title: Predicting the molecular complexity of sequencing libraries.
abstract: Predicting the molecular complexity of a genomic sequencing library is a critical but difficult problem in modern sequencing applications. Methods to determine how deeply to sequence to achieve complete coverage or to predict the benefits of additional sequencing are lacking. We introduce an empirical bayesian method to accurately characterize the molecular complexity of a DNA sample for almost any sequencing application on the basis of limited preliminary sequencing.
authors: Department of Mathematics, University of Southern California, Los Angeles, California, USA.
authors: Timothy
authors: T
authors: Daley
authors: Andrew D
authors: AD
authors: Smith
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003001:Cloning, Molecular; D030541:Databases, Genetic; D015723:Gene Library; D023281:Genomics / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D002679:Pan troglodytes / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*