TABLE 3.

 Lists of computational tools and bioinformatics resources for analysis of personal mutanomes in cancer

Names	Description		Website	Refs
Cancer genomics resources
TCGA and GDC	The Cancer Genome Atlas and Genomic Data Commons Data Portal		https://portal.gdc.cancer.gov	Chin et al. (2011)
ICGC	The International Cancer Genome Consortium		https://icgc.org	Milius et al. (2014)
CBioPortal	Providing visualization, analysis and download of large-scale cancer genomics data sets.		http://www.cbioportal.org	Gao et al. (2013)
COSMIC	Comprehensive resources for curating somatic mutations		http://cancer.sanger.ac.uk/cosmic	Forbes et al. (2015)
Bioinformatics resources for protein structures
PDB	The PDB is the global archive for experimentally determined, atomic-level three-dimensional structures of proteins, DNA, and RNA.		https://www.rcsb.org/	Rose et al. (2017)
Interactome3D	Manually curated protein-protein interactions with known three-dimensional structure information.		http://interactome3d.irbbarcelona.org	Mosca et al. (2013)
dSysMap	dSysMap is a useful tool to study the network perturbations by genetic variants		http://dsysmap.irbbarcelona.org/	Mosca et al. (2015)
Interactome INSIDER	An integrative structural and genomic resource for functional exploration of human disease mutations at multiple-scale, proteome-wide human interactome		http://interactomeinsider.yulab.org	Meyer et al. (2018)
Mutational clustering tools on protein structures
AlloDriver	Detect druggable mutations via dysregulation of protein allosteric functions.		NA	Shen et al. (2017)
Cancer3D	A useful tool to explore potential cancer drivers or pharmacogenomic biomarkers using protein structure information.		http://cancer3d.org/	Porta-Pardo et al. (2015)
CLUMPS	Assess the significance of mutational clustering in a given 3D structure.		NA	Kamburov et al. (2015)
HotSpot3D	Detect mutation–mutation and mutation–drug clusters using three-dimensional protein structures.		NA	Niu et al. (2016)
KNMPx	Detect a kinome-wide pharmacogenomic biomarkers via rewiring phosphorylation-related signaling networks and drug sensitivity/resistance		NA	Zhao et al. (2017)
ReKINect	Detect network-attacking mutations in phosphorylation-based signaling networks.		http://rekinect.science/	Creixell et al. (2015)
SGDriver	A structural genomics-based method that detects mutation clustering on protein–ligand binding site residues via a Bayes inference statistical framework.		NA	Zhao et al. (2016)