ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Realizing the potential of full-length transcriptome sequencing.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
92 citing papers in PubMed.
- High-resolution profiling of bacterial transcriptomes by SEnd-seq.The Biochemical journal · 2026Review
- A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq nanopore long reads data.NAR genomics and bioinformatics · 2026Article
- The circadian isoform landscape of mouse livers.NAR genomics and bioinformatics · 2026Article
- A systematic PCR-based framework for amplification of long and multi-exonic genes in non-model insects: : A case study of Bemisia tabaci Asia II 1.Molecular biology reports · 2026Article
- Eukan: a fully automated nuclear genome annotation pipeline for less studied and divergent eukaryotes.NAR genomics and bioinformatics · 2026Article
- Veterinary Herpesviruses: Experimental Tools for Transcriptomics and Neuroscience.Veterinary sciences · 2026Review
- Review
- A practical guide to targeted single-cell RNA sequencing technologies.Communications biology · 2026Review
- Article
- Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer's Disease Brain.Journal of molecular neuroscience : MN · 2026Article
- Systematic characterization of full-length RNA isoforms in human colorectal cancer at single-cell resolution.Protein & cell · 2025Article
- Long-read RNA sequencing dataset of human pancreatic cancer cell lines.Scientific data · 2025Article
- Article
- High resolution of full-length RNA sequencing deciphers massive transcriptome complexity during zebrafish embryogenesis.BMC biology · 2025Article
- Transcriptomics data for muscle development in Goats.Scientific data · 2025Article
- Unraveling the hidden complexity of cancer through long-read sequencing.Genome research · 2025Review
- Integrating short-read and long-read single-cell RNA sequencing for comprehensive transcriptome profiling in mouse retina.Genome research · 2025Article
- Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Long-read sequencing reveals novel transcript diversity inAnimal cells and systems · 2025Article
- FAM117A and PIGU regulate the trilogy of gastric carcinogenesis.Computational and structural biotechnology journal · 2025Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Long-read sequencing holds great potential for transcriptome analysis because it offers researchers an affordable method to annotate the transcriptomes of non-model organisms. This, in turn, will greatly benefit future work on less-researched organisms like unicellular eukaryotes that cannot rely on large consortia to generate these transcriptome annotations. However, to realize this potential, several remaining molecular and computational challenges will have to be overcome. In this review, we have outlined the limitations of short-read sequencing technology and how long-read sequencing technology overcomes these limitations. We have also highlighted the unique challenges still present for long-read sequencing technology and provided some suggestions on how to overcome these challenges going forward. This article is part of a discussion meeting issue 'Single cell ecology'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.