ArticleGenome research2020
RNA-Bloom enables reference-free and reference-guided sequence assembly for single-cell transcriptomes.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Pharmacogenomic characterization of a uric acid metabolism-related signature associated with prognosis and drug sensitivity in gastric cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.Bioinformatics (Oxford, England) · 2026Article
- Comprehensive assessment of alternative splicing analysis methods for single-cell RNA-seq.iScience · 2026Article
- RAD: A Read-structure Agnostic Demultiplexer for Single-Cell Long-Read Sequencing and Analysis.bioRxiv : the preprint server for biology · 2026Article
- Annotating genomes at increased scale and resolution.Nature reviews. Genetics · 2026Review
- Data-driven AI system for learning how to run transcript assemblers.Genome biology · 2026Article
- Hash functions in nucleotide sequence analysis.Genome research · 2026Review
- A telomere-to-telomere genome assembly of Chinese water deer (Hydropotes inermis).Scientific data · 2026Article
- The chromosomal-level genome assembly and annotation of Phyllospadix iwatensis (Surfgrass).Scientific data · 2026Article
- A comprehensive evaluation of long-read de novo transcriptome assembly.Genome biology · 2026Article
- Integrative Multi-Omics and Machine Learning Identify ID1 as a Candidate Gene Associated with Abdominal Aortic Aneurysm.Current issues in molecular biology · 2026Article
- The Neijiang pig T2T genome reveals domestication history and germplasm traits of Southwest Chinese local breeds.Communications biology · 2026Article
- Identification of Key Targets of Herbal Compounds for Liver Fibrosis Using Network Pharmacology Combined With Transcriptomics.Gastroenterology research and practice · 2026Article
- Amaranth: Enhanced Single-Cell Transcript Assembly via Discriminative Modeling of UMI Reads and Internal Reads.bioRxiv : the preprint server for biology · 2025Article
- Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Identified TIMP1 as a Prognostic Marker in Colorectal Cancer.Immune network · 2025Article
- A genome resource for the marine annelid Platynereis spp.BMC genomics · 2025Article
- Transcriptome assembly at single-cell resolution with Beaver.Bioinformatics (Oxford, England) · 2025Article
- A trove of antiviral TRIM family E3 ligases in reptiles.bioRxiv : the preprint server for biology · 2025Article
- Co-opted and canonical glycerol channels play a major role during anhydrobiosis of an extremophile crustacean.BMC biology · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
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7 authors.
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Abstract
Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%-38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%-11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%-25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.
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