Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among Observations.

ABSTRACT: In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations

SUBMITTER: Rashid NU 

PROVIDER: S-EPMC3963211 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature