Abstract
Next-generation sequencing technologies have greatly accelerated the biological and medical progression. As one of the applications, miRNA-Seq is invaluable in detecting and characterizing genome-wide miRNAs of either too high or too low abundance. Besides, it can also be used in detecting novel miRNAs. Here, we describe an ab initio analysis of an example chronic myeloid leukemia miRNA sequencing data set to quantify the global expression of miRNAs, detect differential expression and novel miRNAs, and predict target genes. The run time of this protocol may vary depending on the volume of miRNA sequencing data and available computing resources but takes ~5 h of computing time for typical experiments and less than 1 h of hands-on time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garzon R, Calin GA, Croce CM (2009) MicroRNAs in cancer. Annu Rev Med 60:167–179
Sayed D, Abdellatif M (2011) MicroRNAs in development and disease. Physiol Rev 91:827–887
Bueno MJ, Perez de Castro I, Gomez de Cedron M, Santos J, Calin GA, Cigudosa JC, Croce CM, Fernández-Piqueras J, Malumbres M (2008) Genetic and epigenetic silencing of microRNA-203 enhances ABL1 and BCR-ABL1 oncogene expression. Cancer Cell 13:496–506
Venturini L, Battmer K, Castoldi M, Schultheis B, Hochhaus A, Muckenthaler MU, Ganser A, Eder M, Scherr M (2007) Expression of the miR-17-92 polycistron in chronic myeloid leukemia (CML) CD34+ cells. Blood 109:4399–4405
Wang P, Gu Y, Zhang Q, Han Y, Hou J, Lin L, Wu C, Bao Y, Su X, Jiang M, Wang Q, Li N, Cao X (2012) Identification of resting and type I IFN-activated human NK cell miRNomes reveals microRNA-378 and microRNA-30e as negative regulators of NK cell cytotoxicity. J Immunol 189:211–221
Li Y, Zhang Z, Liu F, Vongsangnak W, Jing Q, Shen B (2012) Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic Acids Res 40:4298–4305
Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N (2012) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40:37–52
Xiong Q, Yang Y, Wang H, Li J, Wang S, Li Y, Yang Y, Cai K, Ruan X, Yan J, Hu S, Fang X (2014) Characterization of miRNomes in acute and chronic myeloid leukemia cell lines. Genomics Proteomics Bioinformatics 12:79–91
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing. EMBnet J 17:3
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106
Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, Mahaffey S, Rossi S, Calin GA, Bemis L, Theodorescu D (2014) The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res 42:e133
Acknowledgements
This research was supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences, Stem Cell and Regenerative Medicine Research (Grant No. XDA01040405), National Programs for High Technology Research and Development (863 Projects, Grant No. 2012AA022502), and National “Twelfth Five-Year” Plan for Science & Technology Support (Grant No. 2013BAI01B09) awarded to X.F.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Yang, Y., Ding, N., Dong, X., Fang, X. (2016). miRNome Analysis of CML Cells. In: Li, S., Zhang, H. (eds) Chronic Myeloid Leukemia. Methods in Molecular Biology, vol 1465. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-4011-0_17
Download citation
DOI: https://doi.org/10.1007/978-1-4939-4011-0_17
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-4009-7
Online ISBN: 978-1-4939-4011-0
eBook Packages: Springer Protocols