ArticleBMC bioinformatics2024
PRFect: a tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 10 citations in OpenAlex.
- Protein-inducible ribosomal frameshifting enables programmable translational control for genetic circuit design in Escherichia coli.Journal of biological engineering · 2026Article
- The first RNA viruses detected in a trypanosome.BMC biology · 2026Article
- Characterisation of the NovelInternational journal of molecular sciences · 2025Article
- Characterization of the novel Cutibacterium acnes phage KIT08 and its associated pseudolysogenic bacterial isolate.Archives of microbiology · 2025Article
- Sphae: an automated toolkit for predicting phage therapy candidates from sequencing data.Bioinformatics advances · 2025Article
- A universal pipeline MosaicProt enables large-scale modeling and detection of chimeric protein sequences for studies on programmed ribosomal frameshifting.Computational and structural biotechnology journal · 2025Article
- Discovery of additional genomic segments reveals the fluidity of jingmenvirus genomic organization.Virus evolution · 2025Article
- phage therapy candidates from Sphae: An automated toolkit for predicting sequencing data.bioRxiv : the preprint server for biology · 2024Article
- Parvovirus B19 and Human Parvovirus 4 Encode Similar Proteins in a Reading Frame Overlapping the VP1 Capsid Gene.Viruses · 2024Article
- Knowing and Naming: Phage Annotation and Nomenclature for Phage Therapy.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Review
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundOne of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence and shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain. More importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. There is currently no automated software to predict the occurrence of these programmed ribosomal frameshifts (PRF), and they are currently only identified by manual curation.
resultsHere we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect's strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%. The code for PRFect is freely available and installs with a single terminal command.
conclusionPRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed ribosomal frameshifting in coding genes.
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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.