Methods to predict molecular complexity in sequencing experiments

描述（由申请人提供）：预测基因组测序文库的分子复杂性已经成为DNA测序的现代应用中的关键但困难的问题。在RNA-seq和单细胞测序等应用中，潜在生物样品的分子复杂性也是核心兴趣。该项目将产生计算方法，用于预测将从现有测序文库的更深测序中测序的不同分子的数量。我们将调整这些方法，以预测RNA-seq中的饱和度以及作为测序深度的函数的基因组重测序中超过一定倍数覆盖的基因组部分。我们还将开发基于单细胞RNA-seq实验估计组织中表型异质性的方法。这些方法将使研究人员能够优化他们对DNA测序资源的使用，最大限度地减少浪费并提高通量。

DESCRIPTION (provided by applicant): Predicting the molecular complexity of a genomic sequencing library has emerged as a critical but difficult problem in modern applications of DNA sequencing. In applications like RNA-seq and single-cell sequencing, the molecular complexity of the underlying biological sample is also of central interest. This project will produce computational methods for predicting the number of distinct molecules that will be sequenced from deeper sequencing of an existing sequencing library. We will adapt these methods to also predict saturation in RNA-seq and the fraction of the genome covered above some fold in genome resequencing as a function of sequencing depth. We will also develop methods for estimating heterogeneity of phenotypes in a tissue based on single-cell RNA-seq experiments. These methods will allow investigators to optimize their use of DNA sequencing resources, minimizing waste and improving throughput.