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Tissue-specific cis-eQTL single nucleotide variant Annotation and prediction
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README.md
getscore.R

README.md

TIVAN

Tissue-specific cis-eQTL single nucleotide variant Annotation and prediction

Cite

Chen L, Wang Y, Yao B, Mitra A, Wang X, Qin X (2018). TIVAN: Tissue-specific cis-eQTL single nucleotide variant annotation and prediction. Bioinformatics, bty872

Author

Li Chen

Maintainer

Li Chen chen61@iu.edu

Description

Here, we present TIVAN (TIssue-specific Variant Annotation and prediction) to predict cis-eQTL SNVs by considering tissue-specificity. TIVAN is trained based on a comprehensive collection of features including genome-wide genomic and epigenomic profiling data. TIVAN has been shown to accurately discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to other methods. This site provides the pre-computed scores for hg19 and software toolkit to obtain the scores for queried variants.

Summary statistics for 44 eQTL SNVs in GTEx

Tissue Tissue.class eQTL.SNV
Adipose_Subcutaneous Adipose 312097
Adipose_Visceral_Omentum Adipose 148620
Adrenal_Gland Adrenal Gland 102070
Artery_Aorta Artery 213995
Artery_Coronary Artery 76036
Artery_Tibial Artery 297674
Brain_Anterior_cingulate_cortex_BA24 Brain 36778
Brain_Caudate_basal_ganglia Brain 74976
Brain_Cerebellar_Hemisphere Brain 93993
Brain_Cerebellum Brain 123205
Brain_Cortex Brain 74323
Brain_Frontal_Cortex_BA9 Brain 62226
Brain_Hippocampus Brain 33820
Brain_Hypothalamus Brain 37799
Brain_Nucleus_accumbens_basal_ganglia Brain 62692
Brain_Putamen_basal_ganglia Brain 47004
Breast_Mammary_Tissue Breast 145913
Cells_EBV-transformed_lymphocytes Lymphocytes 95401
Cells_Transformed_fibroblasts Fibroblasts 315127
Colon_Sigmoid Colon 97000
Colon_Transverse Colon 150136
Esophagus_Gastroesophageal_Junction Esophagus 93865
Esophagus_Mucosa Esophagus 271541
Esophagus_Muscularis Esophagus 248781
Heart_Atrial_Appendage Heart 132443
Heart_Left_Ventricle Heart 154101
Liver Liver 49395
Lung Lung 265588
Muscle_Skeletal Muscle 277941
Nerve_Tibial Nerve 352489
Ovary Ovary 47446
Pancreas Pancreas 137961
Pituitary Pituitary 65542
Prostate Prostate 45189
Skin_Not_Sun_Exposed_Suprapubic Skin 184157
Skin_Sun_Exposed_Lower_leg Skin 323542
Small_Intestine_Terminal_Ileum Intestine 36771
Spleen Spleen 76520
Stomach Stomach 119532
Testis Testis 312917
Thyroid Thyroid 358276
Uterus Uterus 26619
Vagina Vagina 28397
Whole_Blood Blood 256421

Download hg19 SNVs, all cis-eQTLs in the training set and pre-computed scores of 44 tissues for hg19 SNVs

Download Here

System requirement

To successfully run the example below, we strongly recommend at least 4GB memory for the PC/laptop/Work station. The example has been successfully performed on a MacBook laptop with a 1.7 GHz processor and 8 GB memory.

Installation, GenomicRanges R Bioconductor package

source("https://bioconductor.org/biocLite.R")
biocLite("GenomicRanges")

An example to obtain score for a list of eQTL SNVs from Adipose_Subcutaneous

Adipose_Subcutaneous.eQTL.rda could be downloaded from Here

Adipose_Subcutaneous.score.rda could be downloaded Here

hg19.SNVs.rda could be downloaded Here

getscore.R could be downloaded Here

# hg19.SNVs.rda contains an object hg19.SNVs, which SNVs for hg19
# Adipose_Subcutaneous.score.rda contains an object score, which is pre-computed scores for hg19 SNVs in Adipose_Subcutaneous
# Adipose_Subcutaneous.eQTL.rda contains an object snp1, which are eQTL SNVs for Adipose_Subcutaneous

library("GenomicRanges")
load('hg19.SNVs.rda')
load('Adipose_Subcutaneous.score.rda')
load('Adipose_Subcutaneous.eQTL.rda')
source('getscore.R')
eQTLs.score=getscore(snp1,hg19.SNVs,score)
head(eQTLs.score)
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