Computational and analytical framework for small RNA profiling by high-throughput sequencing.
The advent of high-throughput sequencing (HTS) methods has enabled direct approaches to quantitatively profile small RNA populations. However, these methods have been limited by several factors, including representational artifacts and lack of established statistical methods of analysis. Furthermore, massive HTS data sets present new problems related to data processing and mapping to a reference genome. Here, we show that cluster-based sequencing-by-synthesis technology is highly reproducible as a quantitative profiling tool for several classes of small RNA from Arabidopsis thaliana. We introduce the use of synthetic RNA oligoribonucleotide standards to facilitate objective normalization between HTS data sets, and adapt microarray-type methods for statistical analysis of multiple samples. These methods were tested successfully using mutants with small RNA biogenesis (miRNA-defective dcl1 mutant and siRNA-defective dcl2 dcl3 dcl4 triple mutant) or effector protein (ago1 mutant) deficiencies. Computational methods were also developed to rapidly and accurately parse, quantify, and map small RNA data.
Fahlgren
Noah
N
Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon 97331, USA.
Sullivan
Christopher M
CM
Kasschau
Kristin D
KD
Chapman
Elisabeth J
EJ
Cumbie
Jason S
JS
Montgomery
Taiowa A
TA
Gilbert
Sunny D
SD
Dasenko
Mark
M
Backman
Tyler W H
TW
Givan
Scott A
SA
Carrington
James C
JC
D017360:Arabidopsis / Q000235:genetics*; D019295:Computational Biology; D020869:Gene Expression Profiling; D018749:RNA, Plant / Q000235:genetics*; D017423:Sequence Analysis, RNA
D018749:RNA, Plant