ArticleBioinformatics (Oxford, England)2016
Prediction of ribosome footprint profile shapes from transcript sequences.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- Riboformer: a deep learning framework for predicting context-dependent translation dynamics.Nature communications · 2024Article
- choros: correction of sequence-based biases for accurate quantification of ribosome profiling data.bioRxiv : the preprint server for biology · 2023Article
- Full-length ribosome density prediction by a multi-input and multi-output model.PLoS computational biology · 2021Article
- RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data.Nucleic acids research · 2020Article
- DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data.BMC bioinformatics · 2019Article
- Ribosome Profiling: Global Views of Translation.Cold Spring Harbor perspectives in biology · 2019Review
- Accurate design of translational output by a neural network model of ribosome distribution.Nature structural & molecular biology · 2018Article
- Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution.Cell systems · 2018Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationRibosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism.
resultsHere, we apply kernel smoothing to construct predictive features and build a sparse model to predict the shape of ribosome footprint profiles from transcript sequences alone. Our results on Saccharomyces cerevisiae data show that the marginal ribosome densities can be predicted with high accuracy. The proposed novel method has a wide range of applications, including inferring isoform-specific ribosome footprints, designing transcripts with fast translation speeds and discovering unknown modulation during translation. AVAILABILITY AND IMPLEMENTATION: A software package called riboShape is freely available at https://sourceforge.net/projects/riboshape CONTACT: yss@berkeley.edu.
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