> For the complete documentation index, see [llms.txt](https://help.connected.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.connected.illumina.com/annotation/data-sources/promoterai.md). # PromoterAI ### Overview PromoterAI is an AI annotation model developed by the Illumina Artificial Intelligence Lab to predict impact of variants in promoter regions. The model evaluates DNA sequence context around a promoter variant and estimates whether the variant is likely to increase, decrease, or have no effect on gene expression. Promoter variants are estimated to account for approximately 6% of genetic factors underlying rare disease, highlighting the importance of analyzing non-coding regulatory regions alongside coding variants. For more details, refer to: {% hint style="info" %} **Publication** Jaganathan, et al. Predicting expression-altering promoter mutations with deep learning. *Science* (2025). {% endhint %} {% hint style="warning" %} **Professional data source** This data source requires a Professional license. Contact `annotation_support@illumina.com` to request access. {% endhint %} ### Parsing #### TSV File ```scss gene_id chrom pos ref alt strand gene transcript_id tss_pos strand_adj_dist_tss score_x score_y score ENSG00000181404 chr9 73364 G A -1 WASHC1 ENST00000642633.1 73864 500 -0.0032 -0.0298 -0.0165 ENSG00000181404 chr9 73364 G C -1 WASHC1 ENST00000642633.1 73864 500 -0.0372 -0.052000000000000005 -0.0446 ENSG00000181404 chr9 73364 G T -1 WASHC1 ENST00000642633.1 73864 500 0.0202 -0.0315 -0.0056500000000000005 ENSG00000181404 chr9 73365 G A -1 WASHC1 ENST00000642633.1 73864 499 -0.015 -0.0643 -0.03965 ENSG00000181404 chr9 73365 G C -1 WASHC1 ENST00000642633.1 73864 499 0.0285 -0.0035 0.0125 ENSG00000181404 chr9 73365 G T -1 WASHC1 ENST00000642633.1 73864 499 -0.0095 -0.0352 -0.022350000000000002 ENSG00000181404 chr9 73366 G A -1 WASHC1 ENST00000642633.1 73864 498 0.0068 -0.0565 -0.02485 ENSG00000181404 chr9 73366 G C -1 WASHC1 ENST00000642633.1 73864 498 0.0596 0.0854 0.07250000000000001 ENSG00000181404 chr9 73366 G T -1 WASHC1 ENST00000642633.1 73864 498 -0.0086 -0.0009 -0.00475 ENSG00000181404 chr9 73367 C A -1 WASHC1 ENST00000642633.1 73864 497 0.0091 -0.0068 0.0011500000000000004 ... ``` From the file, Illumina Connected Annotations extracts these columns: * `gene_id` * `transcript_id` * `strand` * `strand_adj_dist_tss` * `score` ### JSON output ```json "promoterAI": [ { "strand": 1, "distanceFromTss": -292, "geneId": "ENSG00000274391", "transcriptId": "ENST00000618007.5", "score": 0.032 } ] ```
FieldTypeNotes
strandintStrand location of the transcript
distanceFromTssintNumber of nucleotides calculated from TSS of the corresponding transcript
geneIdstringGene ID
transcriptIdstringTranscript ID (Ensembl)
scoredecimalCalculated PromoterAI score
{% hint style="info" %} **Transcript and gene IDs from PromoterAI** Illumina Connected Annotations uses its own transcript and gene cache data when reporting transcript annotations. These IDs can differ from the IDs reported directly by PromoterAI, which is based on GENCODE Release 39. As a result, transcript IDs and gene IDs can differ between the source data and the final annotation output. {% endhint %} ### Interpreting scores PromoterAI scores range from `-1` to `1`. * A positive score suggests increased expression of the target gene. * A negative score suggests decreased expression of the target gene. * Scores between `-0.05` and `0.05` suggest no meaningful effect on target gene expression. ### Resources * [PromoterAI GitHub](https://github.com/Illumina/PromoterAI) * [GENCODE Release 39](https://www.gencodegenes.org/human/release_39.html) * [Ensembl release 105](https://may2021.archive.ensembl.org/index.html) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://help.connected.illumina.com/annotation/data-sources/promoterai.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.