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pmid: 35687114
pmc: PMC9458444
Abstract Recent studies have revealed multiple mechanisms that can lead to heterogeneity in ribosomal composition. This heterogeneity can lead to preferential translation of specific panels of mRNAs, and is defined in large part by the ribosomal protein (RP) content, amongst other things. However, it is currently unknown to what extent ribosomal composition is heterogeneous across tissues, which is compounded by a lack of tools available to study it. Here we present dripARF, a method for detecting differential RP incorporation into the ribosome using Ribosome Profiling (Ribo-seq) data. We combine the ‘waste’ rRNA fragment data generated in Ribo-seq with the known 3D structure of the human ribosome to predict differences in the composition of ribosomes in the material being studied. We have validated this approach using publicly available data, and have revealed a potential role for eS25/RPS25 in development. Our results indicate that ribosome heterogeneity can be detected in Ribo-seq data, providing a new method to study this phenomenon. Furthermore, with dripARF, previously published Ribo-seq data provides a wealth of new information, allowing the identification of RPs of interest in many disease and normal contexts. dripARF is available as part of the ARF R package and can be accessed through https://github.com/fallerlab/ARF.
EXPRESSION, Ribosomal Proteins, 570, Biochemistry & Molecular Biology, PROTEINS, Gene Expression, Biochemistry & Proteomics, Ecology,Evolution & Ethology, REVEALS, Humans, RNA, Messenger, Computational & Systems Biology, Science & Technology, FOS: Clinical medicine, Stem Cells, Neurosciences, GENE, MESSENGER-RNA TRANSLATION, RNA, Ribosomal, Methods Online, Life Sciences & Biomedicine, Ribosomes, Genetics & Genomics
EXPRESSION, Ribosomal Proteins, 570, Biochemistry & Molecular Biology, PROTEINS, Gene Expression, Biochemistry & Proteomics, Ecology,Evolution & Ethology, REVEALS, Humans, RNA, Messenger, Computational & Systems Biology, Science & Technology, FOS: Clinical medicine, Stem Cells, Neurosciences, GENE, MESSENGER-RNA TRANSLATION, RNA, Ribosomal, Methods Online, Life Sciences & Biomedicine, Ribosomes, Genetics & Genomics
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |