Podbel’skogo, St. Petersburg, 196608 Russia, e-mail zhukoff01@yahoo.com Acknowledgements: Authors are thankful to E.E. Andronov (ARRIAM, St. Petersburg) for his consultations and fruitful discussions on the issues of «next generation sequencing», to M.N. Povydysh (SPСPA, St. Petersburg) for his assistance in preparing this manuscript, to N.I. Ershov (ICG SB RAN, Novosibirsk) for his assistance in the analysis using bioinformatics methods, and to P. Winter and GenXPro GmbH employees (Frankfurt, Germany) for their assistance in RNA-sequencing. <...> Supported by Russian Science Foundation (grant ¹ 14-24-00135) A b s t r a c t The term «Next Generation Sequencing» refers to modern technologies that help to obtain information about the nucleotide composition of tens and hundreds of millions of sequences in one experiment. <...> NGS technologies are used to solve a wide range of problems (genome sequencing, gene expression assays, development of molecular markers, metagenomic studies of microbial communities, epigenetic studies etc.). <...> One of the major applications of the NGS methods is concerned with analysis of gene expression by sequencing of transcriptome (the whole set of transcribed RNA). <...> The review considers the approaches used for total gene expression analysis by «Next Generation Sequencing» — RNAseq (RNA sequencing) and its modification MACE (Massive Analysis of cDNA Ends). <...> In this modification, developed by GenXPro GmbH (Frankfurt am Main, Germany), for each cDNA molecule only a 100-500 bp fragment (which is adjacent to the 3´-end of the transcript or, in another version, to its 5´-end) is subjected to sequencing; thus, the resolution of the method is increased by several times. <...> In this way, MACE can capture the transcripts with low expression level, which correspond to the key regulatory genes forming the basis of biological processes. <...> Also the review describes functional analysis of RNA sequencing, including the identification of biological patterns based on the detection of differentially expressed genes. <...> The genes and gene products interact with each other to form a structured regulatory network, but the identification and analysis of regulatory networks is a complex task that requires the development of mathematical methods and the accumulation of data on gene expression, localization of gene products and their functional annotation. <...> Thus, the use of NGS for gene expression <...>