> 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/v3.27/data-sources/dann.md).

# DANN

### Overview

DANN uses the same feature set and training data as CADD (Combined Annotation-Dependent Depletion) to train a deep neural network (DNN). CADD is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. DANN improves on CADD (which uses Support Vector Machines (SVMs)) by capturing non-linear relationships by using a deep neural network instead of SVMs. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology.

{% hint style="info" %}
**Publication**

Quang, Daniel, Yifei Chen, and Xiaohui Xie. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. *Bioinformatics* **31.5** 761-763 (2015). <https://doi.org/10.1093/bioinformatics/btu703>
{% endhint %}

### TSV File

#### Example

```tsv
chr     grch37_pos  ref     alt     DANN
1       10001       T       A       0.16461391399220135
1       10001       T       C       0.4396994049749739
1       10001       T       G       0.38108629377072734
1       10002       A       C       0.36182020272810128
1       10002       A       G       0.44413258111779291
1       10002       A       T       0.16812846819989813
```

#### Parsing

From the CSV file, we are interested in all columns:

* `chr`
* `grch37_pos`
* `ref`
* `alt`
* `DANN`

### GRCh38 liftover

The data is not available for GRCh38 on DANN website. We performed a liftover from GRCh37 to GRCh38 using crossmap.

### Known Issues

None

### Download URL

<https://cbcl.ics.uci.edu/public_data/DANN/>

### JSON Output

```json
"dannScore": 0.27
```

| Field     | Type  | Notes          |
| --------- | ----- | -------------- |
| dannScore | float | Range: 0 - 1.0 |


---

# 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/v3.27/data-sources/dann.md?ask=<question>&goal=<endgoal>
```

`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.