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Genome Biol. 2016 Mar 23;17:53. doi: 10.1186/s13059-016-0917-0.

The real cost of sequencing: scaling computation to keep pace with data generation.

Author information

1
Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, 06520, USA.
2
Systems Biology Institute, Yale University, West Haven, CT, 06516, USA.
3
Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT, 06520, USA.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
5
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
6
The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
7
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA. mark@gersteinlab.org.
8
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA. mark@gersteinlab.org.
9
Department of Computer Science, Yale University, New Haven, CT, 06520, USA. mark@gersteinlab.org.

Abstract

As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.

PMID:
27009100
PMCID:
PMC4806511
DOI:
10.1186/s13059-016-0917-0
[Indexed for MEDLINE]
Free PMC Article

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