RNA-Seq is a revolutionary method for transcriptomics that uses deep sequencing technologies to profile the transcriptome. It provides a more precise measurement of transcript levels and isoforms than other methods. This article describes RNA-Seq, its challenges, and advances in characterizing eukaryotic transcriptomes. The transcriptome is the complete set of transcripts in a cell, and understanding it is essential for interpreting the genome and revealing molecular components of cells and tissues. Key aims of transcriptomics include cataloging all transcript species, determining gene transcriptional structure, and quantifying transcript expression levels during development and under different conditions. Various technologies have been developed to deduce and quantify the transcriptome, including hybridization- and sequence-based approaches. Hybridization-based approaches are high throughput but have limitations such as reliance on existing genome knowledge and high background levels. Sequence-based approaches, like SAGE, CAGE, and MPSS, are high throughput and provide precise gene expression levels but are limited by the need for expensive Sanger sequencing and the inability to distinguish isoforms. Recent developments in high-throughput DNA sequencing have provided a new method for mapping and quantifying transcriptomes, with RNA-Seq offering clear advantages over existing approaches. RNA-Seq has been applied to various organisms, including yeast, Arabidopsis, mouse, and human. It allows high-throughput and quantitative analysis of the transcriptome, providing single-base resolution for annotation and genome-scale gene expression levels at a lower cost than tiling arrays or Sanger EST sequencing. RNA-Seq has several advantages over existing technologies, including the ability to detect transcripts not corresponding to existing genomic sequences, low background signal, and a large dynamic range of expression levels. It can reveal precise transcription boundaries and sequence variations, such as SNPs, in transcribed regions. RNA-Seq is also highly accurate for quantifying expression levels and has high reproducibility for both technical and biological replicates. Challenges for RNA-Seq include library construction, which involves several manipulation stages that can complicate transcript profiling. Bioinformatics challenges include the development of efficient methods for storing, retrieving, and processing large data sets. Coverage versus cost is another important issue, with greater coverage requiring more sequencing depth. However, RNA-Seq provides a more comprehensive view of the transcriptome, revealing novel transcribed regions and splicing isoforms, and has enabled the discovery of extensive transcript complexity. RNA-Seq has provided new insights into the transcriptome, including the mapping of gene and exon boundaries, extensive transcript complexity, and novel transcription. It has also been used to define transcription levels, revealing dynamic changes in gene expression across different tissues and conditions. RNA-Seq is expected to replace microarrays for many applications involving transcriptome structure and dynamics. Future directions for RNA-Seq include targeting more complex transcriptomes to identify and track rare RNA isoforms. Technologies such as pair-end sequencing, strand-specific sequencing, and longer reads will help achieve this goal. As sequencing costs continue to fall,RNA-Seq is a revolutionary method for transcriptomics that uses deep sequencing technologies to profile the transcriptome. It provides a more precise measurement of transcript levels and isoforms than other methods. This article describes RNA-Seq, its challenges, and advances in characterizing eukaryotic transcriptomes. The transcriptome is the complete set of transcripts in a cell, and understanding it is essential for interpreting the genome and revealing molecular components of cells and tissues. Key aims of transcriptomics include cataloging all transcript species, determining gene transcriptional structure, and quantifying transcript expression levels during development and under different conditions. Various technologies have been developed to deduce and quantify the transcriptome, including hybridization- and sequence-based approaches. Hybridization-based approaches are high throughput but have limitations such as reliance on existing genome knowledge and high background levels. Sequence-based approaches, like SAGE, CAGE, and MPSS, are high throughput and provide precise gene expression levels but are limited by the need for expensive Sanger sequencing and the inability to distinguish isoforms. Recent developments in high-throughput DNA sequencing have provided a new method for mapping and quantifying transcriptomes, with RNA-Seq offering clear advantages over existing approaches. RNA-Seq has been applied to various organisms, including yeast, Arabidopsis, mouse, and human. It allows high-throughput and quantitative analysis of the transcriptome, providing single-base resolution for annotation and genome-scale gene expression levels at a lower cost than tiling arrays or Sanger EST sequencing. RNA-Seq has several advantages over existing technologies, including the ability to detect transcripts not corresponding to existing genomic sequences, low background signal, and a large dynamic range of expression levels. It can reveal precise transcription boundaries and sequence variations, such as SNPs, in transcribed regions. RNA-Seq is also highly accurate for quantifying expression levels and has high reproducibility for both technical and biological replicates. Challenges for RNA-Seq include library construction, which involves several manipulation stages that can complicate transcript profiling. Bioinformatics challenges include the development of efficient methods for storing, retrieving, and processing large data sets. Coverage versus cost is another important issue, with greater coverage requiring more sequencing depth. However, RNA-Seq provides a more comprehensive view of the transcriptome, revealing novel transcribed regions and splicing isoforms, and has enabled the discovery of extensive transcript complexity. RNA-Seq has provided new insights into the transcriptome, including the mapping of gene and exon boundaries, extensive transcript complexity, and novel transcription. It has also been used to define transcription levels, revealing dynamic changes in gene expression across different tissues and conditions. RNA-Seq is expected to replace microarrays for many applications involving transcriptome structure and dynamics. Future directions for RNA-Seq include targeting more complex transcriptomes to identify and track rare RNA isoforms. Technologies such as pair-end sequencing, strand-specific sequencing, and longer reads will help achieve this goal. As sequencing costs continue to fall,