The article reviews recent advances in transcriptome assembly, focusing on the challenges and solutions in reconstructing the entire transcriptome from deep RNA sequencing (RNA-seq) data. It highlights the importance of identifying all transcripts, including large and small RNAs, novel transcripts, splicing isoforms, and gene-fusion transcripts. The review discusses three main assembly strategies: reference-based, de novo, and combined approaches. Each strategy is evaluated based on its strengths and weaknesses, particularly in the context of gene-dense and complex transcriptomes. The article also provides practical guidelines for designing RNA-seq experiments, including library construction, sequencing, and data pre-processing. Additionally, it explores the impact of different sequencing platforms, read lengths, and paired-end protocols on assembly quality. Finally, the authors discuss future perspectives, emphasizing the role of high-performance computing and the potential of third-generation sequencing technologies in improving transcriptome assembly.The article reviews recent advances in transcriptome assembly, focusing on the challenges and solutions in reconstructing the entire transcriptome from deep RNA sequencing (RNA-seq) data. It highlights the importance of identifying all transcripts, including large and small RNAs, novel transcripts, splicing isoforms, and gene-fusion transcripts. The review discusses three main assembly strategies: reference-based, de novo, and combined approaches. Each strategy is evaluated based on its strengths and weaknesses, particularly in the context of gene-dense and complex transcriptomes. The article also provides practical guidelines for designing RNA-seq experiments, including library construction, sequencing, and data pre-processing. Additionally, it explores the impact of different sequencing platforms, read lengths, and paired-end protocols on assembly quality. Finally, the authors discuss future perspectives, emphasizing the role of high-performance computing and the potential of third-generation sequencing technologies in improving transcriptome assembly.