Abstract
Next-generation sequencing (NGS) is becoming a routine experimental technology. It has been a great success in recent years to profile small-RNA species using NGS. Indeed, a large quantity of small-RNA profiling data has been generated from NGS, and computational methods have been developed to process and analyze NGS data for the purpose of identification of novel and expressed small noncoding RNAs and analysis of their roles in nearly all biological processes and pathways in eukaryotes. We discuss here the computational procedures and major steps for identification of microRNAs and natural antisense transcript-originated small interfering RNAs from NGS small-RNA profiling data.
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Acknowledgment
This work was supported in part by NSF grant DBI-0743797, NIH grants RC1AR058681 and R01GM086412, as well as an internal grant of Fudan University to W. Zhang.
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Zhang, W., Zhou, X., Xia, J., Zhou, X. (2012). Identification of MicroRNAs and Natural Antisense Transcript-Originated Endogenous siRNAs from Small-RNA Deep Sequencing Data. In: Jin, H., Gassmann, W. (eds) RNA Abundance Analysis. Methods in Molecular Biology, vol 883. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-839-9_17
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DOI: https://doi.org/10.1007/978-1-61779-839-9_17
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