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### Borzoi Prediction (`borzoi-prediction`) Predicts regulatory track activity for one or more DNA sequences using a single Borzoi replicate. Each sequence may be supplied as an exact 524,288 bp model window, or as a longer source sequence paired with a sequence-relative target range that the tool uses to extract the fixed model window. For every input, the tool returns a per-bin activity matrix together with the source-sequence coordinates of the model input window and the output-bin span, so predictions can be mapped back onto the original sequence. #### API Reference

Source DNA sequence(s) for Borzoi inference. Each item is a sequence with an optional `target_range`, and a bare string is accepted. Without a `target_range` the sequence must already be the model context length. With one, the source must be long enough to extract a full window (no padding). DNA sequence -- an exact model-context window, or a longer source sequence paired with `target_range`. Optional sequence-relative span the tool must keep inside the model output bins. Windowing is all-or-nothing across a call: set `target_range` on every window or on none (see :func:`windows_target_ranges`).

Source Track indices to extract from model output. Species model to use. Available options: `human`, `mouse` Replicate ID to run. Available options: `0`, `1`, `2`, `3` Whether to average selected tracks. Number of sequences to process in each GPU batch. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device used for inference (inherited). Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.

Source Per-sequence prediction results. Input DNA sequence that was scored. Length of the input sequence. Prediction matrix with shape `[num_tracks, 6144]`. Start coordinate of the Borzoi input window in the source sequence. End coordinate of the Borzoi input window in the source sequence. Source-sequence coordinate of the first Borzoi output bin. Source-sequence coordinate immediately after the last Borzoi output bin. Base pairs represented by each output bin. Target start coordinate supplied for this sequence. Target end coordinate supplied for this sequence. Track indices used for prediction. Species used for prediction (`"human"` or `"mouse"`). Borzoi replicate used (`"0"` through `"3"`). Whether requested tracks were averaged.

#### Applications This tool is appropriate for high-throughput screening and iterative sequence design, where a single forward pass per sequence keeps the analysis fast. Representative applications include predicting RNA-seq, CAGE, and chromatin-accessibility profiles for a locus of interest, comparing reference and alternate alleles to estimate the regulatory effect of a noncoding variant, and ranking candidate regulatory sequences inside an optimization loop. The single-replicate setting is well suited to the inner iterations of a design campaign before a final ensemble assessment. #### Usage Tips * **Exact-window inputs must be exactly 524,288 bp.** When no target range is supplied, the provided sequence is treated as the literal model input and is rejected unless it matches the model context length. A longer genomic region should instead be paired with a target range so the tool can extract the fixed window. * **A target range places a region of interest inside the output bins.** Extraction is aligned to the start of the requested range rather than centered, and the window shifts left near the right edge of the source sequence so the full range remains covered. The returned context and output coordinates report where the model window landed in source coordinates. * **The region of interest is most informative near the center of the input window.** Predictions degrade toward the edges of the 524,288 bp context, so a target gene or variant is best positioned close to the midpoint of the supplied window. * **The `species` setting selects the checkpoint family.** A value of `"human"` loads the FlashAttention Flashzoi checkpoints and requires a CUDA device, while `"mouse"` loads the standard Borzoi checkpoints. The two heads predict different track panels, so the species must match the organism of the input sequence. * **`output_tracks` selects which assays are returned.** Track indices address the full Borzoi output panel (7611 human, 2608 mouse). Selecting a small set of relevant tracks is appropriate when only specific assays inform the analysis. * **`avg_output_tracks=True` collapses the selected tracks into a single composite signal.** This default is appropriate when a single objective is needed, for example when combining related assays into one optimization score. A value of `False` returns one row per requested track when per-assay resolution is required.

### Borzoi Ensemble (`borzoi-ensemble`) Predicts regulatory track activity using all four Borzoi replicates and returns the per-replicate predictions stacked together for each input sequence. The four replicates are evaluated in sequence and share the input handling, coordinate reporting, and track-selection behavior of the single-replicate tool. The spread of predictions across replicates provides a measure of model confidence at each bin. #### API Reference

Source Track indices to extract from model output. Species model to use. Available options: `human`, `mouse` Whether to average selected tracks. Number of sequences to process in each GPU batch. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device used for inference. Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.

Source Per-sequence ensemble prediction results. Input DNA sequence that was scored. Length of the input sequence. Stacked predictions with shape `[4, num_tracks, 6144]` for replicates 0-3. Start coordinate of the Borzoi input window in the source sequence. End coordinate of the Borzoi input window in the source sequence. Source-sequence coordinate of the first Borzoi output bin. Source-sequence coordinate immediately after the last Borzoi output bin. Base pairs represented by each output bin. Target start coordinate supplied for this sequence. Target end coordinate supplied for this sequence. Track indices used for prediction. Species used for prediction (`"human"` or `"mouse"`). Whether requested tracks were averaged. Number of replicates returned (always 4).

#### Applications This tool is appropriate for final assessments and for any analysis that benefits from uncertainty quantification. Computing the dispersion across the four replicate predictions at each bin distinguishes positions where the model is confident from positions where the replicates disagree. Representative applications include reporting confidence intervals on a predicted regulatory profile, filtering candidate variants or designed sequences to those with consistent predicted effects, and producing the headline numbers for a locus after single-replicate screening has narrowed the candidates. #### Usage Tips * **The ensemble runs four full forward passes per sequence.** Inference therefore takes roughly four times as long as a single replicate. The single-replicate tool is appropriate for iteration, and the ensemble is appropriate for the final reportable result. * **Confidence is read from agreement across replicates.** A low spread across the four predictions at a bin indicates a robust signal, while a high spread indicates lower model confidence at that position. The per-replicate predictions are returned in full so any dispersion statistic can be computed downstream. * **Replicate selection is not exposed for the ensemble.** All four replicates are always evaluated. The single-replicate tool is the appropriate choice when only one specific replicate is needed. * **The species, track-selection, and averaging behavior match the single-replicate tool.** The same `species`, `output_tracks`, and `avg_output_tracks` guidance applies, and the ensemble inherits the same input modes and coordinate reporting.