind": "weights"
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      "source_url": "https://github.com/facebookresearch/esm",
      "specifications": {
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        "pretraining_objective": "MLM",
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        "pretraining_data": "UR50/D",
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        "url": "https://doi.org/10.64898/2026.06.03.729735",
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        "kind": "weights"
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        "tokenizer": "Char",
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        "url": "https://doi.org/10.64898/2026.06.03.729735",
        "label": "Paper",
        "title": "Language Modeling Materializes a World Model of Protein Biology"
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        "kind": "weights"
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        "url": "https://doi.org/10.64898/2026.06.03.729735",
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        "kind": "weights"
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        "pretraining_objective": "MLM",
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    },
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      "id": "esm-3-small-open",
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      "download": {
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        "label": "Weights",
        "kind": "weights"
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        "pretraining_objective": "MLM",
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    },
    {
      "id": "xtrimopglm-1b",
      "name": "xTrimoPGLM-1B",
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      "leaderboard_id": null,
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      "download": {
        "url": "https://huggingface.co/biomap-research/xtrimopglm-1b-mlm",
        "label": "Weights",
        "kind": "weights"
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      "source_url": "https://huggingface.co/biomap-research/xtrimopglm-1b-mlm",
      "specifications": {
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    },
    {
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      "paper": {
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      "specifications": {
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        "pretraining_objective": "MLM",
        "pretraining_data": "UniRef90 + ColabFoldDB",
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    },
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      "id": "enformer",
      "name": "Enformer",
      "category": "Task-specific",
      "evaluation": "FT",
      "leaderboard_id": "enformer",
      "aliases": [
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      "paper": {
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      "download": {
        "url": "https://huggingface.co/EleutherAI/enformer-official-rough",
        "label": "Weights",
        "kind": "weights"
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        "input_length": "196,608",
        "tokenizer": "One-hot",
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        "input_length_unit": "nt"
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    },
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      "id": "borzoi",
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      "note": "Access requires signing in to Hugging Face and accepting the publisher’s access terms.",
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        "title": "Advancing regulatory variant effect prediction with AlphaGenome"
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        "kind": "weights"
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        "venue": "Nature",
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    },
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        "kind": "weights"
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        "kind": "code"
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        "pretraining_objective": "—",
        "pretraining_data": "—",
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    },
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        "title": "The genetic and biochemical determinants of mRNA degradation rates in mammals"
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        "kind": "weights"
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        "title": "Predicting the translation efficiency of messenger RNA in mammalian cells"
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        "title": "Detection of nonneutral substitution rates on mammalian phylogenies"
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        "kind": "software"
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    },
    {
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      "leaderboard_id": "baseline_cnn",
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        "CNN"
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      "note": "Trained from scratch in this benchmark; no pretrained checkpoint. Paper describes the referenced baseline framework.",
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        "title": "Genomic benchmarks: a collection of datasets for genomic sequence classification"
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        "kind": "code"
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        "first_public_date": "2022-06-10",
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        "venue": "BMC Genomic Data",
        "venue_year": "2023",
        "venue_url": "https://doi.org/10.1186/s12863-023-01123-8",
        "note": "Reference paper for the scratch baseline; not a pretrained-checkpoint release."
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    },
    {
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      "leaderboard_id": "baseline_lstm",
      "aliases": [
        "LSTM"
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      "note": "Trained from scratch in this benchmark; no pretrained checkpoint. Paper describes the LSTM architecture.",
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      "aliases": [
        "NTv2-50M-Random-Init"
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      "note": "Randomly initialized NTv2-50M architecture; shown as NTv2-50M-Random-Init in the leaderboard. No pretrained checkpoint.",
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        "title": "Attention is all you need"
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        "kind": "code"
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        "venue": "NeurIPS",
        "venue_year": "2017",
        "venue_url": "https://arxiv.org/abs/1706.03762",
        "note": "Reference paper for the scratch baseline; not a pretrained-checkpoint release."
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      "specifications": {
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    },
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      "aliases": [
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      "note": "Trained from scratch; no pretrained checkpoint. Defined in the revised study Methods; the linked study preprint is an earlier version.",
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        "url": "https://doi.org/10.1101/2025.06.25.661622",
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        "title": "Genomic Touchstone: Benchmarking Genomic Language Models in the Context of the Central Dogma"
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        "venue": "bioRxiv",
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        "venue_url": "https://doi.org/10.1101/2025.06.25.661622",
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}
