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Open Access Highly Accessed Methodology article

Utilizing protein structure to identify non-random somatic mutations

Gregory A Ryslik1*, Yuwei Cheng2, Kei-Hoi Cheung23, Yorgo Modis4 and Hongyu Zhao12*

Author Affiliations

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

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BMC Bioinformatics 2013, 14:190  doi:10.1186/1471-2105-14-190

Published: 13 June 2013

Additional files

Additional file 1:

Cosmic Query. The SQL query used to extract the mutations from COSMIC.

Format: DOCX Size: 22KB Download file

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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

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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

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Additional file 4:

Relevant Sites. A review showing which of the iPAC clusters fall within structurally relevant sites.

Format: XLSX Size: 28KB Download file

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Additional file 5:

Performance Validation. In-depth results validating the iPAC results using PolyPhen-2 and CHASM.

Format: XLSX Size: 25KB Download file

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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

Open Data