ArticleNature communications2021
Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Unraveling lncRNA diversity at a single cell resolution and in a spatial context across different cancer types.Nature methods · 2026Article
- Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model.Nature methods · 2026Article
- GeneExt: a gene model extension tool for enhanced single-cell RNA-seq analysis.Bioinformatics (Oxford, England) · 2026Article
- The evolution of gene regulation in mammalian cerebellum development.Science (New York, N.Y.) · 2026Article
- BRRIAR lncRNA alters breast cancer risk by modulating interferon signaling in cis and in trans.Molecular cancer · 2026Article
- Article
- Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues.The Journal of pathology · 2025Article
- Single-cell RNA-seq reveals distinct metabolic "microniches" and close host-symbiont interactions in deep-sea chemosynthetic tubeworm.Science advances · 2024Article
- An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome.Nature communications · 2024Article
- CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing in planarians.BMC genomics · 2023Article
- CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing.bioRxiv : the preprint server for biology · 2023Article
- Spatial mapping of the total transcriptome by in situ polyadenylation.Nature biotechnology · 2023Article
- Deep sequencing unveils altered cardiac miRNome in congenital heart disease.Molecular genetics and genomics : MGG · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.
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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.