Utilizing protein structure to identify non-random somatic mutations
1 Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
2 Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
3 , Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA
4 Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA
BMC Bioinformatics 2013, 14:190 doi:10.1186/1471-2105-14-190Published: 13 June 2013
Additional file 1:
Cosmic Query. The SQL query used to extract the mutations from COSMIC.
Format: DOCX Size: 22KB Download file
Additional file 2:
Structure Files. A detailed list of which protein-structure combinations were used and what side-chains were selected.
Format: XLSX Size: 70KB Download file
Additional file 3:
Results Summary. A summary of each structure’s most significant p-value for both iPAC and NMC.
Format: XLSX Size: 33KB Download file
Additional file 4:
Relevant Sites. A review showing which of the iPAC clusters fall within structurally relevant sites.
Format: XLSX Size: 28KB Download file
Additional file 5:
Performance Validation. In-depth results validating the iPAC results using PolyPhen-2 and CHASM.
Format: XLSX Size: 25KB Download file
Additional file 6:
Potential Driver Loss. An analysis of whether any potential driver mutations are lost when iPAC finds fewer clusters than NMC.
Format: XLSX Size: 18KB Download file