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Yi et al. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nature Reviews Genetics (2017) Supplementary information S1 (table). Additional information on the computational tools for genetic variant interpretation listed in Table 1. Table part 1 | Node centered computational methods to characterize the function of cancer mutations. Features Sequence feature Structural feature Tools SIFT/SIFT4G Descriptions An algorithm that predicts whether an amino acid substitution is deleterious to protein function based on conservation. MutSig/ MutSigCV MuSiC A method to identify genes that are mutated more often than expected by chance given background mutation processes. MSEA PolyPhen-2 STRUM CanDrA MutationTaster ActiveDriver CanBind SGDriver e-Driver Regulatory feature Mutation3D ANNOVAR CADD GWAVA FitCons deltaSVM GWAS3D DeepSEA Function and CanPredict A pipeline that uses standardized sequence-based inputs along with clinical data to establish correlations among mutation sites, affected genes and pathways. Implemented by two methods (MSEA-clust and MSEA-domain) to predict cancer genes based on mutation hotspot patterns. A method for predicting damaging effects of missense mutations based on eight sequence-based and three structure-based predictive features. A method to predict SNP mutation induced stability changes using low-resolution structure modeling. A machine learning program that predicts cancer-type specific driver missense mutations based on structural, evolutionary and gene features. An application for evaluation of the disease-causing potential of mutations based on conservation, splice-site changes, loss of protein features and mRNA changes. A gene-centric method to identify genes with significant phosphorylation-associated SNVs. Extracting proteins with an enriched number of mutations affecting interactions among nucleic acids, small molecules, ions and peptides. A structural genomics-based method incorporating missense mutations into protein-ligand binding site residues using a Bayes inference framework. A method that exploits the internal distribution of mutations between the protein's functional regions with a bias in their mutation rate as compared with other regions of the same protein. A tool to identify mutation clusters on 3D protein structures. An efficient software tool to utilize up-to-date information to functionally annotate genetic variants, providing protein coding changes, overlap with conserved regions, regulatory regions, GWAs hits and RNA-Seq peaks. Integrating information contained in diverse annotations (such as, conservation, regulatory information and protein-level scores) of genetic variation to a single score to measuring the deleteriousness of mutations. A tool that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations. A method for calculating probabilities of fitness consequences for point mutations across the human genome on the basis of data from ENCODE. A sequence-based method to predict the effect of regulatory variation using gkm-SVM classifier that encodes cell typespecific regulatory sequence vocabularies. A web server to analyze the mutations that could affect regulatory elements by integrating annotations from chromatin states, epigenetic modifications, sequence motifs and conservation. A deep learning-based framework that directly learns a regulatory sequence code from chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations. Integrating SIFT, Pfam-based structure and GO similarity score to determine cancer associated variants. network transFIC FunSeq2 SuSPect Integrating GO, pathway and Pfam annotations to assess of the functional impact of tumor SNVs by taking into account the baseline tolerance of genes to functional variants. Integrating conservation, TF binding, enhancer-gene linkages and network centrality to annotate and prioritize mutations. Incorporating sequence conservation and network-level features (degree) for predicting phenotypic impact of mutations. Table part 2| Edgetic computational methods to characterize the function of cancer mutations. Features Protein-protein interaction Tools BeAtMuSiC StructurePPi dSysMap TF-gene interaction MutaBind is-rSNP HaploReg OncoCis miRNA-gene interaction BayesPIBAR Patrocles SomamiR PolymiRTS Descriptions Integrating the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. Comprehensive analysis of coding SNVs based on 3D structures of protein complexes with features extracted from UniProt, InterPro, APPRIS, dbNSFP and COSMIC databases. A resource for mapping mutations on protein structures and on interaction interfaces to visualize the region of the interactome that they affect. Evaluating the effects of disease mutations on protein interactions by quantitative changes in binding affinity. Identifying regulatory SNPs by scoring two alleles with a TF position weight matrix (PWM). A tool for exploring annotations of the non-coding genome among the results of GWAs, inclduing chromatin state, protein binding, and the effect of SNPs on regulatory motifs. A tool for annotating cancer mutations in cis-regulatory regions of DNA, allowing to check if the mutation caused a TF binding site to be removed or created. A Bayesian method for protein-DNA interaction with binding affinity ranking for quantifying the effect of sequence variations on protein binding. A database that compiles mutations predicted to perturb miRNA-mediated gene regulation. A database that contains somatic mutations that may create or disrupt miRNA target sites on mRNA, lncRNA and circRNA. An integrated platform for analyzing the functional impact of genetic polymorphisms in miRNA seed regions and miRNA target sites. Table part 3| Computational methods to identify mutated subnetworks or pathways for characterizing the function of cancer mutations. Tools DriverNet TieDIE OncoIMPACT VarWalker HotNet/HotNet2 NBS Descriptions A novel computational framework to identify driver mutations by virtue of their effect on mRNA expression networks, and it formulates associations between mutations and expression levels using a bipartite graph. A network diffusion approach for identifying cancer mutated subnetworks. Prioritizing significantly mutated genes through integrated network analysis of cancer omics profiles. Mutated subnetworks are selected and optimized using the Random Walk with Restart algorithm in PPI networks. A new algorithm to find mutated subnetworks, using a modified diffusion process and considering the source, or directionality, of heat flow in the identification of subnetworks. A method to integrate somatic tumor genomes with gene networks, allowing for stratification of cancer into informative subtypes and identifying network regions characteristic of each subtype. SUPPLEMENTARY INFORMATION Tool weblinks: SIFT: http://sift.jcvi.org/ SIFT4G: http://sift-dna.org/sift4g MutSig/MutSigCV: http://archive.broadinstitute.org/cancer/cga/mutsig MuSiC: http://gmt.genome.wustl.edu/packages/genome-music/ MSEA: https://github.com/bsml320/MSEA PolyPhen-2: http://genetics.bwh.harvard.edu/pph2/ STRUM: http://zhanglab.ccmb.med.umich.edu/STRUM/ CanDrA: http://bioinformatics.mdanderson.org/main/CanDrA MutationTaster: http://www.mutationtaster.org/ ActiveDriver: http://www.baderlab.org/Software/ActiveDriver CanBind: http://canbind.princeton.edu e-Driver: https://github.com/eduardporta/e-Driver.git Mutation3D: http://mutation3d.org ANNOVAR: http://annovar.openbioinformatics.org/en/latest/ CADD: http://cadd.gs.washington.edu/ GWAVA: http://www.sanger.ac.uk/science/tools/gwava deltaSVM: http://www.beerlab.org/deltasvm/ GWAS3D: http://jjwanglab.org/gwas3d DeepSEA: http://deepsea.princeton.edu/job/analysis/create/ FunSeq2: http://funseq2.gersteinlab.org/ SuSPect: http://www.sbg.bio.ic.ac.uk/~suspect/ BeAtMuSiC: http://babylone.ulb.ac.be/beatmusic Structure-PPi: http://structureppi.bioinfo.cnio.es/Structure dSysMap: http://dsysmap.irbbarcelona.org/ MutaBind: http://www.ncbi.nlm.nih.gov/projects/mutabind/ NATURE REVIEWS | GENETICS In format provided by Yi et al. (doi:10.1038/nrg.2017.8) HaploReg: http://archive.broadinstitute.org/mammals/haploreg/haploreg.php OncoCis: http://149.171.80.192/OncoCis/ BayesPI-BAR: http://folk.uio.no/junbaiw/BayesPI-BAR/ SomamiR/SomamiR 2.0: http://compbio.uthsc.edu/SomamiR/ PolymiRTS: http://compbio.uthsc.edu/miRSNP/ DriverNet: http://compbio.bccrc.ca/software/drivernet/ TieDIE: https://sysbiowiki.soe.ucsc.edu/tiedie OncoIMPACT: https://sourceforge.net/projects/oncoimpact/ VarWalker: https://bioinfo.uth.edu/VarWalker.html HotNet/HotNet2: http://compbio.cs.brown.edu/projects/hotnet2/ NBS: http://chianti.ucsd.edu/~mhofree/NBS/ www.nature.com/nrg
