- Open Access
Estimation of ribosome profiling performance and reproducibility at various levels of resolution
© Diament and Tuller. 2016
Received: 3 February 2016
Accepted: 29 April 2016
Published: 10 May 2016
Ribosome profiling (or Ribo-seq) is currently the most popular methodology for studying translation; it has been employed in recent years to decipher various fundamental gene expression regulation aspects.
The main promise of the approach is its ability to detect ribosome densities over an entire transcriptome in high resolution of single codons. Indeed, dozens of ribo-seq studies have included results related to local ribosome densities in different parts of the transcript; nevertheless, the performance of Ribo-seq has yet to be quantitatively evaluated and reported in a large-scale multi-organismal and multi-protocol study of currently available datasets.
Here we provide the first objective evaluation of Ribo-seq at the resolution of a single nucleotide(s) using clear, interpretable measures, based on the analysis of 15 experiments, 6 organisms, and a total of 612, 961 transcripts. Our major conclusion is that the ability to infer signals of ribosomal densities at nucleotide scale is considerably lower than previously thought, as signals at this level are not reproduced well in experimental replicates. In addition, we provide various quantitative measures that connect the expected error rate with Ribo-seq analysis resolution.
The analysis of Ribo-seq data at the resolution of codons and nucleotides provides a challenging task, calls for task-specific statistical methods and further protocol improvements. We believe that our results are important for every researcher studying translation and specifically for researchers analyzing data generated by the Ribo-seq approach.
This article was reviewed by Dmitrij Frishman, Eugene Koonin and Frank Eisenhaber.
Translation has a major role in the regulation of gene expression and significantly affects various fundamental intracellular processes and biomedical phenomena [1–7]. It is an energetically most costly process, and each of its initiation, elongation and termination steps is tightly regulated [8, 9]. The most prominent experimental technique for studying translation in recent years has been ribosome profiling (RP; or Ribo-seq) . This approach enables high-throughput monitoring of ribosomal density along genes by utilizing deep sequencing methods and has been employed to decipher fundamental gene expression regulation aspects in recent years [10–16].
Ribosome profiling is based on deep-sequencing of ribosome protected mRNA fragments from living cells, such that the sequence of each fragment indicates the position of a translating ribosome on the transcript . The experiment comprises of the following main steps: preparation of the biological samples; sample lysis; nuclease footprinting, in which mRNA that is not protected by ribosomes is digested; ribosome (monosome) recovery; linker ligation; rRNA depletion; library sequencing, followed by bioinformatics analysis of the sequences . Various variants of the experimental protocol have been developed, and many steps in the protocol need to be optimized according to the relevant organism and experimental system . Specifically, it has been shown that the choice methods for translation inhibition [19, 20], RNA digestion enzyme and concentration [17, 18], monosome purification  and rRNA depletion [18, 20] all affect the quality of the resultant data. Moreover, several methods have been applied for mapping the sequenced ribosome protected fragments, and specifically the location of the A-site (or P-site) of the ribosome, to the genome [10, 17, 18, 21–23].
It has been suggested, by utilizing various methods as well as RP, that the speed by which ribosomes progress along the mRNA is affected by different local features of the coding sequence [24, 25]. However, despite its promising throughput, analysis of RP data has led to contradictory conclusions between studies, such as the heating the debate around the determinants of ribosome elongation speed. These include, among others, the following issues: wobble base-pairing was suggested to slow elongation down in C. elegans and human , in agreement with previous (non-RP) experiments [27, 28], but no evidence for this was found in recent studies that analyzed S. cerevisiae profiles [21, 29]. Positively-charged amino acids were shown to slow elongation down in multiple organisms [25, 30], in agreement with previous experiments , but no evidence for this was found in a recent study . The local secondary structure of the mRNA was shown to have a relation between its folding energy and elongation rate [25, 32, 33], in agreement with previous reports , but no evidence for this was found in other studies [21, 30]. Finally, the effect of optimal/non-optimal codons on elongation rate and the relation between the latter and tRNA abundance has been reported [11, 35] and denied [21, 30, 36–38], while being verified by other experimental means [39–42].
While the consistency and reproducibility of RP estimation over entire coding regions was provided in the first paper about this method , no similar analysis has been provided for RP estimations in local regions of the coding region, and particularly not in a large-scale approach encompassing multiple datasets in various organisms and based on various conventional protocols. Thus, the performance of the RP method has yet to be accurately/objectively and thoroughly evaluated. The aim of the current study is to provide for the first time such an objective evaluation which should be robust to the different RP analyses approaches and simple to interpret. In addition, we discuss how our analysis can be used as a tool in future studies of local translation aspects via RP.
To this end, we analyze multiple RP datasets containing experimental replicates in order to determine the consistency and reproducibility of the profiles in closely related repetitions. We show that in most of the studied experiments to date, the level of reproducibility in measured ribosomal densities at nucleotide (or a few nucleotides) scale is considerably lower than previously thought, and argue that some of the aforementioned contradictions may be attributed to the resolution and relatively high ‘noise’ levels in RP data when studying ribosome densities in short fragments of the coding regions. We believe that our results are important for every researcher studying translation and specifically for researchers analyzing data generated by the RP approach.
The robustness of local RP measurements is usually more than one order of magnitude lower than global RP measurements
Estimation of the increase in local RP robustness as a function of the level of resolution
Typically 30 % of the RP extreme peaks are reproducible
It is important to mention that we limited our analyses only to a number of aspects that may affect reproducibility. The variance between the studied datasets suggests that many other factors play a significant role in determining the consistency between replicates and the conclusions of different studies. Among others, additional noise and biases may rise from various further sources: from steps in the experimental protocol such as elongation halting [19, 46], RNA digestion, rRNA filtering, etc.; from genome construction and annotations; from read mapping biases; from analysis of a (very) small subset of reliable genes. Thus, as the analysis of the datasets was performed here in a unified manner (where methods usually vary between studies) and focused on replicates from the same experiment (conducted in very similar conditions), the results reported here are only an upper bound on reproducibility of Ribo-seq analysis results, which is expected to be much lower in practice (specifically when comparing the results obtained based on different experimental protocols and computational procedures).
Our study demonstrates that usually we should be very cautious when analyzing RP at the intra-coding region nucleotide(s) level; if such an analysis is performed it should be based on statistical approaches tailored for dealing with this challenging data or should include various filtering steps. We also suggest to evaluate the expected reproducibility before starting the analysis/experiments, as described here.
Indeed, more elaborate models can be utilized to deal with bias and noise in the data without discarding information. Ingolia et al.  improved the mapping of the A/P-sites by estimating the location of the site along reads that mapped directly upstream the start/sop codons. Oh et al.  assigned ribosome protected footprints in 1–16 nt long smoothed footprints, depending on the footprint length, thus adjusting the effective resolution of the profiles. Artieri and Fraser  performed bias correction by normalizing the observed RP read counts using the corresponding RNA-seq read counts at the same positions. Recently, a multi-scale approach for analyzing RP profiles at an adaptive resolution while correcting for biases has been proposed by Gritsenko et al. . In Dana and Tuller  the noise in RP read counts was modeled as a combination of independent random variables (signal and noise), in order to filter out the latter.
One possible approach to alleviate the issues discussed here is to conduct larger/high-coverage experiments, as we show that reproducibility is strongly correlated with depth and coverage. Sequencing depth can also be partially increased by improved preparation of the RP library in order to avoid contamination, e.g., by rRNA fragments . However, it should be noted that the plots in Fig. 5 are in logarithmic scale, and the reproducibility does not grow very quickly. For instance, in order to achieve an expected correlation of 0.9 between replicates, according to Fig. 5b, we would need a sequencing depth of 105 reads per base. Such a transcriptome-wide sequencing depth would require approximately 400 M mappable reads for a small transcriptome like E. coli’s, but closer to 4,000 M mappable reads for the human, mouse or zebrafish transcriptomes – 2-3 orders of magnitude higher than recently published RP papers. Authors should be encouraged to report the extent and scale of their experiments clearly in every study; this is specifically important when local nucleotide-level signals are reported. Another approach that is more readily available is rigorous statistical handling of the data. The experience gained since ribosome profiling was first proposed has led to the development of a number of techniques to reduce noise in the data. The most common of which is gene filtering, either according to read count threshold [10, 14, 23, 36, 38, 43, 48, 49], coverage threshold [11, 13], or by comparing to a reference null distribution . Reads are usually filtered according to their length, with approaches that vary from strict [30, 37] to more relaxed ones . Acceptable alignments to the genome are also subject to constraints, from 0 mismatches and unique alignment [21, 37], to 2 mismatches and handling of multiple alignments . Another form of filtering is ignoring the 5’-end and/or 3’-end of ORF [11, 13, 21]. When detecting transcripts with differential changes in read-counts, genes with inconsistent results between replicates can be filtered out .
Here we provide an additional approach for handling data as the plots reported here can be used for evaluation of the RP data and for choosing the resolution of the analyses according to the desired reproducibility level.
The challenges in analyzing RP data that arise from this report call for the continuation of development and enhancement of robust and tailored statistical methods.
In this study we provide, for the first time, an objective evaluation of RP reproducibility at different levels of intra-coding region resolution for various organisms and RP protocols.
Our main conclusions are that that the level of noise in measured ribosomal densities at nucleotide(s) scale is considerably higher than previously thought, as signals at this level are not reproduced well in experimental replicates. Our analyses indicate that this holds even when ignoring 80 % of the genes with lower expression levels in the genome. Furthermore, various protocol variants, including such that avoided pre-treatment of the samples with cycloheximide before lysis, showed similar levels of performance in our analyses. This issue has important implications to many of the intra-coding region analyses done on ribosome profiling data, and may explain some of the discrepancies between the conclusions of different studies in the field; nevertheless, it hasn’t been systematically studied and discussed in the literature.
Transcript sequences were obtained from EnsEMBL : S. cerevisiae (R64-1-1, release 78, 12/2014), M. musculus (GRCm38, release 78, 12/2014), H. sapiens (GRCh38, release 80, 5/2015), D. rerio (GRCz10, release 81, 7/2015), C. elegans (WBcel235, release 81, 7/2015), E. coli (K-12 MG1655 release 121, accessed 28/07/15). We used annotated UTRs where available, and otherwise used up to 100 nt upstream and downstream the ORF that didn’t overlap another ORF. Each gene was represented by its longest annotated transcript.
post GMPPNP+ Chloramphenicol
pre/post Chloramphenicol rapid filtration
mESC, noLIF-36 h
YPD, mixed \w S. paradoxus
Data analysis was performed in Python 3.4 (Anaconda distribution, version 2.3.0) and plotting was done using the Seaborn package (version 0.7.0). All tests in this study are based on comparing a pair of replicates. To this end, we generated all unique pairs between experimental replicates (a total of 26 pairs from 15 publications/datasets). Some of the analyses, such as coverage and depth calculation, were performed independently for each replicate and then averaged or summed to assign the pair with a single value (for example, see Fig. 5a, and details below). When taking the subset of highly expressed genes, we analyzed genes that were in the top 20 % of genes’ ribosomal densities in both replicates. All analyses were performed only on ORFs.
All correlations are Spearman rank correlations unless stated otherwise. Ribo-seq read count densities (RCD) were computed by summing all reads that mapped to the ORF and dividing by ORF length (see Fig. 1a-c). Per-position correlations were computed separately for each gene by computing the correlation between two replicate profiles, including all positions in the ORF. The median correlation of all genes in the genome was used as a summary statistic in Figs. 3c and 4b.
Smoothing was done using a sliding window in various sizes. Each “nucleotide” in the smoothed profile represents the average over 3, 10, 30, 100 or 300 nucleotides around it in the raw profile (see Fig. 4a). Averaging was calculated uniformly over the window. Genes shorter than the window were discarded.
Depth and coverage
Depth was defined as the average number of times every nucleotide in the genome appeared in the 5’ of a ribosome protected fragment (read). That is, the read count density of the genome (total read count divided by the total length of ORFs). This value is directly related to the sequencing depth of the experiment. When computed for individual genes (see Fig. 5c), the read count density of the gene (total read count divided by ORF length) was utilized as depth. In order to represent a replicate pair we utilized their total depth, i.e., the sum of their depths.
Coverage was defined as the percentage of non-zero positions in a gene, and the total coverage was defined as the average coverage of all genes. For a replicate pair, the coverage was the average coverage of the two. This value is not only related to sequencing depth, but also to the number of unique ribosome protected fragments that were sampled in the library (which is related to the number of cells, number of mRNA molecules and number of ribosomes on each molecule).
Peak detection score
We defined a peak detection threshold that was calculated for each gene independently. The threshold was set to be 3 standard deviations above the median, as calculated over all non-zero positions in the gene. When testing for peak detection reproducibility we accepted the reproduction of a peak if the other replicate had a detected peak within 3 nt upstream or downstream the original peak. The peak detection score is the probability of a detected peak to be reproduced, as estimated by the fraction of all identified peaks in the transcriptome that were successfully reproduced in the two replicates (see Fig. 6a).
Reviewer’s report 1: Dmitrij Frishman, Technische Universität München, Germany
This is a very useful and timely study, which might explain, at least to some extent, the recent controversial results in analyzing various aspects of mRNA structure, function, and evolution based on ribosomal profiling data. The paper is very well written and its technical quality is very good.
What I found a little confusing is the statement on page 7, which seems to suggests that reproducibility of the results quickly grows with increased sequencing depth. What are the implications of this finding? Does that mean that the problem can be fixed by deeper sequencing?
The authors implemented their own pipeline for processing NGS data and obtaining ribosomal occupancy profiles from each experiment. I would be interested to know whether the profiles they derived are similar to those provided by the authors of the original experimental studies. This could provide some insight as to how much depends on the particular approach for processing reads.
Would it make sense to present results separately for technical and biological replicates (Table 1)?
The reproducibility of the results indeed grows with the sequencing depth/percentage of sequence covered by reads. However, it should be noted that the plots in Fig. 5 are in logarithmic scale, and the reproducibility does not grow very quickly. For instance, in order to achieve an expected correlation of 0.9 between replicates, according to Fig. 5 b, we would need a sequencing depth of 105 reads per base. Such a transcriptome-wide sequencing depth would require approximately 400 M mappable reads for a small transcriptome like E. coli’s, but closer to 4000 M mappable reads for the human, mouse or zebrafish transcriptomes – 2-3 orders of magnitude higher than recently published papers. Finally, there are many additional sources of error/bias in RP experiments, as discussed in the manuscript.
We included in the revised manuscript a comparison between the profiles we generated and two previously published profiles in S. cerevisiae and in D. rerio (see Additional file 1 ). The results show a high correlation between the two mappings in both cases. However, it should be noted that in most cases aligned/further-processed profiles were not provided by the authors. In addition, even if such profiles exist, they were often generated using different reference genomes/gene annotations as these are frequently updated. The comparison is further complicated when additional non-trivial steps were taken to produce the profiles, such as smoothing or various methods for the estimation of the location of the A-site of the ribosome.
Provided that only two of the replicates are technical replicates, we leave it to the reader.
We fixed Fig. 1 D where one red dot covered a blue dot with a similar y-axis value.
We added a clearer description to the legend of Fig. 2 .
The area denotes the 95 % confidence interval of the regression parameters. We added a clarification to the figure legend.
Reviewer’s report 2: Eugene Koonin, National Institutes of Health, United States
Reviewer summary: In this straightforward paper, Diament and Tuller analyze the consistency between experimental replicates in ribosomal profiling experiments and show that it is high at the level of whole genes but low at the level of individual nucleotides or short segments. Thus, at present the RP data appear not to be truly informative for the interpretation of the role of local features (such as, for instance, short hairpins in mRNA) which could explain various contradictions that have accumulated in the literature. Quite strikingly, the local accuracy is shown to be low even for subsets of highly expressed genes. As far as I can see, the analysis is well done and carefully presented. The authors make several suggestions how to extract more information from RP results without discarding the data or seeking a major experimental breakthrough. I believe these findings are important for any researcher involved in RP experiments or using the RP data for other analysis, which is a large and growing segment of the scientific community.
Reviewer recommendations to authors: I think all is well done, no suggestions.
Minor issues: No such issues.
Authors’ response: We thank Prof. Koonin for his endorsement.
Reviewer’s report 3: Frank Eisenhaber, Agency for Science, Technology and Research Singapore
Reviewer summary: The authors review the ribosome profiling (ribo-seq) methodology as a tool for studying translation and the biological results obtained with it as reported in recent literature.
The article is written as if all readers are well informed about the ribo-seq method and its possible applications. I suggest the authors to add another section at the beginning of the results where they describe the procedure in detail including the post-experimental data processing and conclusion chain (instead of just referring to the original articles. Along this description, the authors can critically remark where are issues of complications with regard to experimental or numerical inaccuracies, assumptions that are not fully supported by evidence, etc. In the later part of the MS, these issues can then be argued with the help of data taken from the 15 studies used.
What is labelled “conclusions” in the MS, is rather an elongated discussion section.
Minor issues: none.
We added a description of the ribo-seq method to the introduction of the paper, along with references to recent papers that review the experimental protocol in detail and point to sensitive steps in the process.
We re-organized the manuscript and divided the last section into discussion and conclusions.
Reviewer’s report 1: Dmitrij Frishman, Technische Universität München, Germany
I am happy with the revision.
Reviewer’s report 2: Eugene Koonin, National Institutes of Health, United States
Reviewer’s report 3: Frank Eisenhaber, Agency for Science, Technology and Research Singapore
It appears to me that Dima Frishman has been labelled as reviewer two times in the answers. I guess that my name should appear as referee 3.
Authors’ response: Sorry. This was fixed.
AD is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. This study was supported in part by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University. The funding bodies took no part in the design and analysis of the study or in the writing of the manuscript.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Vogel C, Abreu R de S, Ko D, Le S-Y, Shapiro BA, Burns SC, et al. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol. 2010;6:400.View ArticlePubMedPubMed CentralGoogle Scholar
- Tian Q, Stepaniants SB, Mao M, Weng L, Feetham MC, Doyle MJ, et al. Integrated genomic and proteomic analyses of gene expression in Mammalian cells. Mol Cell Proteomics. 2004;3:960–9.View ArticlePubMedGoogle Scholar
- Calkhoven CF, Müller C, Leutz A. Translational control of gene expression and disease. Trends Mol Med. 2002;8:577–83.View ArticlePubMedGoogle Scholar
- Silvera D, Formenti SC, Schneider RJ. Translational control in cancer. Nat Rev Cancer. 2010;10:254–66.View ArticlePubMedGoogle Scholar
- Harding HP, Calfon M, Urano F, Novoa I, Ron D. Transcriptional and Translational Control in the Mammalian Unfolded Protein Response. Annu Rev Cell Dev Biol. 2002;18:575–99.View ArticlePubMedGoogle Scholar
- Gebauer F, Hentze MW. Molecular mechanisms of translational control. Nat Rev Mol Cell Biol. 2004;5:827–35.View ArticlePubMedGoogle Scholar
- Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, et al. Global quantification of mammalian gene expression control. Nature. 2011;473:337–42.View ArticlePubMedGoogle Scholar
- Russell JB, Cook GM. Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Rev. 1995;59:48–62.PubMedPubMed CentralGoogle Scholar
- Buttgereit F, Brand MD. A hierarchy of ATP-consuming processes in mammalian cells. Biochem J. 1995;312:163–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS. Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling. Science. 2009;324:218–23.View ArticlePubMedPubMed CentralGoogle Scholar
- Dana A, Tuller T. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 2014;42:9171–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Brar GA, Yassour M, Friedman N, Regev A, Ingolia NT, Weissman JS. High-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling. Science. 2012;335:552–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Sabi R, Tuller T. A comparative genomics study on the effect of individual amino acids on ribosome stalling. BMC Genomics. 2015;16:S5.View ArticlePubMedPubMed CentralGoogle Scholar
- Bazzini AA, Lee MT, Giraldez AJ. Ribosome Profiling Shows That miR-430 Reduces Translation Before Causing mRNA Decay in Zebrafish. Science. 2012;336:233–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011;25:1915–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Guttman M, Donaghey J, Carey BW, Garber M, Grenier JK, Munson G, et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature. 2011;477:295–300.View ArticlePubMedPubMed CentralGoogle Scholar
- Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat Protoc. 2012;7:1534–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Aeschimann F, Xiong J, Arnold A, Dieterich C, Großhans H. Transcriptome-wide measurement of ribosomal occupancy by ribosome profiling. Methods. 2015;85:75–89.View ArticlePubMedGoogle Scholar
- Hussmann JA, Patchett S, Johnson A, Sawyer S, Press WH. Understanding biases in ribosome profiling experiments reveals signatures of translation dynamics in yeast. PLoS Genet. 2015;11:e1005732.Google Scholar
- Weinberg DE, Shah P, Eichhorn SW, Hussmann JA, Plotkin JB, Bartel DP. Improved Ribosome-Footprint and mRNA Measurements Provide Insights into Dynamics and Regulation of Yeast Translation. Cell Rep. 2016;14:1787–99.View ArticlePubMedGoogle Scholar
- Artieri CG, Fraser HB. Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res. 2014;24:2011-2021.Google Scholar
- Bartholomäus A, Del CC, Ignatova Z. Mapping the non-standardized biases of ribosome profiling. Biol Chem. 2015;397:23–35.Google Scholar
- Oh E, Becker AH, Sandikci A, Huber D, Chaba R, Gloge F, et al. Selective Ribosome Profiling Reveals the Cotranslational Chaperone Action of Trigger Factor In Vivo. Cell. 2011;147:1295–308.View ArticlePubMedPubMed CentralGoogle Scholar
- Gingold H, Pilpel Y. Determinants of translation efficiency and accuracy. Mol Syst Biol. 2011;7:481.View ArticlePubMedPubMed CentralGoogle Scholar
- Tuller T, Veksler-Lublinsky I, Gazit N, Kupiec M, Ruppin E, Ziv-Ukelson M. Composite effects of gene determinants on the translation speed and density of ribosomes. Genome Biol. 2011;12:R110.View ArticlePubMedPubMed CentralGoogle Scholar
- Stadler M, Fire A. Wobble base-pairing slows in vivo translation elongation in metazoans. RNA. 2011;17:2063–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Thomas LK, Dix DB, Thompson RC. Codon choice and gene expression: synonymous codons differ in their ability to direct aminoacylated-transfer RNA binding to ribosomes in vitro. Proc Natl Acad Sci U S A. 1988;85:4242–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Kato M, Nishikawa K, Uritani M, Miyazaki M, Takemura S. The difference in the type of codon-anticodon base pairing at the ribosomal P-site is one of the determinants of the translational rate. J Biochem. 1990;107:242–7.PubMedGoogle Scholar
- Pop C, Rouskin S, Ingolia NT, Han L, Phizicky EM, Weissman JS, et al. Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation. Mol Syst Biol. 2014;10:770–0.Google Scholar
- Charneski CA, Hurst LD. Positively Charged Residues Are the Major Determinants of Ribosomal Velocity. PLoS Biol. 2013;11:e1001508.View ArticlePubMedPubMed CentralGoogle Scholar
- Lu J, Deutsch C. Electrostatics in the ribosomal tunnel modulate chain elongation rates. J Mol Biol. 2008;384:73–86.View ArticlePubMedPubMed CentralGoogle Scholar
- Dana A, Tuller T. Determinants of Translation Elongation Speed and Ribosomal Profiling Biases in Mouse Embryonic Stem Cells. PLoS Comput Biol. 2012;8:e1002755.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang J-R, Chen X, Zhang J. Codon-by-Codon Modulation of Translational Speed and Accuracy Via mRNA Folding. PLoS Biol. 2014;12:e1001910.View ArticlePubMedPubMed CentralGoogle Scholar
- Nackley AG, Shabalina SA, Tchivileva IE, Satterfield K, Korchynskyi O, Makarov SS, et al. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science. 2006;314:1930–3.View ArticlePubMedGoogle Scholar
- Gardin J, Yeasmin R, Yurovsky A, Cai Y, Skiena S, Futcher B. Measurement of average decoding rates of the 61 sense codons in vivo. Elife. 2014;3:e03735.View ArticleGoogle Scholar
- Li G-W, Oh E, Weissman JS. The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature. 2012;484:538–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Qian W, Yang J-R, Pearson NM, Maclean C, Zhang J. Balanced Codon Usage Optimizes Eukaryotic Translational Efficiency. PLoS Genet. 2012;8:e1002603.View ArticlePubMedPubMed CentralGoogle Scholar
- Ingolia NT, Lareau LF, Weissman JS. Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes. Cell. 2011;147:789–802.View ArticlePubMedPubMed CentralGoogle Scholar
- Ben-Yehezkel T, Atar S, Zur H, Diament A, Goz E, Marx T, et al. Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants. RNA Biol. 2015;12:972–84.View ArticlePubMedGoogle Scholar
- Kudla G, Lipinski L, Caffin F, Helwak A, Zylicz M. High Guanine and Cytosine Content Increases mRNA Levels in Mammalian Cells. PLoS Biol. 2006;4:e180.View ArticlePubMedPubMed CentralGoogle Scholar
- Lithwick G, Margalit H. Hierarchy of Sequence-Dependent Features Associated With Prokaryotic Translation. Genome Res. 2003;13:2665–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Cannarozzi G, Schraudolph NN, Faty M, von Rohr P, Friberg MT, Roth AC, et al. A Role for Codon Order in Translation Dynamics. Cell. 2010;141:355–67.View ArticlePubMedGoogle Scholar
- Artieri CG, Fraser HB. Evolution at two levels of gene expression in yeast. Genome Res. 2014;24:411–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Stadler M, Artiles K, Pak J, Fire A. Contributions of mRNA abundance, ribosome loading, and post- or peri-translational effects to temporal repression of C. elegans heterochronic miRNA targets. Genome Res. 2012;22:2418–26.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu B, Han Y, Qian S-B. Cotranslational Response to Proteotoxic Stress by Elongation Pausing of Ribosomes. Mol Cell. 2013;49:453–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Gerashchenko MV, Gladyshev VN. Translation inhibitors cause abnormalities in ribosome profiling experiments. Nucleic Acids Res. 2014;42:e134–4.Google Scholar
- Gritsenko AA, Hulsman M, Reinders MJT, de Ridder D. Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data. PLoS Comput Biol. 2015;11:e1004336.View ArticlePubMedPubMed CentralGoogle Scholar
- Andreev DE, O’Connor PBF, Fahey C, Kenny EM, Terenin IM, Dmitriev SE, et al. Translation of 5’ leaders is pervasive in genes resistant to eIF2 repression. Elife. 2015;4:e03971.View ArticlePubMedGoogle Scholar
- McManus CJ, May GE, Spealman P, Shteyman A. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res. 2014;24:422–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Stumpf CR, Moreno MV, Olshen AB, Taylor BS, Ruggero D. The Translational Landscape of the Mammalian Cell Cycle. Mol Cell. 2013;52:574–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Flicek P, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2014. Nucleic Acids Res. 2014;42:D749–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet journal. 2011;17:10–2.View ArticleGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee S, Liu B, Lee S, Huang S-X, Shen B, Qian S-B. Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution. Proc Natl Acad Sci U S A. 2012;109:E2424–32.View ArticlePubMedPubMed CentralGoogle Scholar