ArticleNucleic acids research2022
Rapid and accurate identification of ribosomal RNA sequences via deep learning.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
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86 citing papers in PubMed.
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- Multiple hypervariable markers improve mycobiome classification in metatranscriptome and metagenome data.Communications biology · 2026Article
- Contact Unmodified Antisense DNA Biotechnology (CUADb)-Based Oligonucleotide Insecticides and RNA Biocontrols: Molecular Bases and Potential in Plant Protection.Current issues in molecular biology · 2026Review
- Cell-free genomics reveals fundamental regulatory principles of the Mycobacterium tuberculosis transcription cycle.Molecular cell · 2026Article
- Profiling the epigenomic landscape of late embryonic and adult mouse hind limb muscles.Scientific reports · 2026Article
- Circularization of 23S rRNA but not 16S rRNA within archaeal ribosomes.Genome biology · 2026Article
- The interplay between the marine diazotrophApplied and environmental microbiology · 2026Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences do not include polyadenylation, these cannot be easily removed in library preparation, requiring their post-hoc removal with computational techniques to accelerate and improve downstream analyses. Here, we describe RiboDetector, a novel software based on a Bi-directional Long Short-Term Memory (BiLSTM) neural network, which rapidly and accurately identifies rRNA reads from transcriptomic, metagenomic, metatranscriptomic, noncoding RNA, and ribosome profiling sequence data. Compared with state-of-the-art approaches, RiboDetector produced at least six times fewer misclassifications on the benchmark datasets. Importantly, the few false positives of RiboDetector were not enriched in certain Gene Ontology (GO) terms, suggesting a low bias for downstream functional profiling. RiboDetector also demonstrated a remarkable generalizability for detecting novel rRNA sequences that are divergent from the training data with sequence identities of <90%. On a personal computer, RiboDetector processed 40M reads in less than 6 min, which was ∼50 times faster in GPU mode and ∼15 times in CPU mode than other methods. RiboDetector is available under a GPL v3.0 license at https://github.com/hzi-bifo/RiboDetector.
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