Tissue-specific cis-eQTL single nucleotide variant Annotation and prediction
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
Li Chen
Li Chen li.chen@auburn.edu
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.
| 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
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.
source("https://bioconductor.org/biocLite.R")
biocLite("GenomicRanges")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)