Predicting the molecular complexity of sequencing libraries
By Timothy P. Daley, Andrew D. Smith. Nature Methods, 2013
320 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/604d02d3ff8238440540fd4824ab8c7ad251a43c
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In Artificial Intelligence
In Molecular Biology
- Thomas Sicheritz-Pontén
- Christian Carøe
- J. Krause
- Alexander Seitz
- Shyam Gopalakrishnan
- K. Nieselt
- A. Herbig
- Christian Kniep
Citations in the corpus
- Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing (2017, unknown): “This had a clear effect on overall library complexity as while there was no statistically significant difference with regards to library clonality levels or the % reads retained after initial filtering (Tables 2 and 3), whenwe used preseq [85] to extrapolate on the library complexity, we observed th”
- Single‐tube library preparation for degraded DNA (2017, methodology): “The Preseq analysis clearly showed that the BEST method had the highest complexity on average, followed by ATST and Ultra, while the BEMC showed very low complexity (Figure 4).”
- EAGER: efficient ancient genome reconstruction (2016, methodology): “Furthermore, the mapping module contains the Preseq tool [14] to determine the complexity of the sequencing library.”