Table 1.

Online Resources for Accessing Noncoding SNP Annotation Tools^a

Tool	URL	Refs
VEP	VEP incorporates annotations from the Ensembl database, allowing it to make predictions genome-wide as well as predict tissue-specific activity for 13 human cell lines. http://www.ensembl.org/info/docs/tools/vep/script/index.html	McLaren et al. [17]
RegulomeDB	RegulomeDB uses a heuristic scoring system to catalog the likelihood that a given SNP or indel resides in a functional region, using functional data from over 100 cell types. http://regulomedb.org	Boyle et al. [18]
FunciSNP	FunciSNP is an R/Bioconductor package that employs user input annotations to prioritize SNPs, allowing users to customize their annotations to query a cell type of interest. http://www.bioconductor.org/packages/release/bioc/html/FunciSNP.html	Coetzee et al. [19]
ANNOVAR	ANNOVAR is a command line tool that uses region-based annotations to annotate noncoding variants and insertions and deletions (indels), in addition to comparing them to known variation databases. http://annovar.openbioinformatics.org	Wang et al. [21]
HaploReg	HaploReg is a searchable repository for SNPs and indels from the 1000 Genomes Project, providing a summary of known annotations for variants within an LD block. http://www.broadinstitute.org/mammals/haploreg/haploreg.php	Ward and Kellis [22]
GWAS3D	GWAS3D evaluates SNPs and indels by analyzing their 3D chromosomal interactions and disruptions to TF binding affinity. It outputs scores as well as a circle plot mapping local 3D interactions. http://jjwanglab.org/gwas3d	Li et al. [23]
fitCons	fitCons uses the INSIGHT method to predict the probability that SNPs will influence fitness by screening for signatures positive and negative selection using data from three cell types. http://compgen.bscb.cornell.edu/fitCons/	Gulko et al. [24]
GWAVA	GWAVA trains on a random forest algorithm using disease mutations from HGMD and control variants from the 1000 genomes project to predict if queried variants are functional. ftp://ftp.sanger.ac.uk/pub/resources/software/gwava/	Ritchie et al. [28]
CADD	CADD trains on a linear kernel support vector matrix using simulated variants as deleterious variants and alleles fixed between human and chimpanzee as control variants. http://cadd.gs.washington.edu	Kircher et al. [26]
DANN	DANN trains on a nonlinear learning neural network algorithm using the same training set data (fixed alleles vs. simulated variants) as CADD. https://cbcl.ics.uci.edu/public_data/DANN/	Quang et al. [29]
FATHMM-MKL	FATHMM-MKL implements a kernel-based classifier to estimate complex nonlinear patterns using HGMD pathogenic and 1000 Genomes Project control variant training set data. http://fathmm.biocompute.org.uk	Shihab et al. [30]
deltaSVM	deltaSVM uses a gapped k-mer support vector machine to estimate the effect of a variant in a cell-type-specific manner. http://www.beerlab.org/deltasvm/	Lee et al. [31]
DeepSEA	DeepSEA uses a multilayered hierarchical structured deep learning-based sequence model to predict functional SNPs with single nucleotide sensitivity using ENCODE and Roadmap Epigenomics data. http://deepsea.princeton.edu/job/analysis/create/	Zhou and Troyanskaya [32]

^aAbbreviation: HGMD, Human Gene Mutation Database.