Mapping the non-standardized biases of ribosome profiling

Bartholomäus, Alexander; Campo, Cristian Del; Ignatova, Zoya

doi:10.1515/hsz-2015-0197

Cited by 56 publications

(56 citation statements)

References 96 publications

(184 reference statements)

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“…Compounding the variation introduced by the issues already discussed, much of the difference between different ribosome profiling experiments resides in parameters that are often not examined systematically, such as cell lysis conditions, buffer recipes, and the enzymology of library preparation (reviewed in [63]). The extent to which these variables can skew our understanding of the sequence determinants of ribosome footprint distribution across mRNAs was uncovered by O’Connor et al [64], in their effort to develop a statistical model to learn the relationships between sequence local to the ribosome and footprint density.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome-wide measurement of translation by ribosome profiling

McGlincy

Ingolia

2017

Methods

436

504

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Translation is one of the fundamental processes of life. It comprises the assembly of polypeptides whose amino acid sequence corresponds to the codon sequence of an mRNA’s ORF. Translation is performed by the ribosome; therefore, in order to understand translation and its regulation we must be able to determine the numbers and locations of ribosomes on mRNAs in vivo. Furthermore, we must be able to examine their redistribution in different physiological contexts and in response to experimental manipulations. The ribosome profiling method provides us with an opportunity to learn these locations, by sequencing a cDNA library derived from the short fragments of mRNA covered by the ribosome. Since its original description, the ribosome profiling method has undergone continuing development; in this article we describe the method’s current state. Important improvements include: the incorporation of sample barcodes to enable library multiplexing, the incorporation of unique molecular identifiers to enable to removal of duplicated sequences, and the replacement of a gel-purification step with the enzymatic degradation of unligated linker.

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Section: Discussionmentioning

confidence: 99%

Transcriptome-wide measurement of translation by ribosome profiling

McGlincy

Ingolia

2017

Methods

436

504

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“…27 Moreover, the use of translation inhibitors as well as differences in sample preparation and downstream analyses can introduce biases in ribosome footprinting assays that might not accurately reflect the translational state in an unperturbed cell. [28][29][30] To distinguish actual translation events from technical noise, a series of data analysis tools have been developed. These computational approaches use certain features like Ribosome Protected Fragment (RPF) abundance, length and trinucleotide periodicity, positioning of the ORF within a transcript and responsiveness to translation inhibitors to help detect "real" translation and eliminate technical noise.…”

Section: Introductionmentioning

confidence: 99%

Decoding sORF translation – from small proteins to gene regulation

2016

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Translation is best known as the fundamental mechanism by which the ribosome converts a sequence of nucleotides into a string of amino acids. Extensive research over many years has elucidated the key principles of translation, and the majority of translated regions were thought to be known. The recent discovery of wide-spread translation outside of annotated protein-coding open reading frames (ORFs) came therefore as a surprise, raising the intriguing possibility that these newly discovered translated regions might have unrecognized protein-coding or gene-regulatory functions. Here, we highlight recent findings that provide evidence that some of these newly discovered translated short ORFs (sORFs) encode functional, previously missed small proteins, while others have regulatory roles. Based on known examples we will also speculate about putative additional roles and the potentially much wider impact that these translated regions might have on cellular homeostasis and gene regulation.

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“…Ribo-seq data is characterized by highly heterogeneous coverage, containing peaks and valleys that can be caused by ribosome pausing, technical artifacts, and sensitivity to experimental conditions. 51 Indeed, poor reproducibility of ribosome profiles at the individual transcript level is a known issue with Ribo-seq datasets. 15 As such, a variety of normalization methods and data analysis techniques have been developed to extract biological insights from noisy datasets.…”

Section: Discussionmentioning

confidence: 99%

Multimapping confounds ribosome profiling analysis: A case‐study of the Hsp90 molecular chaperone

2019

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Ribosome profiling (Ribo‐seq) can potentially provide detailed information about ribosome position on transcripts and estimates of protein translation levels in vivo. Hsp90 chaperones, which play a critical role in stress tolerance, have characteristic patterns of differential expression under nonstressed and heat shock conditions. By analyzing published Ribo‐seq data for the Hsp90 chaperones in S. cerevisiae, we find wide‐ranging artifacts originating from “multimapping” reads (reads that cannot be uniquely assigned to one position), which constitute ~25% of typical S. cerevisiae Ribo‐seq datasets and ~80% of the reads from HEK293 cells. Estimates of Hsp90 protein production as determined by Ribo‐seq are reproducible but not robust, with inferred expression levels that can change 10‐fold depending on how multimapping reads are processed. The differential expression of Hsp90 chaperones under nonstressed and heat shock conditions creates artificial peaks and valleys in their ribosome profiles that give a false impression of regulated translational pausing. Indeed, we find that multimapping can even create an appearance of reproducibility to the shape of the Hsp90 ribosome profiles from biological replicates. Adding further complexity, this artificial reproducibility is dependent on the computational method used to construct the ribosome profile. Given the ubiquity of multimapping reads in Ribo‐seq experiments and the complexity of artifacts associated with multimapping, we developed a publicly available computational tool to identify transcripts most at risk for multimapping artifacts. In doing so, we identify biological pathways that are enriched in multimapping transcripts, meaning that particular biological pathways will be highly susceptible to multimapping artifacts.

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Mapping the non-standardized biases of ribosome profiling

Cited by 56 publications

References 96 publications

Transcriptome-wide measurement of translation by ribosome profiling

Transcriptome-wide measurement of translation by ribosome profiling

Decoding sORF translation – from small proteins to gene regulation

Multimapping confounds ribosome profiling analysis: A case‐study of the Hsp90 molecular chaperone

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