task-mapping.md

BEND 2.0 native HF protocol map

Eight scientific endpoints are preserved. Seven downstream endpoints have separate embeddings and native-output subleaderboards so a shared checkpoint never collapses two readouts into one score. The sequence-prior endpoint is independent of downstream readout and is published once per checkpoint.

Scientific task	Native task ID	Unit	Better
Prior loss	prior_loss_bits_per_base	bits/base	Lower
Splicing effects	opensplice_embeddings_spearman_rho	ρ	Higher
Splicing effects	opensplice_native_outputs_spearman_rho	ρ	Higher
Cell contrast	context_embeddings_spearman_rho	ρ	Higher
Cell contrast	context_native_outputs_spearman_rho	ρ	Higher
Tissue identification	tissue_identification_embeddings_accuracy_percent	%	Higher
Tissue identification	tissue_identification_native_outputs_accuracy_percent	%	Higher
Background interaction	background_embeddings_spearman_rho	ρ	Higher
Background interaction	background_native_outputs_spearman_rho	ρ	Higher
Single-edit rank	repair_percentile_embeddings_residual_midrank_percentile	percentile	Lower
Single-edit rank	repair_percentile_native_outputs_residual_midrank_percentile	percentile	Lower
Cell selectivity	cell_selective_embeddings_utility_log2	log₂ utility	Higher
Cell selectivity	cell_selective_native_outputs_utility_log2	log₂ utility	Higher
Multi-cell gain	atlas_profile_embeddings_gain_over_random_log2	log₂ gain	Higher
Multi-cell gain	atlas_profile_native_outputs_gain_over_random_log2	log₂ gain	Higher

Prior loss

Predict the hidden central base of 512 human reference-genome windows. Lower loss means better calibrated conditional probabilities.

Metric: Hidden-base conditional log loss, in bits/base. 512 windows of 1,200 bp from separate 1-Mb genomic blocks; central base hidden.

• Interface-sensitive: NTv3 uses 12% additional masking and averages the individual losses of two queries; GENERATORv2 uses temperature-scaled probabilities.
• Only rows with a comparable nucleotide-probability interface report this task. Missing prior scores are omitted from the combined score, never imputed.

Splicing effects

Predict mutation-induced changes in alternative splicing; correlate within each held-out gene, then average genes equally.

Metric: Macro-gene Spearman correlation, in ρ. 590,104 interventions in 466 genes; five folds hold out complete genes for fitted readouts.

• Native splice heads retain historical supervision; fitted readouts are trained on the benchmark’s source folds.
• OpenSplice is the full gene-held-out endpoint, not a cross-assay transfer score.

Cell contrast

Predict the HepG2-minus-K562 difference in a mutation’s reporter effect, then correlate within regulatory elements and average elements equally.

Metric: Macro-element Spearman correlation, in ρ. 170,292 substitutions across 128 elements; complete elements and related allelic backgrounds remain together during fitting and evaluation.

• The experimental reference has physical counts for 127 of 128 elements and uses repeat subsets; model values use the pooled posterior effects.

Tissue identification

Match a predicted expression profile across held-out genes to the correct measured tissue among 51 candidates.

Metric: Top-1 tissue identification accuracy, in %. 5,782 held-out genes and 51 represented tissues; 12,146 training and 1,350 validation genes.

• This holds out genes within represented tissues; it does not test transfer to unseen tissues.
• The 99.0% experimental reference averages both donor-half directions; no one-direction interval is transferred to it.

Background interaction

Predict how the same mutation’s effect differs across two parental genetic backgrounds, isolating background dependence.

Metric: Macro-element Spearman correlation, in ρ. 68,026 paired mutations in 114 elements; paired backgrounds remain together.

• The current experimental reference is HepG2–K562 transfer (0.421), not the superseded replicate-subset background value.
• Transfer across cells is an experimental comparison, not a noise ceiling.

Single-edit rank

Choose an alternative single-base edit that matches the effect of a reference edit. Score the first nomination by its measured residual’s rank among eligible candidates.

Metric: Mean measured residual midrank percentile, in percentile. 331 response targets in 110 elements; 578–597 eligible substitutions per element; direct reversion is excluded.

• All candidate measurements are withheld until evaluation; predictions rank proximity to the model’s own predicted reference-edit effect.

Cell selectivity

Select one substitution or no edit to increase activity in the target cell while penalizing the absolute effect in the other cell: U = target − |other|.

Metric: Mean cell-selective utility, in log₂ utility. 167,958 substitutions, 280 parental backgrounds and 127 loci; both cells serve as targets; loci receive equal weight.

• Native outputs use held-locus, nonnegative per-cell calibration slopes; they are not zero-shot in this task.
• Model utilities use all five RNA repeats; experimental selection uses reciprocal disjoint repeat subsets.
• Original confirmatory tests concern within-model controls; a sorted point estimate is not a significance claim.

Multi-cell gain

Select ten enhancers for each multi-cell ON/OFF program. Utility rewards the weakest requested activity and penalizes the strongest unwanted activity; report gain over intent-matched random selection.

Metric: Mean utility gain over intent-matched random selection, in log₂ gain. 2,411 measured 145-nt candidates, 16 overlapping programs and 12 cell types; select 10 per request. Fit on 5,246 separate source constructs after excluding candidate families.

• The native rows and Enformer fit linear heads to native functional outputs on source constructs.
• The two RNA-repeat markers are descriptive repeat measurements, not confidence intervals or independent program replications.
• Positive gain does not imply positive absolute utility; AlphaGenome fitted has 0.833 gain but −0.096 absolute utility.