miRNA Temporal Analyzer (mirnaTA): a bioinformatics tool for identifying differentially expressed microRNAs in temporal studies using normal quantile transformation

GigaScience

Vishwesh P Mokashi¹  Kimberly A Bishop-Lilly²  Joseph J Anderson³  J Enrique Herrera-Galeano²  Regina Z Cer² 

[1] Biological Defense Research Directorate, Naval Medical Research Center-Frederick, 8400 Research Plaza, Fort Detrick, MD 21702, USA;Henry M. Jackson Foundation for the Advancement of Military Medicine, 6720-A Rockledge Drive, Suite 100, Bethesda, MD 20817, USA;Chem Bio Research Center of Excellence, Defense Threat Reduction Agency, 2800 Bush River Road E2800-b198, Aberdeen Proving Ground, MD 21010, USA

关键词: Normal quantile transformation;    Linear model;    Quantile normalization;    DE;    Differential expression;    Time series;    mirnaTA;    miRNA Temporal Analyzer;    microRNA;   

Others  :  1118584
DOI  :  10.1186/2047-217X-3-20

 received in 2014-04-08, accepted in 2014-09-30,  发布年份 2014

【 摘 要 】

Background

Understanding the biological roles of microRNAs (miRNAs) is a an active area of research that has produced a surge of publications in PubMed, particularly in cancer research. Along with this increasing interest, many open-source bioinformatics tools to identify existing and/or discover novel miRNAs in next-generation sequencing (NGS) reads become available. While miRNA identification and discovery tools are significantly improved, the development of miRNA differential expression analysis tools, especially in temporal studies, remains substantially challenging. Further, the installation of currently available software is non-trivial and steps of testing with example datasets, trying with one’s own dataset, and interpreting the results require notable expertise and time. Subsequently, there is a strong need for a tool that allows scientists to normalize raw data, perform statistical analyses, and provide intuitive results without having to invest significant efforts.

Findings

We have developed miRNA Temporal Analyzer (mirnaTA), a bioinformatics package to identify differentially expressed miRNAs in temporal studies. mirnaTA is written in Perl and R (Version 2.13.0 or later) and can be run across multiple platforms, such as Linux, Mac and Windows. In the current version, mirnaTA requires users to provide a simple, tab-delimited, matrix file containing miRNA name and count data from a minimum of two to a maximum of 20 time points and three replicates. To recalibrate data and remove technical variability, raw data is normalized using Normal Quantile Transformation (NQT), and linear regression model is used to locate any miRNAs which are differentially expressed in a linear pattern. Subsequently, remaining miRNAs which do not fit a linear model are further analyzed in two different non-linear methods 1) cumulative distribution function (CDF) or 2) analysis of variances (ANOVA). After both linear and non-linear analyses are completed, statistically significant miRNAs (P < 0.05) are plotted as heat maps using hierarchical cluster analysis and Euclidean distance matrix computation methods.

Conclusions

mirnaTA is an open-source, bioinformatics tool to aid scientists in identifying differentially expressed miRNAs which could be further mined for biological significance. It is expected to provide researchers with a means of interpreting raw data to statistical summaries in a fast and intuitive manner.

【 授权许可】

   
2014 Cer et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Kim VN, Han J, Siomi MC: Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 2009, 10(2):126-139.
• [2]Xiao C, Rajewsky K: MicroRNA control in the immune system: basic principles. Cell 2009, 136(1):26-36.
• [3]Tili E, Michaille JJ, Calin GA: Expression and function of micro-RNAs in immune cells during normal or disease state. Int J Med Sci 2008, 5(2):73-79.
• [4]Matsushima K, Isomoto H, Inoue N, Nakayama T, Hayashi T, Nakayama M, Nakao K, Hirayama T, Kohno S: MicroRNA signatures in Helicobacter pylori-infected gastric mucosa. Int J Cancer 2011, 128(2):361-370.
• [5]Wang WX, Wilfred BR, Madathil SK, Tang G, Hu Y, Dimayuga J, Stromberg AJ, Huang Q, Saatman KE, Nelson PT: miR-107 regulates granulin/progranulin with implications for traumatic brain injury and neurodegenerative disease. Am J Pathol 2010, 177(1):334-345.
• [6]Garza-Manero S, Pichardo-Casas I, Arias C, Vaca L, Zepeda A: Selective distribution and dynamic modulation of miRNAs in the synapse and its possible role in Alzheimer’s disease. Brain Res 2013, 1584:80-93.
• [7]Ono K, Kuwabara Y, Han J: MicroRNAs and cardiovascular diseases. FEBS J 2011, 278(10):1619-1633.
• [8]Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T: A uniform system for microRNA annotation. RNA 2003, 9(3):277-279.
• [9]Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011, 39(Database issue):D152-D157.
• [10]Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP: The microRNAs of Caenorhabditis elegans. Genes Dev 2003, 17(8):991-1008.
• [11]Huang TH, Fan B, Rothschild MF, Hu ZL, Li K, Zhao SH: MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics 2007, 8:341. BioMed Central Full Text
• [12]An J, Lai J, Lehman ML, Nelson CC: miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 2013, 41(2):727-737.
• [13]Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM: miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011, 39(Web Server issue):W132-W138.
• [14]Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A: NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 2013, 41(Database issue):D991-D995.
• [15]Jayaswal V, Lutherborrow M, Ma DD, Hwa Yang Y: Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data. Nucleic Acids Res 2009, 37(8):e60.
• [16]Li Z, Branham WS, Dial SL, Wang Y, Guo L, Shi L, Chen T: Genomic analysis of microRNA time-course expression in liver of mice treated with genotoxic carcinogen N-ethyl-N-nitrosourea. BMC Genomics 2010, 11:609. BioMed Central Full Text
• [17]Ernst J, Bar-Joseph Z: STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 2006, 7:191. BioMed Central Full Text
• [18]Craig P, Cannon A, Kukla R, Kennedy J: MaTSE: the gene expression time-series explorer. BMC Bioinformatics 2013, 14:S1.
• [19]Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:Article 3. Epub 2004 Feb 12
• [20]Grace C, Nacheva EP: Significance Analysis of Microarrays (SAM) offers clues to differences between the genomes of adult philadelphia positive ALL and the lymphoid blast transformation of CML. Cancer Inform 2012, 11:173-183.
• [21]Leek JT, Monsen E, Dabney AR, Storey JD: EDGE: extraction and analysis of differential gene expression. Bioinformatics 2006, 22(4):507-508.
• [22]Aryee MJ, Gutierrez-Pabello JA, Kramnik I, Maiti T, Quackenbush J: An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics 2009, 10:409. BioMed Central Full Text
• [23]Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139-140.
• [24]Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol 2010, 11(10):R106. BioMed Central Full Text
• [25]SourceForgehttp://sourceforge.net/projects/mirnata webcite
• [26]Garmire LX, Subramaniam S: Evaluation of normalization methods in mammalian microRNA-Seq data. RNA 2012, 18(6):1279-1288.
• [27]Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94. BioMed Central Full Text
• [28]McCormick KP, Willmann MR, Meyers BC: Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. Silence 2011, 2(1):2. BioMed Central Full Text
• [29]Dalgaard P: Statistics and Computing: Introductory Statistics with R. New York: Springer Science; 2002.
• [30]Cer RZ, Herrera-Galeano JE, Anderson JJ, Bishop-Lilly KA, Mokashi VP: Software and Supporting Material for: “mirnaTA: a Bioinformatics Tool for Identifying Differentially Expressed microRNAs in Temporal Studies using Normal Quantile Transformation (NQT)”. GigaScience Database; 2014. http://dx.doi.org/10.5524/100107 webcite
• [31]Caraux G, Pinloche S: PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 2005, 21(7):1280-1281.