Predicting the molecular complexity of sequencing libraries

Predicting the molecular complexity of a genomic sequencing library has emerged as a critical but difficult problem in modern applications of genome sequencing. Available methods to determine either how deeply to sequence, or predict the benefits of additional sequencing, are almost completely lacking. We introduce an empirical Bayesian method to implicitly model any source of bias and accurately characterize the molecular complexity of a DNA sample or library in almost any sequencing application.