GEMINI: a flexible framework for exploring genome variation



At long last, version 0.12.2 of GEMINI supports multi-allelic variants thanks to great work from Brent Pedersen. In order to provide this support, GEMINI now requires that your input VCF file undergo additional preprocessing such that multi-allelic variants are decomposed and normalized using the vt toolset from the Abecasis lab. Note that we have also decomposed and normalized all of the VCF-based annotation files (e.g., ExAC, dbSNP, ClinVar, etc.) so that variants and alleles are properly annotated and we minimize false negative and false positive annotations. For a great discussion of why this is necessary, please read this blog post from Eric Minikel in Daniel MacArthur’s lab.

Essentially, VCF preprocessing for GEMINI now boils down to the following steps.

  1. If working with GATK VCFs, you need to correct the AD INFO tag definition to play nicely with vt.
  2. Decompose the original VCF such that variants with multiple alleles are expanded into distinct variant records; one record for each REF/ALT combination.
  3. Normalize the decomposed VCF so that variants are left aligned and represented using the most parsimonious alleles.
  4. Annotate with VEP or snpEff.
  5. bgzip and tabix.

A workflow for the above steps is given below.

# setup

# decompose, normalize and annotate VCF with snpEff.
# NOTE: can also swap snpEff with VEP
#NOTE: -classic and -formatEff flags needed with snpEff >= v4.1
zless $VCF \
   | sed 's/ID=AD,Number=./ID=AD,Number=R/'
   | vt decompose -s - \
   | vt normalize -r $REF - \
   | java -Xmx4G -jar $SNPEFFJAR -formatEff -classic GRCh37.75  \
   | bgzip -c > $NORMVCF
tabix $NORMVCF

# load the pre-processed VCF into GEMINI
gemini load --cores 3 -t snpEff -v $NORMVCF $db

# query away
gemini query -q "select chrom, start, end, ref, alt, (gts).(*) from variants" \
             --gt-filter " == HET and \
                 == HET and \
                          gt_types.kid == HOM_ALT" \


GEMINI (GEnome MINIng) is designed to be a flexible framework for exploring genetic variation in the context of the wealth of genome annotations available for the human genome. By placing genetic variants, sample genotypes, and useful genome annotations into an integrated database framework, GEMINI provides a simple, flexible, yet very powerful system for exploring genetic variation for disease and population genetics.

Using the GEMINI framework begins by loading a VCF file (and an optional PED file) into a database. Each variant is automatically annotated by comparing it to several genome annotations from source such as ENCODE tracks, UCSC tracks, OMIM, dbSNP, KEGG, and HPRD. All of this information is stored in portable SQLite database that allows one to explore and interpret both coding and non-coding variation using “off-the-shelf” tools or an enhanced SQL engine.

Please also see the original manuscript.

This video provides more details about GEMINI’s aims and utility.


  1. GEMINI solely supports human genetic variation mapped to build 37 (aka hg19) of the human genome.
  2. GEMINI is very strict about adherence to VCF format 4.1.
  3. For best performance, load and query GEMINI databases on the fastest hard drive to which you have access.


If you use GEMINI in your research, please cite the following manuscript:

Paila U, Chapman BA, Kirchner R, Quinlan AR (2013)
GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations.
PLoS Comput Biol 9(7): e1003153. doi:10.1371/journal.pcbi.1003153

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GEMINI is a flexible framework for exploring genome variation.

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