DESeq / DESeq2 for translation efficiency in polysome profiling or ribsome profiling experiments
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Hello, I wanted to ask whether it is possible to use DESeq for the analysis of translation efficiency in polysome profiling or ribsome profiling. In polysome profiling - polysomes are isolated (e.g. by sucrose gradients), and mRNA that were associated with the polysomes are then isolated and sequenced. In ribosome profiling, mRNA fragments that are protected by the ribosome are sequenced. When such experiments are conducted in parallel to total RNA-seq, one can ask questions regarding the translation efficiency. For example: if we have 4 samples: ribosome sequencing (without treatment), total sequencing (without treatment), sequencing (with treatment), total sequencing (with treatment), one can ask how does the treatment affects the protein synthesis rate (which is the ratio ribosome/total). I wanted to ask whether it is possible to define such a model in DESeq for such analysis, in order to answer how is this ratio affected by the treatment. Thanks a lot -- output of sessionInfo(): none -- Sent via the guest posting facility at bioconductor.org.
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@mikelove
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hi Gilgi, Yes if you are interested in whether treatment affects the ratio of one assay to another you can investigate this using an interaction term, so: design(dds) <- ~ assay + treatment + assay:treatment Then you can either use a likelihood ratio test (reduced formula = ~ assay + treatment), or a Wald test on the interaction term. This was recently included as a recommendation in the vignette of the devel branch (v1.3), but Wolfgang has a slide on this from a recent Bioc talk, testing if the ratio of CLIP-Seq to RNA-Seq is affected by treatment. slide 25 here: http://bioconductor.org/help/course-materials/2013/EMBOBGI/131119-bgi- huber-de.pdf best, Mike On Mon, Dec 23, 2013 at 2:37 AM, Gilgi [guest] <guest@bioconductor.org>wrote: > > Hello, > I wanted to ask whether it is possible to use DESeq for the analysis of > translation efficiency in polysome profiling or ribsome profiling. > In polysome profiling - polysomes are isolated (e.g. by sucrose > gradients), and mRNA that were associated with the polysomes are then > isolated and sequenced. > In ribosome profiling, mRNA fragments that are protected by the ribosome > are sequenced. > When such experiments are conducted in parallel to total RNA-seq, one can > ask questions regarding the translation efficiency. > For example: if we have 4 samples: ribosome sequencing (without > treatment), total sequencing (without treatment), sequencing (with > treatment), total sequencing (with treatment), one can ask how does the > treatment affects the protein synthesis rate (which is the ratio > ribosome/total). > I wanted to ask whether it is possible to define such a model in DESeq for > such analysis, in order to answer how is this ratio affected by the > treatment. > Thanks a lot > > > -- output of sessionInfo(): > > none > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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Hi Mike, Thanks a lot. I tried doing so, and I am getting an error. I tried to find what the problem is without success. Sorry if it is something trivial I am missing. I have 2 duplicates for each sample Here is what I did: design = data.frame(row.names=colnames( countsTable ),condition = sampleTable$condition,type=sampleTable$type) > design condition type mut_Tot mut total mut_Tot mut total mut_poly mut poly mut_poly mut poly wt_Tot wt total wt_Tot wt total wt_poly wt poly wt_poly wt poly cds <- newCountDataSet( countsTable, design ) cds <- estimateSizeFactors( cds ) estimateDispersions( cds ) fit1 = fitNbinomGLMs( cds, count ~ type + condition + type:condition ) Error in fitNbinomGLMs(cds, count ~ type + condition + type:condition) : Call 'estimateDispersions' with 'method="pooled"' (or 'blind') first. I have duplicates, so why am I getting that error? Thanks a lot, Gilgi From: Michael Love [mailto:michaelisaiahlove@gmail.com] Sent: Monday, December 23, 2013 4:36 PM To: Gilgi [guest] Cc: bioconductor@r-project.org; Gilgi Friedlander; DESeq Maintainer Subject: Re: [BioC] DESeq / DESeq2 for translation efficiency in polysome profiling or ribsome profiling experiments hi Gilgi, Yes if you are interested in whether treatment affects the ratio of one assay to another you can investigate this using an interaction term, so: design(dds) <- ~ assay + treatment + assay:treatment Then you can either use a likelihood ratio test (reduced formula = ~ assay + treatment), or a Wald test on the interaction term. This was recently included as a recommendation in the vignette of the devel branch (v1.3), but Wolfgang has a slide on this from a recent Bioc talk, testing if the ratio of CLIP-Seq to RNA-Seq is affected by treatment. slide 25 here: http://bioconductor.org/help/course- materials/2013/EMBOBGI/131119-bgi-huber-de.pdf best, Mike On Mon, Dec 23, 2013 at 2:37 AM, Gilgi [guest] <guest@bioconductor.org<mailto:guest@bioconductor.org>> wrote: Hello, I wanted to ask whether it is possible to use DESeq for the analysis of translation efficiency in polysome profiling or ribsome profiling. In polysome profiling - polysomes are isolated (e.g. by sucrose gradients), and mRNA that were associated with the polysomes are then isolated and sequenced. In ribosome profiling, mRNA fragments that are protected by the ribosome are sequenced. When such experiments are conducted in parallel to total RNA-seq, one can ask questions regarding the translation efficiency. For example: if we have 4 samples: ribosome sequencing (without treatment), total sequencing (without treatment), sequencing (with treatment), total sequencing (with treatment), one can ask how does the treatment affects the protein synthesis rate (which is the ratio ribosome/total). I wanted to ask whether it is possible to define such a model in DESeq for such analysis, in order to answer how is this ratio affected by the treatment. Thanks a lot -- output of sessionInfo(): none -- Sent via the guest posting facility at bioconductor.org<http: bioconductor.org="">. _______________________________________________ Bioconductor mailing list Bioconductor@r-project.org<mailto:bioconductor@r-project.org> https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]
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hi Gilgi, On Mon, Dec 23, 2013 at 10:15 AM, Gilgi Friedlander < gilgi.friedlander@weizmann.ac.il> wrote: > Hi Mike, > > > > Thanks a lot. > > > > I tried doing so, and I am getting an error. I tried to find what the > problem is without success. Sorry if it is something trivial I am missing. > > > > I have 2 duplicates for each sample > > Here is what I did: > > design = data.frame(row.names=colnames( countsTable ),condition = > sampleTable$condition,type=sampleTable$type) > > > > > design > > condition type > > mut_Tot mut total > > mut_Tot mut total > > mut_poly mut poly > > mut_poly mut poly > > wt_Tot wt total > > wt_Tot wt total > > wt_poly wt poly > > wt_poly wt poly > > > > > > > > cds <- newCountDataSet( countsTable, design ) > > cds <- estimateSizeFactors( cds ) > > estimateDispersions( cds ) > ​it looks like this line above is the problem. You should have: cds <- estimateDispersions(cds) best, Mike​ > > > fit1 = fitNbinomGLMs( cds, count ~ type + condition + type:condition ) > > Error in fitNbinomGLMs(cds, count ~ type + condition + type:condition) : > > Call 'estimateDispersions' with 'method="pooled"' (or 'blind') first. > > > > I have duplicates, so why am I getting that error? > > > > Thanks a lot, > > Gilgi > > > > *From:* Michael Love [mailto:michaelisaiahlove@gmail.com] > *Sent:* Monday, December 23, 2013 4:36 PM > *To:* Gilgi [guest] > *Cc:* bioconductor@r-project.org; Gilgi Friedlander; DESeq Maintainer > *Subject:* Re: [BioC] DESeq / DESeq2 for translation efficiency in > polysome profiling or ribsome profiling experiments > > > > hi Gilgi, > > > > Yes if you are interested in whether treatment affects the ratio of one > assay to another you can investigate this using an interaction term, so: > > > > design(dds) <- ~ assay + treatment + assay:treatment > > > > Then you can either use a likelihood ratio test (reduced formula = ~ assay > + treatment), or a Wald test on the interaction term. > > > > This was recently included as a recommendation in the vignette of the > devel branch (v1.3), but Wolfgang has a slide on this from a recent Bioc > talk, testing if the ratio of CLIP-Seq to RNA-Seq is affected by treatment. > > > > slide 25 here: > http://bioconductor.org/help/course-materials/2013/EMBOBGI/131119 -bgi-huber-de.pdf > > > > best, > > > > Mike > > > > On Mon, Dec 23, 2013 at 2:37 AM, Gilgi [guest] <guest@bioconductor.org> > wrote: > > > Hello, > I wanted to ask whether it is possible to use DESeq for the analysis of > translation efficiency in polysome profiling or ribsome profiling. > In polysome profiling - polysomes are isolated (e.g. by sucrose > gradients), and mRNA that were associated with the polysomes are then > isolated and sequenced. > In ribosome profiling, mRNA fragments that are protected by the ribosome > are sequenced. > When such experiments are conducted in parallel to total RNA-seq, one can > ask questions regarding the translation efficiency. > For example: if we have 4 samples: ribosome sequencing (without > treatment), total sequencing (without treatment), sequencing (with > treatment), total sequencing (with treatment), one can ask how does the > treatment affects the protein synthesis rate (which is the ratio > ribosome/total). > I wanted to ask whether it is possible to define such a model in DESeq for > such analysis, in order to answer how is this ratio affected by the > treatment. > Thanks a lot > > > -- output of sessionInfo(): > > none > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > [[alternative HTML version deleted]]
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Hi Mike, Ooops, sorry, missed that. Thanks a lot. I have two more questions: 1. Would you suggest to do the correction of the library size (estimateSizeFactors) for all samples together (although there are 2 types: all mRNAs and ribosome bound mRNAs, that are only a fraction of all mRNAs in the cell)? If not, how would you suggest to do the normalization? 2. Would you suggest to calculate the ratio: (wt-ribosome/wt- total) / (mutant-ribosome/mutant-total) and look at it together with the p-value of the interaction assay:treatment? Thanks a lot for all the help, Gilgi From: Michael Love [mailto:michaelisaiahlove@gmail.com] Sent: Monday, December 23, 2013 6:33 PM To: Gilgi Friedlander Cc: Gilgi [guest]; bioconductor@r-project.org; DESeq Maintainer Subject: Re: [BioC] DESeq / DESeq2 for translation efficiency in polysome profiling or ribsome profiling experiments hi Gilgi, On Mon, Dec 23, 2013 at 10:15 AM, Gilgi Friedlander <gilgi.friedlander @weizmann.ac.il<mailto:gilgi.friedlander@weizmann.ac.il="">> wrote: Hi Mike, Thanks a lot. I tried doing so, and I am getting an error. I tried to find what the problem is without success. Sorry if it is something trivial I am missing. I have 2 duplicates for each sample Here is what I did: design = data.frame(row.names=colnames( countsTable ),condition = sampleTable$condition,type=sampleTable$type) > design condition type mut_Tot mut total mut_Tot mut total mut_poly mut poly mut_poly mut poly wt_Tot wt total wt_Tot wt total wt_poly wt poly wt_poly wt poly cds <- newCountDataSet( countsTable, design ) cds <- estimateSizeFactors( cds ) estimateDispersions( cds ) ​it looks like this line above is the problem. You should have: cds <- estimateDispersions(cds) best, Mike​ fit1 = fitNbinomGLMs( cds, count ~ type + condition + type:condition ) Error in fitNbinomGLMs(cds, count ~ type + condition + type:condition) : Call 'estimateDispersions' with 'method="pooled"' (or 'blind') first. I have duplicates, so why am I getting that error? Thanks a lot, Gilgi From: Michael Love [mailto:michaelisaiahlove@gmail.com<mailto:michaeli saiahlove@gmail.com="">] Sent: Monday, December 23, 2013 4:36 PM To: Gilgi [guest] Cc: bioconductor@r-project.org<mailto:bioconductor@r-project.org>; Gilgi Friedlander; DESeq Maintainer Subject: Re: [BioC] DESeq / DESeq2 for translation efficiency in polysome profiling or ribsome profiling experiments hi Gilgi, Yes if you are interested in whether treatment affects the ratio of one assay to another you can investigate this using an interaction term, so: design(dds) <- ~ assay + treatment + assay:treatment Then you can either use a likelihood ratio test (reduced formula = ~ assay + treatment), or a Wald test on the interaction term. This was recently included as a recommendation in the vignette of the devel branch (v1.3), but Wolfgang has a slide on this from a recent Bioc talk, testing if the ratio of CLIP-Seq to RNA-Seq is affected by treatment. slide 25 here: http://bioconductor.org/help/course- materials/2013/EMBOBGI/131119-bgi-huber-de.pdf best, Mike On Mon, Dec 23, 2013 at 2:37 AM, Gilgi [guest] <guest@bioconductor.org<mailto:guest@bioconductor.org>> wrote: Hello, I wanted to ask whether it is possible to use DESeq for the analysis of translation efficiency in polysome profiling or ribsome profiling. In polysome profiling - polysomes are isolated (e.g. by sucrose gradients), and mRNA that were associated with the polysomes are then isolated and sequenced. In ribosome profiling, mRNA fragments that are protected by the ribosome are sequenced. When such experiments are conducted in parallel to total RNA-seq, one can ask questions regarding the translation efficiency. For example: if we have 4 samples: ribosome sequencing (without treatment), total sequencing (without treatment), sequencing (with treatment), total sequencing (with treatment), one can ask how does the treatment affects the protein synthesis rate (which is the ratio ribosome/total). I wanted to ask whether it is possible to define such a model in DESeq for such analysis, in order to answer how is this ratio affected by the treatment. Thanks a lot -- output of sessionInfo(): none -- Sent via the guest posting facility at bioconductor.org<http: bioconductor.org="">. _______________________________________________ Bioconductor mailing list Bioconductor@r-project.org<mailto:bioconductor@r-project.org> https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]
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Dear Gilgi On 24/12/13 09:50, Gilgi Friedlander wrote: > 1.Would you suggest to do the correction of the library size > (estimateSizeFactors) for all samples together (although there are 2 > types: all mRNAs and ribosome bound mRNAs, that are only a fraction of > all mRNAs in the cell)? If not, how would you suggest to do the > normalization? Not necessary. If you do the normalization separately, the average ratio of total-RNA to the bound-RNA libraries will be in the size factors; if you normalize all together, it will be absorbed in the "assay" factor. In any case, your final result will be the same, as this extra factor cancels out in the double ratio you mention below. > 2.Would you suggest to calculate the ratio: (wt-ribosome/wt-total) / > (mutant-ribosome/mutant-total) and look at it together with the p-value > of the interaction assay:treatment? For the interaction, you get not only a p value but also a log2 fold change in the results data frame. The latter is an estimate of the log2 of the double ratio you mention. Using this estimate is preferrable to simply using the ratio itself because it has undergone empirical-Bayes shrinkage, which gets rid of the exaggerated values that the naive ratio tends to exhibit when counts are low. Simon
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On 24/12/13 15:08, Simon Anders wrote: > For the interaction, you get not only a p value but also a log2 fold > change in the results data frame. The latter is an estimate of the log2 > of the double ratio you mention. Using this estimate is preferrable to > simply using the ratio itself because it has undergone empirical- Bayes > shrinkage, which gets rid of the exaggerated values that the naive ratio > tends to exhibit when counts are low. Just noticed that you were using DESeq1, not DESeq2. Shrinkage is offered only in DESeq2 (and it might be useful for you to switch for this reason). In DESeq1, the reported log fold change is the unshrunken GLM estimate, which, in case of a balanced design, tends to be pretty close to the actual log ratio. Simon
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Hi Simon, Thanks a lot! Gilgi -----Original Message----- From: Simon Anders [mailto:anders@embl.de] Sent: Tuesday, December 24, 2013 4:11 PM To: Gilgi Friedlander; 'Michael Love' Cc: Gilgi [guest]; bioconductor at r-project.org; DESeq Maintainer Subject: Re: [BioC] DESeq / DESeq2 for translation efficiency in polysome profiling or ribsome profiling experiments On 24/12/13 15:08, Simon Anders wrote: > For the interaction, you get not only a p value but also a log2 fold > change in the results data frame. The latter is an estimate of the > log2 of the double ratio you mention. Using this estimate is > preferrable to simply using the ratio itself because it has undergone > empirical-Bayes shrinkage, which gets rid of the exaggerated values > that the naive ratio tends to exhibit when counts are low. Just noticed that you were using DESeq1, not DESeq2. Shrinkage is offered only in DESeq2 (and it might be useful for you to switch for this reason). In DESeq1, the reported log fold change is the unshrunken GLM estimate, which, in case of a balanced design, tends to be pretty close to the actual log ratio. Simon
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Protein biosynthesis can be divided into five stages, the amino acid activation, extension of polypeptide chain initiation of synthesis, peptide, peptide chain termination and release, protein synthesis after the modification. And there are three methods: cell-free protein synthesis, cell-based protein synthesis and virus-like particles.
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