ArticleMethods (San Diego, Calif.)2018
Using the Ribodeblur pipeline to recover A-sites from yeast ribosome profiling data.
Article in Methods (San Diego, Calif.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- GNN Codon Adjacency Tunes Protein Translation.International journal of molecular sciences · 2024Article
- uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast.Methods in molecular biology (Clifton, N.J.) · 2021Article
- RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data.BMC bioinformatics · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Ribosome profiling has emerged as a powerful technique to study mRNA translation. Ribosome profiling has the potential to determine the relative quantities and locations of ribosomes on mRNA genome wide. Taking full advantage of this approach requires accurate measurement of ribosome locations. However, experimental inconsistencies often obscure the positional information encoded in ribosome profiling data. Here, we describe the Ribodeblur pipeline, a computational analysis tool that uses a maximum likelihood framework to infer ribosome positions from heterogeneous datasets. Ribodeblur is simple to install, and can be run on an average modern Mac or Linux-based laptop. We detail the process of applying the pipeline to high-coverage ribosome profiling data in yeast, and discuss important considerations for potential extension to other organisms.
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