Description

This track shows the genomic positions of variants in the AVADA database. AVADA is a database of variants built by a machine learning software that analyzes full text research articles to find the gene mentions in the text that look like they are most relevant for monogenic (non-cancer) genetic diagnosis, finds variant descriptions and uses the genes to map the variants to the genome. For details see the AVADA paper.

As the data is automatically extracted from full-text publications, it includes some false positives. In the original study, out of 200 randomly selected articles, only 99 were considered relevant after manual curation. However, this share is very high compared to the Genomenom track. Ideally, the track is used in combination with variants found in human patients, to find relevant literature, or with Genome Browser tracks of variant databases that curated a single study for each variant, like our tracks for HGMD or LOVD.

Display Conventions and Configuration

Genomic locations of a variants are labeled with the variant description in the original text. This is not a normalized HGVS string, but the original text as the authors of the study described it. The Pubmed ID, gene and transcript for each variant are shown on the variant's details page, as well as the PubMed title, authors, and abstract.

Mouse over the variants to show the gene, variant, first author, year, and title.

The data has been lifted from hg19 to hg38.

Data access

The raw data can be explored interactively with the Table Browser, for download, intersection or correlations with other tracks. To join this track with others based on the chromosome positions, use the Data Integrator.

For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called avada.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/bbi/avada.bb -chrom=chr21 -start=0 -end=100000000 stdout

For automated access, this track like all others, is also available via our API. However, for bulk processing in pipelines, downloading the data and/or using bigBed files as described above is usually faster.

Methods

The AVADA VCF file was reformatted at UCSC to the bigBed format. The program that performs the conversion is available on Github. The paper reference information was added from MEDLINE and is used Courtesy of the U.S. National Library of Medicine, according to its Terms and Conditions.

Credits

Thanks to Gill Bejerano and Johannes Birgmeier for making the data available.

References

Johannes Birgmeier, Cole A. Deisseroth, Laura E. Hayward, Luisa M. T. Galhardo, Andrew P. Tierno, Karthik A. Jagadeesh, Peter D. Stenson, David N. Cooper, Jonathan A. Bernstein, Maximilian Haeussler, and Gill Bejerano. AVADA: Towards Automated Pathogenic Variant Evidence Retrieval Directly from the Full Text Literature. . Genetics in Medicine. 2019. PMID: 31467448