Unknown

Dataset Information

0

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.

SUBMITTER: Daley T 

PROVIDER: S-EPMC3612374 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

altmetric image

Publications

Predicting the molecular complexity of sequencing libraries.

Daley Timothy T   Smith Andrew D AD  

Nature methods 20130224 4


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. ...[more]

Similar Datasets

| S-EPMC8124947 | biostudies-literature
| S-EPMC4176369 | biostudies-literature
2021-11-30 | GSE152491 | GEO
| S-EPMC3116844 | biostudies-literature
| S-EPMC4919018 | biostudies-literature
| S-EPMC2577887 | biostudies-literature
| S-EPMC7277246 | biostudies-literature
| S-EPMC3483553 | biostudies-literature
| S-EPMC3876701 | biostudies-literature
| S-EPMC3495292 | biostudies-other