This review summarizes recent advances in transcriptome assembly methods, including reference-based, de novo, and combined strategies. Transcriptome assembly from RNA-seq data is challenging due to the short length of reads and the complexity of transcriptomes. Reference-based assembly uses a known genome to align reads and assemble transcripts, while de novo assembly reconstructs transcripts without a reference genome. Combined strategies merge both approaches. The review discusses the advantages and disadvantages of each method, highlighting the importance of sequencing depth, read length, and the use of paired-end protocols. It also addresses the challenges of assembling complex transcriptomes with alternative splicing and the need for high-quality reference genomes. The review emphasizes the importance of computational resources and the role of parallel computing in handling large datasets. It concludes that advances in sequencing technologies and computing will significantly improve transcriptome assembly, potentially leading to a future where assembly is no longer required.This review summarizes recent advances in transcriptome assembly methods, including reference-based, de novo, and combined strategies. Transcriptome assembly from RNA-seq data is challenging due to the short length of reads and the complexity of transcriptomes. Reference-based assembly uses a known genome to align reads and assemble transcripts, while de novo assembly reconstructs transcripts without a reference genome. Combined strategies merge both approaches. The review discusses the advantages and disadvantages of each method, highlighting the importance of sequencing depth, read length, and the use of paired-end protocols. It also addresses the challenges of assembling complex transcriptomes with alternative splicing and the need for high-quality reference genomes. The review emphasizes the importance of computational resources and the role of parallel computing in handling large datasets. It concludes that advances in sequencing technologies and computing will significantly improve transcriptome assembly, potentially leading to a future where assembly is no longer required.