Abstract
The past two decades ofmicroRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regula- tors ofmany biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platformof choice for the discovery and quantification ofmiRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstreamanalyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn fromdownstreamanalyses. Using a spike-in dilution study, we evaluated the effects of several gen- eral-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. Wemake practical recommendations on the optimal preprocessingmethods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments.
| Original language | English (US) |
|---|---|
| Article number | bbv019 |
| Pages (from-to) | 950-963 |
| Number of pages | 14 |
| Journal | Briefings in Bioinformatics |
| Volume | 16 |
| Issue number | 6 |
| DOIs | |
| State | Published - Feb 6 2015 |
| Externally published | Yes |
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Keywords
- Data preprocessing
- miRNA sequencing
- miRNA-seq normalization
- Small RNA sequence alignment
ASJC Scopus subject areas
- Information Systems
- Molecular Biology
Cite this
Optimization of miRNA-seq data preprocessing. / Tam, Shirley; Tsao, Ming Sound; Mcpherson, John Douglas.
In: Briefings in Bioinformatics, Vol. 16, No. 6, bbv019, 06.02.2015, p. 950-963.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimization of miRNA-seq data preprocessing
AU - Tam, Shirley
AU - Tsao, Ming Sound
AU - Mcpherson, John Douglas
PY - 2015/2/6
Y1 - 2015/2/6
N2 - The past two decades ofmicroRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regula- tors ofmany biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platformof choice for the discovery and quantification ofmiRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstreamanalyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn fromdownstreamanalyses. Using a spike-in dilution study, we evaluated the effects of several gen- eral-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. Wemake practical recommendations on the optimal preprocessingmethods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments.
AB - The past two decades ofmicroRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regula- tors ofmany biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platformof choice for the discovery and quantification ofmiRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstreamanalyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn fromdownstreamanalyses. Using a spike-in dilution study, we evaluated the effects of several gen- eral-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. Wemake practical recommendations on the optimal preprocessingmethods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments.
KW - Data preprocessing
KW - miRNA sequencing
KW - miRNA-seq normalization
KW - Small RNA sequence alignment
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UR - http://www.scopus.com/inward/citedby.url?scp=84938380221&partnerID=8YFLogxK
U2 - 10.1093/bib/bbv019
DO - 10.1093/bib/bbv019
M3 - Article
C2 - 25888698
AN - SCOPUS:84938380221
VL - 16
SP - 950
EP - 963
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
IS - 6
M1 - bbv019
ER -