Academic literature on the topic 'Long-read RNA-seq'

Gene expression studies are extremely useful for understanding a broad range of biological, physiological, and molecular responses. The techniques for gene expression reflect differential patterns of gene regulation and have evolved with time from detecting one gene to many genes at a time laterally. Gene expression depends on the spatiotemporal expression in a particular tissue at a given time point and needs critical examination and interpretation. Transcriptome sequencing or RNA-seq using next-generation sequencing (short and long reads) is the most widely deployed technology for accurate quantification of gene expression. According to the biological aim of the experiment, replications, platform, and chemistries, propelling improvement has been demonstrated and documented using RNA-seq in plants, humans, animals, and clinical sciences with respect to gene expression of mRNA, small non-coding, long non-coding RNAs, alternative splice variations, isoform variations, gene fusions, single-nucleotide variants. Integrating transcriptome sequencing with other techniques such as chromatin immunoprecipitation, methylation, genome-wide association studies, manifests insights into genetic and epigenetic regulation. Epi-transcriptome including RNA methylation, modification, and alternative polyadenylation events can also be explored through long-read sequencing. In this chapter, we have presented an account of the basics of gene expression methods, transcriptome sequencing, and the various methodologies involved in the downstream analysis.

Delving into the intricate world of transcriptome analysis, this chapter unfolds the story of gene expression in organisms. The classic DNA microarray and RNA-seq methods have long been the pillars, with RNA-seq taking the spotlight for its superior resolution in understanding dynamic aspects. Yet, tools like Hisat2 and DESeq2, while effective, come with the drawback of being time-consuming and reliant on powerful GPUs. The need for quicker, less resource-intensive techniques has sparked a shift toward simpler R and Python-based tools that not only sidestep GPU dependence but also offer enhanced graphical representations. As we navigate through the content, the chapter draws a vivid comparison between the established tools and the emerging ones, highlighting the pressing need for innovative approaches in transcriptome analysis. The narrative guides readers through the fundamentals, from the Central Dogma’s backstory to the pivotal role of RNA in gene expression and disease. It uncovers the nuances between RNA-Seq and microarray technologies, providing a comprehensive overview of tools for data collection and interpreting changes in gene expression. Our journey extends to the latest breakthroughs, such as the TACITuS platform and the TALON pipeline, tailored for in-depth analysis of transcriptomes using long-read data. The chapter concludes by emphasizing the ever-growing significance of transcriptomics in unraveling complex biological phenomena, with a spotlight on the promising applications of next-generation sequencing. A comprehensive summary ties it all together, detailing the step-by-step protocol of transcriptome analysis, along with insights into current tools, their advantages, and limitations, providing readers with a holistic understanding of their practical application and outcomes.

The completion of the draft and complete human genome has revealed that there are only around 20,000 genes encoding proteins. Nonetheless, these genes can generate eight times more RNA transcript isoforms, while this number is still growing with the accumulation of high-throughput RNA sequencing (RNA-seq) data. In general, over 90% of genes generate various RNA isoforms emerging from variations at the 5′ and 3′ ends, as well as different exon combinations, known as alternative transcription start site (TSS), alternative polyadenylation (APA), and alternative splicing (AS). In this chapter, our focus will be on introducing the significance of these three types of isoform variations in gene regulation and their underlying molecular mechanisms. Additionally, we will highlight the historical, current, and prospective technological advancements in elucidating isoform regulations, from both the computational side such as deep-learning-based artificial intelligence, and the experimental aspect such as the long-read third-generation sequencing (TGS).

Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule se