Abstract
High throughput deep-sequencing or next-generation sequencing has emerged as an exciting new tool in a great number of applications (e.g., variant discovery, profiling of histone modifications, identifying transcription factor binding sites, resequencing, and transcriptome characterization). Even though this technology has generated unprecedented amounts of data in the scientific community few studies have looked carefully at its inherent variability. Recent studies of mRNA expression levels found little appreciable technical variation in Illumina’s Solexa sequencing platform (a next-generation sequencing device). Although these results are encouraging, they are limited to a specific platform and application, and have been made without any attention to experimental design. This paper provides an overview of some key issues in data management and experimental design related to Illumina’s Solexa Genome Analyzer technology.
Keywords
next-generation sequencing, RNA-Seq, experimental design
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Auer, Paul L. and Doerge, R. W.
(2009).
"STATISTICAL ISSUES IN NEXT-GENERATION SEQUENCING,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1077
STATISTICAL ISSUES IN NEXT-GENERATION SEQUENCING
High throughput deep-sequencing or next-generation sequencing has emerged as an exciting new tool in a great number of applications (e.g., variant discovery, profiling of histone modifications, identifying transcription factor binding sites, resequencing, and transcriptome characterization). Even though this technology has generated unprecedented amounts of data in the scientific community few studies have looked carefully at its inherent variability. Recent studies of mRNA expression levels found little appreciable technical variation in Illumina’s Solexa sequencing platform (a next-generation sequencing device). Although these results are encouraging, they are limited to a specific platform and application, and have been made without any attention to experimental design. This paper provides an overview of some key issues in data management and experimental design related to Illumina’s Solexa Genome Analyzer technology.
