Optimization of miRNA-seq Data Pre-Processing
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ABSTRACT: Next-generation sequencing is currently the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be pre-processed prior to conducting downstream analyses. Often overlooked, data pre-processing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-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.
ORGANISM(S): Homo sapiens
PROVIDER: GSE67074 | GEO | 2015/03/21
SECONDARY ACCESSION(S): PRJNA278977
REPOSITORIES: GEO
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