Assessing Ribosome Distribution Along Transcripts with Polarity Scores and Regression Slope Estimates
(2021) In Methods in Molecular Biology 2252. p.269-294- Abstract
During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically. Here, we offer a computational protocol for analyzing positional distribution profiles using ribosome profiling (Ribo-Seq) data. The protocol uses papolarity, a new Python toolkit for the analysis of transcript-level short read coverage... (More)
During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically. Here, we offer a computational protocol for analyzing positional distribution profiles using ribosome profiling (Ribo-Seq) data. The protocol uses papolarity, a new Python toolkit for the analysis of transcript-level short read coverage profiles. For a single sample, for each transcript papolarity allows for computing the classic polarity metric which, in the case of Ribo-Seq, reflects ribosome positional preferences. For comparison versus a control sample, papolarity estimates an improved metric, the relative linear regression slope of coverage along transcript length. This involves de-noising by profile segmentation with a Poisson model and aggregation of Ribo-Seq coverage within segments, thus achieving reliable estimates of the regression slope. The papolarity software and the associated protocol can be conveniently used for Ribo-Seq data analysis in the command-line Linux environment. Papolarity package is available through Python pip package manager. The source code is available at https://github.com/autosome-ru/papolarity.
(Less)
- author
- Vorontsov, Ilya E. ; Egorov, Artyom A. LU ; Anisimova, Aleksandra S. ; Eliseeva, Irina A. ; Makeev, Vsevolod J. ; Gladyshev, Vadim N. ; Dmitriev, Sergey E. and Kulakovskiy, Ivan V.
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- Linear regression, Polarity, Ribo-Seq, Ribosome distribution, Ribosome footprint coverage, Ribosome footprint density, Segmentation
- host publication
- Ribosome Profiling : Methods and Protocols - Methods and Protocols
- series title
- Methods in Molecular Biology
- volume
- 2252
- pages
- 269 - 294
- publisher
- Humana Press
- external identifiers
-
- pmid:33765281
- scopus:85103496622
- ISSN
- 1940-6029
- 1064-3745
- ISBN
- 978-1-0716-1149-4
- 978-1-0716-1150-0
- DOI
- 10.1007/978-1-0716-1150-0_13
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2021, Springer Science+Business Media, LLC, part of Springer Nature.
- id
- 2246f8c8-c6de-4c58-bf0d-cafabdf6183a
- date added to LUP
- 2022-08-24 23:15:52
- date last changed
- 2024-04-14 22:19:33
@inbook{2246f8c8-c6de-4c58-bf0d-cafabdf6183a, abstract = {{<p>During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically. Here, we offer a computational protocol for analyzing positional distribution profiles using ribosome profiling (Ribo-Seq) data. The protocol uses papolarity, a new Python toolkit for the analysis of transcript-level short read coverage profiles. For a single sample, for each transcript papolarity allows for computing the classic polarity metric which, in the case of Ribo-Seq, reflects ribosome positional preferences. For comparison versus a control sample, papolarity estimates an improved metric, the relative linear regression slope of coverage along transcript length. This involves de-noising by profile segmentation with a Poisson model and aggregation of Ribo-Seq coverage within segments, thus achieving reliable estimates of the regression slope. The papolarity software and the associated protocol can be conveniently used for Ribo-Seq data analysis in the command-line Linux environment. Papolarity package is available through Python pip package manager. The source code is available at https://github.com/autosome-ru/papolarity.</p>}}, author = {{Vorontsov, Ilya E. and Egorov, Artyom A. and Anisimova, Aleksandra S. and Eliseeva, Irina A. and Makeev, Vsevolod J. and Gladyshev, Vadim N. and Dmitriev, Sergey E. and Kulakovskiy, Ivan V.}}, booktitle = {{Ribosome Profiling : Methods and Protocols}}, isbn = {{978-1-0716-1149-4}}, issn = {{1940-6029}}, keywords = {{Linear regression; Polarity; Ribo-Seq; Ribosome distribution; Ribosome footprint coverage; Ribosome footprint density; Segmentation}}, language = {{eng}}, pages = {{269--294}}, publisher = {{Humana Press}}, series = {{Methods in Molecular Biology}}, title = {{Assessing Ribosome Distribution Along Transcripts with Polarity Scores and Regression Slope Estimates}}, url = {{http://dx.doi.org/10.1007/978-1-0716-1150-0_13}}, doi = {{10.1007/978-1-0716-1150-0_13}}, volume = {{2252}}, year = {{2021}}, }