Dataset Information

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

ABSTRACT:

Motivation

It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression ('driver' mutation) from those that do not ('passenger' mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated.

Results

In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies.

Availability

The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/.

Contact

xzhou@tmhs.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Tan H

PROVIDER: S-EPMC3496432 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Publications

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

Tan Hua H Bao Jiguang J Zhou Xiaobo X

Bioinformatics (Oxford, England) 20121007 22

<h4>Motivation</h4>It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression ('driver' mutation) from those that do not ('passenger' mutation). However, the existing methods have multifacet ...[more]

PMID: 23044540

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Project description:BackgroundGlucose-6-phosphate dehydrogenase (G6PD) deficiency is a hereditary disease caused by pathogenic mutations of G6PD. While most of the pathogenic variants of G6PD have been annotated, hemolysis of unknown etiology but analogous to that in G6PD deficiency persists, implying the existence of undocumented pathogenic variants. In our previous study, we reported four novel G6PD variants in China, for which the pathogenicity remains to be verified.MethodsThe variants were verified by exogenous expression in HEK-293 cells, and their functions were predicted by PolyPhen-2 and SIFT. The CRISPR/Cas9 system was exploited to edit the G6PD c.697G>C variant in HEK-293 cells and K562 cells. The expression of G6PD was detected by quantitative PCR (qPCR) and western blotting. The cell growth capacity was detected by the CCK-8 assay and crystal violet staining. The G6PD enzyme activity was reflected by the G6P/6PG ratio test. The apoptosis of cells was detected by Annexin V-APC/7-AAD staining. The secondary and crystallographic structures were denoted according to the literature and PyMOL software. The G6PD protein was purified from lysis of transformed Escherichia coli (E. coli) cell with Ni-charged Resin Column. The enzymatic activity was detected at different temperatures.ResultsThe G6PD activity of exogenous G6PD c.697G>C in HEK-293 cells was significantly lower than that of wild type (WT) G6PD, a finding that was consistent with the observation in clinical samples. The functional predictions conducted by different algorithms indicated the damage role of the G6PD c.697G>C variant in its enzymatic activity. We recapitulated the G6PD c.697G>C variant both in HEK-293 cells and K562 cells by adapting the CRISPR/Cas9 strategy. Using distinct cell lines expressing the G6PD c.697G>C variant endogenously, we confirmed the deteriorative role of the G6PD c.697G>C variant in its enzymatic activity. Regarding the secondary and crystallographic structure, we found a mutated amino acid approaching the structural NADP+ binding site. Finally, we demonstrated the c.697G>C variant compromised the thermal stability of G6PD protein.ConclusionsOur data delineated the pathogenic role of G6PD c.697G>C variant for G6PD deficiency, implying the wide usage of CRISPR/Cas9 for genetic disease research.

Dataset Information

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

Motivation

Results

Availability

Contact

Supplementary information

Publications

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

Similar Datasets

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