Analysis of mutations of 3D clusters with the help of HOMCOS and py3Dcluster.py

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Many mutations are observed in cancer cells, it is important to extract functionally significant sites among them. This page explains, how to identify potential cancer-associated sites by "3D cluster" sites (dense mutations site in 3D space) with the help of HOMCOS server and PyMOL script py3Dcluster.py. The algorithm for detecting 3D cluster is based on Gao et al, 2017.


Purpose

Identify potential cancer associated sites as mutation sites clustered in 3D space with statistically significance (3D cluster), and the neighboring surface sites around the 3D cluster.

Procedures


This work was supported by the Project of Osaka University Institute for Datability Science.

LastModfied:2021/05/22

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