RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. This method has already altered our understanding of the complexity and extent of eukaryotic transcriptomes. RNA-Seq provides more precise measurements of transcript levels and isoforms compared to other methods. The article discusses the RNA-Seq approach, its challenges, and the advances made in characterizing several eukaryote transcriptomes. RNA-Seq involves converting RNA into a library of cDNA fragments, which are then sequenced in a high-throughput manner. The resulting reads are aligned to a reference genome or transcriptome to generate a genome-scale transcription map. Key advantages of RNA-Seq include its ability to detect transcripts not corresponding to existing genomic sequences, precise location of transcription boundaries, and low background signal. It also offers a large dynamic range of expression levels and high accuracy and reproducibility. However, RNA-Seq faces challenges such as library construction, informatics, and the need for sufficient sequencing depth to achieve adequate coverage. Despite these challenges, RNA-Seq has provided unprecedented insights into transcriptome organization, including novel transcribed regions, splicing isoforms, and extensive transcript complexity. It can also accurately monitor gene expression dynamics across different conditions and tissues. The future of RNA-Seq lies in targeting more complex transcriptomes and identifying rare RNA isoforms. Technologies like pair-end sequencing, strand-specific sequencing, and longer reads are expected to enhance its capabilities. As sequencing costs continue to decrease, RNA-Seq is likely to replace microarrays for many applications involving transcriptome analysis.RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. This method has already altered our understanding of the complexity and extent of eukaryotic transcriptomes. RNA-Seq provides more precise measurements of transcript levels and isoforms compared to other methods. The article discusses the RNA-Seq approach, its challenges, and the advances made in characterizing several eukaryote transcriptomes. RNA-Seq involves converting RNA into a library of cDNA fragments, which are then sequenced in a high-throughput manner. The resulting reads are aligned to a reference genome or transcriptome to generate a genome-scale transcription map. Key advantages of RNA-Seq include its ability to detect transcripts not corresponding to existing genomic sequences, precise location of transcription boundaries, and low background signal. It also offers a large dynamic range of expression levels and high accuracy and reproducibility. However, RNA-Seq faces challenges such as library construction, informatics, and the need for sufficient sequencing depth to achieve adequate coverage. Despite these challenges, RNA-Seq has provided unprecedented insights into transcriptome organization, including novel transcribed regions, splicing isoforms, and extensive transcript complexity. It can also accurately monitor gene expression dynamics across different conditions and tissues. The future of RNA-Seq lies in targeting more complex transcriptomes and identifying rare RNA isoforms. Technologies like pair-end sequencing, strand-specific sequencing, and longer reads are expected to enhance its capabilities. As sequencing costs continue to decrease, RNA-Seq is likely to replace microarrays for many applications involving transcriptome analysis.