> For the complete documentation index, see [llms.txt](https://docs.finngen.fi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.finngen.fi/working-in-the-sandbox/which-tools-are-available/exome-browser.md).
# Exome browser
Exome browser is a graphical user interface to examine the exome data available in FinnGen sandbox.
#### Use cases for the Exome Browser
* Identify carriers for rare coding variants not available in imputed/chip data
* Explore coding variants available for genes in the exome data
#### Exome data
Exome sequencing data processed from 45,375 samples across four sequencing batches, of which 43,289 were successfully mapped to existing FinnGen IDs.
The data consists of four datasets:
| Name | Samples available after QC | Description |
| -------------------------------------------------------- | -------------------------- | --------------------------------------------------------------------------- |
| gnomAD v4 Finns subset | 23,419 | GnomAD Finns subset of individuals already in FinnGen |
| Blended Genome Exome scizophrenia, bipolar, controls | 12,223 | Broad Institute sequenced Bipolar & schizophrenia and shared control cohort |
| Botnia THL diabetes study | 7,029 | Botnia Diabetes cohort |
| Autosomal dominant polycystic kidney disease WES (ADPKD) | 618 | Autosomal dominant polycystic kidney disease patients |
NOTE: Some cohorts have a large amount of samples with a certain condition, such as the Broad Institute schizophrenia & bipolar & control cohort. Using these samples in e.g. CODEWAS without taking that into account will introduce bias to the analysis.
You can find more information about the exome datasets from the exome data readme, available in sandbox red library: /finngen/library-red/wes\_4batches\_min\_QC/finngen\_R14\_exome\_readme
#### How to launch the Exome Browser
In the sandbox, the Exome Browser can be launched from the Applications menu. It is under Sandbox->Exome Browser.
#### Querying data
Data can be queried by either a gene name, which will bring up all of the data variants in a gene, or by one or more variants.\
The gene symbol will select all of the variants for which VEP has predicted that the gene of the most severe effect is that gene.\
The variant query will include variants with the correct chromosome, position, reference and alternate alleles.
#### Filtering data
Data can be filtered with multiple different filter types. When searching for a gene, you can filter to either all coding variants, protein-truncating variants, or select your own set of variant consequences.
You can select either all of the call sets or any combination of them.
You can filter the data with a wide variety of quality control filters. The available fields at this point are:
* Allelic balance (AB)
* Read depth (DP)
* Genotype quality (GQ)
* allelic balance p-value (pAB)
* Genotype call (GT)
These fields can be filtered and combined, for example you can filter with genotype quality (GQ) and read depth (DP) at the same time. You can also group filters, e.g. to have different quality control thresholds for genotype calls.
You can filter the variants with allele frequency (AF) or allele counts (AC). Note that a variant is included if ANY dataset passes the filter. For example, if the AF filter is variants lower than 0.01, and dataset 1 has AF 0.05, and dataset 2 has AF 0.005, the variant is included.
Then, press the "Run Query" button to fetch the exome data.
#### Interpreting results
Once the results are fully fetched, you will get three types of data from the results:
1. Dataset-level summaries, detailing the amount of variants and variant carriers per dataset
2. Variant-level summaries, giving information per variant, such as callsets in which that variant is, allele frequency in that callset, allele count, allele numbers, amount of carriers in the callset, and the amount of specific genotypes in that callset. The data will also include whether there are variants in LD in the imputed data with that variant (called proxies in the browser), and gnomAD v4 population allele frequencies if they are available.
3. Sample-level data, which is essentially the sample information in the dataset.\
The sample data can be exported from the browser in TSV format by pressing the buttons "Export carriers (TSV)" or "Export cases (TSV)" at the top-right of the app. This will create a tab-separated values file, which can be used in other tools.
