MODMatcher: multi-omics data matcher for integrative genomic analysis.

ABSTRACT: Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic association

SUBMITTER: Yoo S 

PROVIDER: S-EPMC4133046 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature