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. Modern DNA sequencing experiments often interrogate hundreds of millions or even billions of reads, possibly to achieve deep coverage or to observe very rare molecules. Low complexity DNA sequencing libraries are problematic in such experiments: many sequenced reads will correspond to the same original molecules and deeper sequencing either provides redundant data that is discarded, or introduces biases in downstream analyses. When sequencing depth appears insufficient, investigators may be presented with the decision to sequence more deeply from an existing library or to generate another. Perhaps this situation has been anticipated during experimental design, and investigators can select from severa