Tasks Reference

Six DNA-sequence tasks, one shared REST shape: POST /v1/tasks/{task}/predict with body {sequence, sequence_name, model?, options?}, returning a {data, meta} envelope. On MCP, the equivalent is predict_<task>(sequence_ref, ...).

Omit model to get the task's default — the API resolves it server-side (model is optional: "If omitted, the task's default model is used."). Default model IDs are deliberately not listed here: defaults change and old IDs are retired, so a hardcoded ID is a future hard failure. Discover them at call time with GET /v1/tasks/{task}/models (REST) or list_models(task) (MCP), and never invent one.

Source of truth for bounds: the live OpenAPI at https://api.genomicintelligence.ai/v1/openapi.json.

Task	Mode	Length	Default architecture
promoter	sync	1–500,000 bp	sliding-window; human/mammalian
splice	sync	1–500,000 bp	BigBird (long-context)
enhancer	sync	1–500,000 bp	DeepSTARR — Drosophila
chromatin	sync	1–500,000 bp	DeepSEA — hundreds of tracks
expression	sync	exactly 9,198 bp	log(TPM+1)
annotation	async	1–500,000 bp	de-novo transcripts

promoter

Promoter regions over a sliding window. data.summary reports promoter_windows / total_windows; data.regions lists windows with name, start, end, score, strand. Non-human models exist (Drosophila, yeast, Arabidopsis) — pass model. Default targets human/mammalian sequence.

splice

Splice donor and acceptor sites. data.sites lists each with name, start, end, site_type (donor/acceptor), score, strand. The default is a BigBird long-context model.

enhancer

Enhancer activity. The default (DeepSTARR) reports developmental and housekeeping scores — summary.dev_score_max / summary.hk_score_max per window. DeepSTARR is a Drosophila model — match the species to the model.

chromatin

Chromatin state across a large panel of tracks (histone marks, DNase, ATAC, TF binding). The default (DeepSEA) covers hundreds of features. summary.total_annotations is the headline; the full per-track matrix is in data.

expression

Expression as log(TPM+1) from a fixed window. Two enforced requirements:

1. Exactly 9,198 bp — a window centred on the TSS (4,599 upstream + TSS + 4,598 downstream). Other lengths are rejected. Build it with the acquisition helpers (fetch_gene_for_expression on MCP), not by hand — see sequence-acquisition.md.
2. options.description — a cell-type / assay string (e.g. "K562 cells"). Required.

Result: data.prediction.expression_log_tpm (and expression_tpm).

annotation

De-novo gene / transcript structure — transcript intervals and strand, no reference annotation. Async only: submit with Prefer: respond-async → job_id; poll GET /v1/tasks/jobs/{job_id} until it returns 200. data.transcripts lists each transcript with name, start, end, strand, score, plus structure fields (length, tss_position, polya_position, transcript_type, exons, introns, cds).

Composite: find genes + predict expression

"What genes are in this region, and how are they expressed?" — MCP find_genes_and_predict_expression(sequence_ref, description) takes a handle, not a region (acquire one with fetch_region first); description is required. It finds genes in the sequence and returns an expression prediction per gene. Over REST, discover genes then loop expression per gene (build each TSS-centred 9,198 bp window first).