Variational inference for detecting differential translation in ribosome profiling studies
Translational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1178508/full |
| _version_ | 1827918115006054400 |
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| author | David C. Walker Zachary R. Lozier Ran Bi Pulkit Kanodia W. Allen Miller Peng Liu |
| author_facet | David C. Walker Zachary R. Lozier Ran Bi Pulkit Kanodia W. Allen Miller Peng Liu |
| author_sort | David C. Walker |
| collection | DOAJ |
| description | Translational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that are being actively translated. Existing methods for Ribo-seq data analysis either ignore the pairing structure in the experimental design or treat the paired samples as fixed effects instead of random effects. To address these issues, we propose a hierarchical Bayesian generalized linear mixed effects model which incorporates a random effect for the paired samples according to the experimental design. We provide an analytical software tool, “riboVI,” that uses a novel variational Bayesian algorithm to fit our model in an efficient way. Simulation studies demonstrate that “riboVI” outperforms existing methods in terms of both ranking differentially translated genes and controlling false discovery rate. We also analyzed data from a real ribosome profiling experiment, which provided new biological insight into virus-host interactions by revealing changes in hormone signaling and regulation of signal transduction not detected by other Ribo-seq data analysis tools. |
| first_indexed | 2024-03-13T03:38:08Z |
| format | Article |
| id | doaj.art-189100a95ff1446c8eeb18a3c22c2623 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-13T03:38:08Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-189100a95ff1446c8eeb18a3c22c26232023-06-23T12:55:21ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-06-011410.3389/fgene.2023.11785081178508Variational inference for detecting differential translation in ribosome profiling studiesDavid C. Walker0Zachary R. Lozier1Ran Bi2Pulkit Kanodia3W. Allen Miller4Peng Liu5Department of Statistics, Iowa State University, Ames, IA, United StatesDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United StatesDepartment of Statistics, Iowa State University, Ames, IA, United StatesDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United StatesDepartment of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United StatesDepartment of Statistics, Iowa State University, Ames, IA, United StatesTranslational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that are being actively translated. Existing methods for Ribo-seq data analysis either ignore the pairing structure in the experimental design or treat the paired samples as fixed effects instead of random effects. To address these issues, we propose a hierarchical Bayesian generalized linear mixed effects model which incorporates a random effect for the paired samples according to the experimental design. We provide an analytical software tool, “riboVI,” that uses a novel variational Bayesian algorithm to fit our model in an efficient way. Simulation studies demonstrate that “riboVI” outperforms existing methods in terms of both ranking differentially translated genes and controlling false discovery rate. We also analyzed data from a real ribosome profiling experiment, which provided new biological insight into virus-host interactions by revealing changes in hormone signaling and regulation of signal transduction not detected by other Ribo-seq data analysis tools.https://www.frontiersin.org/articles/10.3389/fgene.2023.1178508/fullriboVIvariational inferencevariational Bayes (VB)ribosome profiling (Ribo-seq)next-generation sequencingRNA sequencing (RNA-seq) |
| spellingShingle | David C. Walker Zachary R. Lozier Ran Bi Pulkit Kanodia W. Allen Miller Peng Liu Variational inference for detecting differential translation in ribosome profiling studies Frontiers in Genetics riboVI variational inference variational Bayes (VB) ribosome profiling (Ribo-seq) next-generation sequencing RNA sequencing (RNA-seq) |
| title | Variational inference for detecting differential translation in ribosome profiling studies |
| title_full | Variational inference for detecting differential translation in ribosome profiling studies |
| title_fullStr | Variational inference for detecting differential translation in ribosome profiling studies |
| title_full_unstemmed | Variational inference for detecting differential translation in ribosome profiling studies |
| title_short | Variational inference for detecting differential translation in ribosome profiling studies |
| title_sort | variational inference for detecting differential translation in ribosome profiling studies |
| topic | riboVI variational inference variational Bayes (VB) ribosome profiling (Ribo-seq) next-generation sequencing RNA sequencing (RNA-seq) |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1178508/full |
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