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marin-dna/zoonomia-v1-v3_ncrna_exon

bolinas-dna/zoonomia-v1-v3_ncrna_exon Per-anchor region-type partition of the cross-mammal training set bolinas-dna/zoonomia-v1-v1, restricted to anchors labelled ncrna_exon by the snakemake/zoonomia_projection_dataset pipeline (commit 2ab868a2f1d4). Region label (ncrna_exon) Non-coding-RNA exon — every Ensembl r115 exon that is not part of a protein-coding transcript (get_exons(ann) − get_ensembl_protein_coding_exons(ann)). No biotype or quality filter, so this… See the full description on the dataset page: https://huggingface.co/datasets/marin-dna/zoonomia-v1-v3_ncrna_exon.

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bolinas-dna/zoonomia-v1-v3_ncrna_exon

Per-anchor region-type partition of the cross-mammal training set `bolinas-dna/zoonomia-v1-v1`, restricted to anchors labelled ncrna_exon by the `snakemake/zoonomia_projection_dataset` pipeline (commit `2ab868a2f1d4`).

Region label (ncrna_exon)

Non-coding-RNA exon — every Ensembl r115 exon that is not part of a protein-coding transcript (get_exons(ann) − get_ensembl_protein_coding_exons(ann)). No biotype or quality filter, so this label is broader than the val_ncrna validation recipe (which restricts to canonical transcripts of seven functional ncRNA biotypes).

Partition

The six v3 subsets partition the conservation-filtered human anchor set (every anchor is assigned exactly one label) by priority-walk:

cds > utr3 > ncrna_exon > tss_region_and_utr5 > ccre_non_promoter > background

This subset contains 93,064 of 1,136,854 human anchors (8.19% of v1), expanding to 15,277,064 training samples after halLiftover projection to up to 108 Zoonomia mammals and reverse-complement augmentation (same shape as `bolinas-dna/zoonomia-v1-v1`, just filtered to this region label). The total is the exact row count across all 64 JSONL.zst shards — included explicitly because HF's automatic estimate (based on first-shard byte size) is unreliable for sharded datasets.

Five sibling v3 subsets (one per region label):

Schema

Same as `bolinas-dna/zoonomia-v1-v1` — a single train split of JSONL.zst shards at data/train/shard_NNNN.jsonl.zst:

ColumnTypeDescription
query_namestrhuman-window id (win_<chrom>_<NNN> from windows.smk)
speciesstrone of 108 Zoonomia mammals
t_chromstrUCSC chr1-style
t_startint0-based half-open
t_endint0-based half-open; t_end - t_start == 255
t_strandstr+ or -
t_src_sizeinttarget chromosome size
sequencestrexactly 255 bp; strand-aware (already RC'd if t_strand == "-")
augmentationstr+ (original) or - (RC of sequence)

Construction

  1. 1.Build the v1 cross-mammal training set (108-species halLiftover projection of conservation-filtered 255 bp human anchors). See the pipeline README.
  2. 2.Annotate each anchor with one of six region labels (priority shown above; union-of-functional fraction ≥ 0.20 required to escape background). Library: bolinas.zoonomia_projection_dataset.region_labels.
  3. 3.Filter v1 to anchors labelled ncrna_exon via subset_dataset_derived (Polars lazy-filter on query_name).
  4. 4.RC-augment, shuffle (seed=42), shard to 64 JSONL files, zstd-compress, upload via hf upload-large-folder.

Caveats

  • —The six v3 subsets are a partition of v1, not independent probes. Concatenating them reconstructs v1 (modulo the RC augmentation and the shuffle seed). Each anchor appears in exactly one subset.
  • —Broad `ncrna_exon`. ncrna_exon here is the set complement get_exons(ann) − get_ensembl_protein_coding_exons(ann), which is broader than the val_ncrna validation recipe — it includes pseudogene exons, retained-intron exons, and other non-PC Ensembl biotypes. Use val_ncrna if you want functional ncRNA only.
  • —Background is heterogeneous. ~90% have zero functional overlap by the labeler's definitions (true gene deserts or deep introns); ~10% sit just below threshold and are candidates for unannotated regulatory elements or UCEs.

Source code

  • —Pipeline: snakemake/zoonomia_projection_dataset (latest)
  • —Pinned to this dataset's build: commit `2ab868a2f1d4`
  • —Region labeler library: bolinas.zoonomia_projection_dataset.region_labels
  • —Sister cross-mammal datasets: bolinas-dna/zoonomia-v1-v1, bolinas-dna/zoonomia-v1-v2
  • —Sister validation datasets: bolinas-dna/zoonomia-v1-val_*