It is noteworthy that SIFT and POLYPHEN have analyzed data from other mutation databases such as the human non-synonymous single nucleotide polymorphism database [ 22, 40, 41].
For example, certain dataset may be more suited for detection of susceptibility variants, while applying to single gene disorders, training set from known causal mutation databases such as HumDiv, OMIM or HGMD [ 25] may work better.
Locus specific databases (see http://www.hgvs.org/dblist/glsdb.html for a comprehensive list) and "whole-genome" mutation databases such as HGMD [ 33], ClinVar [ 34], LOVD [ 35], and OMIM [ 36] are very informative resources for this task.
We investigated the coding sequences of 21 genes, selected according to the previously reported genes most frequently mutated in CRCs [ 3, 5, 7] or present in public cancer mutations databases, such as COSMIC [ 11] and cBioPortal [ 12] (Additional file 2: Table S1).
HTS studies are highly reliant on ascribing likely pathogenicity according to mutational databases such as the Leiden Open Variation Database (LOVD, http://www.LOVD.nl/), The Human Gene Mutation Database (HGMD, http://www.hgmd.cf.ac.uk/) and ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/).nih.gov/clinvar/
It might be helpful to utilize a number of locus-specific mutation databases for specific diseases or genes such as the HbVar database [31] for human hemoglobin variations and thalassemia mutations, and the IDR database [32] for immunodeficiency mutations.
In addition, our database can be expanded so as to include and integrate more data in the future including somatic mutations in melanoma from publicly available databases such as the COSMIC database (22), as well as more and of different types of pre-compiled relationship networks.
The development of locus-specific mutation databases (LSDBs) and tools to build them such as the Leiden Open Variation Database (LOVD) [ 3], and the Universal Mutation Database (UMD) [ 4] started to pave the way to solve the problem of collecting genetic datasets produced by diverse experimental methods in different laboratories.
These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information.
The information associated with mutations in a protein family can be obtained from databases such as OMIM as well as publications related to the protein family, their clinical importance and reported phenotypes known to be associated with the mutations.
The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM.
