MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer.

Nucleic Acids Res 2016 Jan 20;44(D1):D986-91. Epub 2015 Nov 20.

Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK

The MutationAligner web resource, available at, enables discovery and exploration of somatic mutation hotspots identified in protein domains in currently (mid-2015) more than 5000 cancer patient samples across 22 different tumor types. Using multiple sequence alignments of protein domains in the human genome, we extend the principle of recurrence analysis by aggregating mutations in homologous positions across sets of paralogous genes. Protein domain analysis enhances the statistical power to detect cancer-relevant mutations and links mutations to the specific biological functions encoded in domains. We illustrate how the MutationAligner database and interactive web tool can be used to explore, visualize and analyze mutation hotspots in protein domains across genes and tumor types. We believe that MutationAligner will be an important resource for the cancer research community by providing detailed clues for the functional importance of particular mutations, as well as for the design of functional genomics experiments and for decision support in precision medicine. MutationAligner is slated to be periodically updated to incorporate additional analyses and new data from cancer genomics projects.

Download full-text PDF

Source Listing
January 2016

Publication Analysis

Top Keywords

protein domains
mutation hotspots
tumor types
hotspots protein
enhances statistical
genomics experiments
design functional
analysis enhances
statistical power
links mutations
functional genomics
detect cancer-relevant
mutations links
domain analysis
cancer-relevant mutations
power detect
paralogous genes

Similar Publications

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell Syst 2015 Sep 23;1(3):197-209. Epub 2015 Sep 23.

Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address:

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Read More

View Article and Full-Text PDF
September 2015

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer.

J Am Med Inform Assoc 2012 Mar-Apr;19(2):275-83

University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA.

Background And Objective: With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants. Read More

View Article and Full-Text PDF
May 2012

TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data.

Hum Mutat 2016 09 8;37(9):865-76. Epub 2016 Jul 8.

Group of Molecular Mechanisms and Biomarkers, International Agency for Research on Cancer, Lyon Cedex 08, 69372, France.

TP53 gene mutations are one of the most frequent somatic events in cancer. The IARC TP53 Database (http://p53.iarc. Read More

View Article and Full-Text PDF
September 2016

Identification and analysis of mutational hotspots in oncogenes and tumour suppressors.

Oncotarget 2017 Mar;8(13):21290-21304

School of Life Sciences, University of Sussex, Falmer, Brighton, UK.

Background: The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes.

Results: In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Read More

View Article and Full-Text PDF
March 2017