ArticleNAR molecular medicine2026
Transcript-Capture sequencing enriches mRNA of
Article in NAR molecular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Bacterial mRNA Vaccines: Programming Immunity Against Antimicrobial Resistance.Drug development research · 2026Review
- Enriching Bacteria-Specific RNA From Host Samples Before NGS With Transcript-Capture.Bio-protocol · 2026Article
- Article
- Hybrid capture RNA-seq defines temporal gene expression inbioRxiv : the preprint server for biology · 2026Article
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
Abstract
Bacterial gene expression from sites of infection are poorly studied due to low levels of bacterial mRNA present in clinical samples. Here, we develop Transcript-Capture Seq, which uses customizable biotinylated probes generated in-house to enrich bacteria-specific RNA from host samples before Next Generation Sequencing (NGS). This method results in a >200-fold increase in bacterial mRNA reads from mixed samples and allows analysis of the complete bacterial transcriptome from clinical samples. We apply Transcript-Capture to models of tuberculosis (TB) infection as well as sputum samples from TB patients. TB patients exhibit unexplained heterogeneity in disease progression, and the activity of
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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.