Brief. Bioinformatics - The dilemma of choosing the ideal permutation strategy while estimating statistical significance of genome-wide enrichment.

Tópicos

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Resumo

Integrative analyses of genomic, epigenomic and transcriptomic features for human and various model organisms have revealed that many such features are nonrandomly distributed in the genome. Significant enrichment (or depletion) of genomic features is anticipated to be biologically important. Detection of genomic regions having enrichment of certain features and estimation of corresponding statistical significance rely on the expected null distribution generated by a permutation model. We discuss different genome-wide permutation approaches, present examples where the permutation strategy affects the null model and show that the confidence in estimating statistical significance of genome-wide enrichment might depend on the choice of the permutation approach. In those cases, where biologically relevant constraints are unclear, it is preferable to examine whether key conclusions are consistent, irrespective of the choice of the randomization strategy.

Resumo Limpo

integr analys genom epigenom transcriptom featur human various model organ reveal mani featur nonrandom distribut genom signific enrich deplet genom featur anticip biolog import detect genom region enrich certain featur estim correspond statist signific reli expect null distribut generat permut model discuss differ genomewid permut approach present exampl permut strategi affect null model show confid estim statist signific genomewid enrich might depend choic permut approach case biolog relev constraint unclear prefer examin whether key conclus consist irrespect choic random strategi

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