> For the complete documentation index, see [llms.txt](https://help.bioinsight.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.bioinsight.illumina.com/annotation/data-sources/splice-ai.md). # SpliceAI ### Overview SpliceAI is an AI annotation model developed by the BioInsight Artificial Intelligence Lab to predict splice effects. [SpliceAI2](/annotation/data-sources/splice-ai-2.md) was released for improved splicing predictions. The model evaluates probability that a variant disrupts or creates acceptor and donor splice sites. Higher delta scores suggest a stronger predicted effect on RNA splicing. Splicing defects are a major contributor to human disease, particularly in rare disease and oncology, where many pathogenic variants occur in non-coding regions. For more details, refer to: {% hint style="info" %} **Publication** Jaganathan, et al. Predicting splicing from primary sequence with deep learning. *Cell* (2019). {% endhint %} {% hint style="warning" %} **Professional data source** This data source requires a Professional license. Contact `annotation_support@illumina.com` to request access. {% endhint %} ### Parsing #### VCF File ```scss ##fileformat=VCFv4.0 ##assembly=GRCh37/hg19 ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= #CHROM POS ID REF ALT QUAL FILTER INFO 10 92946 . C T . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0000;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-26;DP_AL=-10;DP_DG=3;DP_DL=35 10 92946 . C G . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0008;DS_AL=0.0000;DS_DG=0.0003;DS_DL=0.0000;DP_AG=34;DP_AL=-27;DP_DG=35;DP_DL=1 10 92946 . C A . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0004;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=-10;DP_AL=-48;DP_DG=35;DP_DL=-21 10 92947 . A C . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-49;DP_AL=-11;DP_DG=0;DP_DL=34 10 92947 . A T . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=-22;DP_DL=34 10 92947 . A G . . SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0006;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=34;DP_DL=32 ``` DRAGEN Annotation extracts these INFO fields: * `DS_AG` - Δ score (acceptor gain) * `DS_AL` - Δ score (acceptor loss) * `DS_DG` - Δ score (donor gain) * `DS_DL` - Δ score (donor loss) * `DP_AG` - Δ position (acceptor gain) relative to the variant position * `DP_AL` - Δ position (acceptor loss) relative to the variant position * `DP_DG` - Δ position (donor gain) relative to the variant position * `DP_DL` - Δ position (donor loss) relative to the variant position These fields report the predicted splice gain or loss and the relative position of the effect. #### Filtering SpliceAI provides entries across the genome. Many low-scoring entries have limited value, especially in intergenic regions. These entries increase storage requirements and slow annotation. DRAGEN Annotation filters out low-confidence entries except within 15 bp of nascent splice sites. In those regions, low-confidence predictions can still help identify potential splice disruption. ### JSON output ```json "spliceAI":[ { "hgnc":"BLCAP", "acceptorGainDistance":-3, "acceptorGainScore":0.3, "donorLossDistance":7, "donorLossScore":0.9 }, { "hgnc":"NNAT", "acceptorGainDistance":-1, "acceptorGainScore":0.2, "donorGainDistance":-2, "donorGainScore":0.3 } ] ```
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place
### Interpreting scores SpliceAI delta scores range from `0` to `1`. The SpliceAI team suggests this interpretation: | Range | Confidence | Pathogenicity | | :-----------: | ---------- | ----------------- | | 0 ≤ x < 0.1 | low | likely benign | | 0.1 ≤ x ≤ 0.5 | medium | likely pathogenic | | x > 0.5 | high | pathogenic | ### Resources * [SpliceAI GitHub](https://github.com/Illumina/spliceAI) * [Download URL](https://basespace.illumina.com/s/5u6ThOblecrh) * Related paper: * Rowlands, et al. Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders. *Scientific Reports* (2021). --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.bioinsight.illumina.com/annotation/data-sources/splice-ai.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.