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. 2014 Oct 13;3:20. doi: 10.1186/2047-217X-3-20

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Copyright © 2014 Cer et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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An overview of the mirnaTA workflow using a three time point example. mirnaTA converts the tab-delimited, matrix raw data into quantiles using Normal Quantile Transformation (NQT). Then, using a linear regression model, mirnaTA identifies miRNA species which either increase or decrease linearly. Any miRNA species with P < 0.05 are considered to be statistically significant (shown in the black box). miRNAs that did not fit into a linear model with statistical significance are further analyzed using either cumulative distribution function (CDF) or analysis of variance (ANOVA) depending on the number of time points.