Science method

RNA-Seq - Science method

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A project in my lab involves single cell RNA-seq data analysis of mouse whole lung samples. However, when we analyzed and clustered the dataset and searched for markers to annotate the group, there appears to be few to no cells exhibiting the classical epithelial cell markers EPCAM and CDH1.
Each sample has around 8000 cells after filtering for mitochondrial content, and the overall quality seems fine. But over half of the cells appear to be immune cells and there were less than 100 epithelial cells for each sample.
The mouse models are established by a collaborating group, and whole lung samples are sent to a sequencing company (travel time about 3 hours minimum) to generate the scRNA data. Our collaborators have adjusted experimental protocols multiple times to increase cell viability (~85%), but we are having difficulty fixing this lack of epithelial cells.
Does anyone have some experience with this, or know why there would be so few epithelial cells in scRNA-seq data of mouse lung samples?
Both my lab and our collaborators are fairly new to handling scRNA-seq data, so any insight would be helpful.
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Hi Yeogha,
Firstly, what proportion of your cells do you expect to be epithelial? From my understanding of human lungs, a large proportion of the cell types are mesenchymal, immune, or endothelial - so your results might not be that unreasonable.
Secondly, have you tried any of the canonical markers of the epithelial subtypes? p63, KRT5, MUC5AC, SCGB1A1, FOXJ1 for example?
In saying that, I know very little about scRNA-seq too, so I'm sure someone else will have a much more sensible answer!
All the best,
Sam
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I've done RNA-seq analysis on a dataset downloaded from GEO looking at immune gene expression in Asthmatic, COPD and normal epithelial lung cells. Trying to do a t-test for my statistical analysis, but I need to group my data into Asthmatic, Healthy and COPD samples/cells as it doesn't show up in R which samples belong to which group?
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Hi,
you need to compare the disease versus control samples when doing the statistical test. I didn't fully get if the problem is that you lack the information of which samples belongs to which category or it is a coding problem. As for the former, usually datasets have a metadata file in which the sample names you find in the gene expression table are present, and the treatment information is included. If it is a coding problem, you can index the sample names to divide the data into Asthmatic, Healthy and COPD.
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I need to ship some RNA samples overseas for RNA-seq.
I saw a paper that compares lyophilized RNA and non-lyophilized RNA. Also, I found a protocol that dries RNA with lithum + ethanol and ships at RT.
Has anyone ever done it? Did it work?
Thanks a lot
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You can store RNA at room temperature as an ethanol precipitate. If you have RNA in solution, take some known amount - say 10-20 ug- and add 1/10 vol. NaAcetate and 2.5 volumes ethanol. The precipitate is stable and can be mailed. Upon receiving, spin down in a microcentrifuge and resuspend the RNA pellet in a specific volume of dH2O or whatever buffer you need.
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I need tutorial to analyze RNA Seq data in ubuntu Linux and R, and IGV.
I can't run the commands in Ubuntu Linux for alignment of data and mapping of reads. I need the tutorial for running commands in Ubuntu for merge, sort and index my data, and have to use Sam tools, Bam tools, and Bed tools., but I can't run the commands. And also need to RNA Seq data analyzing in R and IGV as well.
Thank you
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Hmm, I'm not entirely sure how to run that myself, but since I lack coding experience I've been using the LatchBio platform. Through their RNA-seq and DeSeq2 workflows, I've analyzed and visualized a lot of the data at my lab. Hope that helps!
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I want to perform a phylotranscriptome analysis. For that I have downloaded RNA-seq data from SRA. In galaxy I have trimmed my data, then did assembly through trinity to get contigs from reads. Now what should I do, I need the complete transcriptome to put it into the MEGA for MSA and subsequent analysis.
Please help.
Thanks in Advance.
Sincerely-
Sunzid.
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Sheikk I share you this guide:
Is under development, but I consider that is pretty good.
Also I think you must check the quality of your assembly, and in the guide that I passed you is very clear, but you can see an expanded explanation in Trinity's Github:
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I performed RNA-seq and scRNA-seq on the same set of samples but the log2 fold change values are in very different range and I am not sure we can normalize it in any way. Please let me know if someone has performed a similar analysis.
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Hi Abhay,
You could try to 'pseudo-bulk' your colleague's scRNA-seq data and then compare that to your own.
Here, the expression values of all profiled cells in a single-cell experiment are bulked together for each replicate. This then can be processed using the same pipeline as your bulk data giving you a much more direct comparison.
One would expect concordant fold-changes to validate underlying biology (ko effect) but I wouldn't expect results to be identical though... Some differences would arise from different batches (replicates), library chemistry, sequencing runs, etc.
Also, if you still have some cells available you could run some qPCR on a few selected genes to tell you which dataset is better to trust.
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there are diffirent program such as Rstudio, python,... for RNA-seq data analysis, according to your knowledge and experiences whic one is better and more comprehensive? and is there another program??
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Hi all,
I have RNA seq data and I wanted to check the relative expression of selected targets based on RNA seq data. To validate this I have isolated RNA from a separate cohort and run the qPCR. However, the trend of my qPCR data is completely against the RNA seq data. The genes which are up-regulated in RNA seq is down-regulated in qPCR and vice versa. I do not know whether I am missing any variable here.
I know I have not written in detail but will be happy to discuss more if need any information.
Thanks
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What was the N for your RNA seq, and qPCR analysis? Surprisingly, the trend is the opposite, which shouldn't be the case, unless the primer isn't specific and amplifies some other segment.
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I'm uploading RNA-seq data on NCBI. I have successfully done step one but in the second step there is an error occurring during data submission process. kindly guide me in details if someone know well. thanks for your cooperation
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thanks Erik
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I'm looking for any publicly available RNA-seq data sets related to all sub-types of breast cancers to presearch for thesis project, thank you all...
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Thanks Saubhik Sengupta...
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Can the reads from multiple samples be aligned to the reference genome at once via the HiSat2 tool in RNA-Seq data analysis? Or should I run HiSat2 on each sample individually and then somehow combine them later?
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Run HiSat2 on each sample individually and then combine them using samtools
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Hi,
I am working on a gene cluster from an amycolatopsis strain that supposedly produces a glycopeptide antibiotic - its a silent gene cluster at the minute.
I have sent it for RNA sequencing and the cluster is highly expressed, but there was no glycopeptide produced (checked via MS)
Any ideas as to why?
Thanks
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Katie A Burnette I used 3 separate cultures. The gene cluster was expressed in 2/3. But nothing was detected via MS. And yeah I have tested standards and limit of detection on the MS prior to this experiment
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I'm in the initial stages of planning a miRNA seq experiment using human cultured cells and decided on TRIzol extraction, Truseq small RNA prep kit, using an illumina HiSeq2500. The illumina webinar suggests 10-20 Million reads for discovery, the QandA support page suggests 2-5M, and I wrote the tech support to ask, who suggested I do up to 100M reads for rare transcripts. Exiqon guide to miRNA discovery manual says there is not really any benefit on going over 5M reads. I was hoping to save money by pooling more samples in a lane, so I was hoping someone with experience might be able to suggest a suitable number of reads.
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i am working on cardiomyopathy patients Blood samples . and wanted to do miRNA sequencing can some one please suggest how many millions reads i need to sequence 20 millions or 30 millions and also please suggest the platform as well .
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Hello everyone,
I am looking for an open dataset to verify the results obtained by analyzing the total RNA-seq of patients in the TARGET-AML project (GEO search was not successful).
The dataset should include:
1) bone marrow RNA-seq of pediatric patients with non-relapsed AML;
2)bone marrow RNA-seq of pediatric patients with relapsed AML (primary tumor BEFORE relapse);
3)clinical data (relapse-free time, for example) - optional.
If you know where to find a dataset like this, I would be very thankful.
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TCGA, UCSC Xena, and some papers with supplementary.
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I am studying a protein and from imaging I can see that my protein is recruited to sites of DNA damage. I wish to UV irradiate HEK293 cells in culture prior to collection and analyses by RNA-seq and mass spectrometer. Does anyone have an idea of how to (protocol and instrumentation) UV irradiate cells in culture for such studies? 
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How did this turn out? Curious to know if the UV light or hydrogen peroxide worked for causing DNA damage without killing Hek293 cells.
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I have an analyzed RNA seq data set. The analysis part including differential gene expression, clustering analysis and enrichment analysis has been done. I am aware that the bioinformatic part is done and most of the analysis part is also done. Could someone please guide on how to extract the biological relevance from the data set. What should be the starting point for working with this data? Should I start by looking at the differentially expressed genes in different comparisons or start from the cluster analysis and try to look for the genes.
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In recent years, RNA sequencing (in short RNA-Seq) has become a very widely used technology to analyze the continuously changing cellular transcriptome, i.e. the set of all RNA molecules in one cell or a population of cells. One of the most common aims of RNA-Seq is the profiling of gene expression by identifying genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This tutorial demonstrates a computational workflow for the detection of DE genes and pathways from RNA-Seq data by providing a complete analysis of an RNA-Seq experiment profiling Drosophila cells after the depletion of a regulatory gene.
Regards,
Shafagat
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Hello everyone!
I have RNA seq dataset for two groups knockout and wild types of mice samples. I have the normalized values in terms of quant all datasets. Please guide me how to perform PCA on the normalized values. I am not a bioinformatician, kindly suggest non-coding methods.
Thanks in advance!
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Hi! I recommend using LatchBio for RNA seq data. I've used it several times, and it is super easy to use since it has a non-coding interface.
Hope that helps!
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We plan to send total RNA samples from fish tissues for RNA-seq analysis. The total RNA samples will be TRIzol-extracted, DNase-treated, and cleaned using Zymo RNA Clean & Concentrator-5. For previous transcriptome profiling studies, we cleaned the total RNA samples using QIAGEN kits, so this would be our first time with the Zymo kit. The manufacturer states, "RNA is ready for all downstream applications including Next-Gen Sequencing, RT-qPCR, hybridization, etc."
Please let me know if you have any experience with Zymo-prepped RNA samples used for RNA-seq. Any feedback will be greatly appreciated.
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Thanks, Kyle and Mohamed, for your feedback. Our RNA purifications using this kit have shown excellent yield, A260/230 and A260/280 ratios, and integrity, but they have only been used for RT-qPCR. Based on your comments, they should also be suitable for RNA-seq.
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I have a good R and statistical analysis background (also with machine learning). in addition, I'm a fresh biotechnology grad. I would like to try to replicate some Rna-seq analysis using R papers (with their provided data). Any SHORT (beginner-friendly) papers to recommend?
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This might be a good tutorial for you to analyze RNA-seq data using R
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If I am looking at a specific gene that is comprised of 3 exons or 2 protein coding regions, and I find that some of my reads being aligned are very small proportionally to to the entire protein coding region and located only in one of those protein coding regions. Should I consider this a "bad quality" alignment generally speaking? Similarly if the read spans the entirety of one protein coding region, but is largely absent in the other (1/2), how should I classify these alignments?
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or maybe one transcript is present and the other absent, not expressed....
quality in RNAseq is not assessed by this way, fastqc is better.
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Hello,
I have several single-end fastq files. Before trimming with Trimmomatic, FASTQC reported TruSeq adapter sequences as possible source of overrepresented sequences. However, after trimming, now FASTQC reports Clontech SMART CDS Primer II A as source of overrepresented sequnces. What should i do about them? Can those sequences cause any negative effects on downstream analysis?
Thanks in advance.
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Thank you Mehmet Tardu
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We would like to know the best value for money commercial company for DNA sequencing as part of an RNA-seq study.
Thanks you. Joe Duffy
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Perhaps the LatchBio platform might be of use? It does not sequence data but it's a great RNA-seq analysis pipeline.
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Hi everyone,
I have a question and I was hoping to get some insight from you.
I ran RNA-seq on my samples and I didn't have replicates, I used differentially expressed genes with a cut-off of 2-fold change for Preranked-GSEA and got a list of pathways activated in each of my samples. The question is that can I use any of the values such as ES, NES, FDR, etc. or since I have no replicates, it doesn't make sense to use these? Next, if I don't use these values, should I rank my pathways based on the number of genes they have in the gene set? If yes, is there a cut-off that is being commonly used for that?
Thanks for the help.
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I would say that at least three replicates per sample are needed. Otherwise, you can rank the pathways but can't show statistical significance. But I feel manual ranking will be tedious.
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Hello! I am looking to isolate cell nuclei from mouse brains (hypothalamus specifically) and have been considering several kits. There are two from Sigma - the Nuclei EZ Prep and Nuclei Pure Prep, as well as the Minute Single Nucleus isolation kit - offered both with and without detergent.
I have considered adapting a 'home-brew' protocol described in some recent papers, but due to time constraints, a kit would be ideal since there might be less troubleshooting and validation.
Does anyone have experience with these kits and do you have a recommendation as to which might work best?
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Hi Eugene, I was thinking the same after my own various in-house variations. I have used the Nuclei EZ kit, which is actually just a detergent-containing buffer, and not a true kit. I did get good results with it but I was using it as part of a Fluorescence- Associated Nuclei Sorting protocol, so I was always guaranteed pure nuclei. I do have my eye on the Minute Single Nucleus products, so if anyone else has used these?
Thanks,
RT
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Hello, What would be the best methodology to perform RNA-seq with samples with low RIN? What do you recommend?
Thanks
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The NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® protocol has the ability to work with partially degraded FFPE samples.
Good luck!
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If we want to use patients data, does RNA seq include any potential patient identifying information that should be checked for donor agreement?
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RNA sequence is considered as PHI (Protected health information).
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Hello everyone,
I've just started studying about STAR aligner and I came across primary assembly and patch release. I understood so far that patch release is a minor version of a genome which comprises only the sequence(s) that has some update, not the whole genome itself.
Therefore, for RNA-Seq studies and lncRNA characterization (from alignment to differential expression), the patch release would not be recommended. Instead, the primary assembly should be used. Is that right?
I would appreciate if anyone could share any insight, review or basic publications. Thanks.
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I think you should use primary assembly. You can download the latest version, which at the time is release 106 of GRCh38. It is available on the FTP server of Ensembl.
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I have read the sentence below, and I have still diffculty to understand the term Read Depth. I would be glad if someone could explain it to me.
Read depth:The total number of sequencing reads obtained for a sample. This should not beconfused with coverage, or sequencing depth, in genome sequencing, which refers to how many times individual nucleotides are sequenced.
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Recently I did RNA seq on mycobacterium. I got the TPM data from one of our colleagues in the Bioinformatics department because they helped us to analyze the raw data.
Then, I'm interested in making a volcano plot from the data. Do you think it's possible to get a p-value from TPM data?
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Hi Desak Nyoman Surya Suameitria Dewi, RNA-seq data should be processed with dedicated statistics, such as DESeq2 - Bioconductor or edgeR - Bioconductor. If you use p-values calculated with those statistics, you can create volcano plots. The reason why you cannot use more typical statistics, such as ANOVA, to get the p-values, is that RNA -seq data does not have a normal distribution, and many common inferential statistics probes require data with normal distribution.
Best !!
AN
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I am running RNA extractions on whole gut samples for downstream RNAseq. For one individual I realized there was a length of gut tissue still in the original collection tube that I didn't add to the homogenization solution. I'm not sure what region of the gut it actually is or proportionally how large it is relative tissue that was homogenized (it is smaller), but I'm worried that if there are regional differences in RNA expression profiles that will bias the RNAseq data towards the already-processed portion of the gut.
Is this sample salvageable? If I extract RNA from the leftover tissue, could I just combine the total RNA sample volumes from both prior to sending in for sequencing? Alternatively, if we sequence both separately could we normalize and combine reads somehow? Are there any other strategies that would be more robust to prevent bias? Thanks in advance.
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Was the gut that didn't get homogenised effectively protected from RNases? If the leftover tissue was: stored in a reagent like RNAlater the whole time, OR frozen the whole time and never thawed, then I think it would be fine to extract the RNA from it now and add that RNA to the existing RNA from the same sample. But if the leftover tissue sat around in the collection tube at ambient temperatures for even a minute longer than the other samples - it's gone, let it go. Substantial differences in RNA quality between samples can be a major source of bias in RNAseq experiments, so deliberately adding RNA which is likely to be degraded is a bad idea. (If you are in doubt at all, do you have access to a BioAnalyzer or TapeStation or other way to check RNA integrity? You could extract the RNA from the leftover tissue, compare the RIN of the new extraction to the previous extraction, and mix them only if they are comparable.)
I don't think preparing a second library from the RNA made from the leftover tissue and then sequencing that would be worth the effort and expense. I don't think it would be possible to neatly integrate this data into your experiment.
Whatever you end up doing, this sample will be different in some way to the other samples in your experiment. So you should run an outlier analysis at the bioinformatic level, and seriously consider excluding this sample entirely if it looks different from the other samples. Building in some extra n into RNA-seq experiments to account for errors like this (they happen to us all!) is a very good idea when feasible.
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Anyone know why the DEGs are different when RNA-seq results mapped to genome vs transcriptome?
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James Y. M. Tang Genomics examines the entire set of genetic instructions supplied by DNA, whereas transcriptomics investigates gene expression patterns.
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Hello! I'm new to bioinformatics and cancer databases. I was exploring cbioportal and analyzing coexpression of different genes through scatter plots. I noticed that the axis are labeled as " RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)" (I attached an example so you can see). I know that RSEM is a transcript quantification software but what does "Batch normalized" mean? does it give upper quartile normalization? FPKM? or what?.
thanks in advance!
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It's upper quantile normalisation. See https://www.biostars.org/p/106127/.
Here is a paper comparing normalisation methods I personally find informative.
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We currently have a study done with RNA-seq analysis. But the raw counts in our data show a large difference, up to 10 fold higher in the raw counts versus the lowest one. But after normalization, it shows 1.2 fold changes.
Is this 10-fold change in raw counts generally acceptable in the field of RNA-seq?
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Dear Shenq.
An additional quality control step after normalization is to check the densities plots with log10 normalized expression values to compare the distributions from all libraries. Overlapping expression distributions indicate appropriate homogeneity of the sequencing depth and that count normalization was suitable to compare the expression levels of the different libraries. Non-overlapping densities indicate that comparison of libraries would not be appropriated, you can check if maybe some libraries have excess of PCR duplication of reads. For more details on this analysis an as an example you can check my publication
Hope this helps
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I have in my RNA-seq quantification data from Arabidopsis obtained by mRNA-seq polyA enrichment library transcripts encoded by chloroplast and mitochondrial genes in significant DEG. How is it possible, if chloroplast and mitochondrial transcripts do not have poly-A tail? Are these data reliable or contaminants which should be ignored?
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I checked it and I probably found the response: Moreover, polyadenylation is often
a degradation signal in organelles, meaning that researchers
using poly(A)-selected RNA-Seq for measuring differential
expression in organelle systems may, in some instances,
be measuring the opposite: differential degradation (Smith and Lima, 2016).
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Currently conducting cDNA amplification during a Tag-Seq (RNA-Seq) protocol. In the gel attached, all wells with 'D' should be cDNA with smears, all others should not because they are controls missing certain oligos during this test PCR that was run. The picture was edited and slight smears faintly appear. These should be amplified cDNA but look like junk because they appear in every well and should not.
We have already troubleshooted with template concentrations, reagent quality and batch, used 2 thermocyclers, different sets of polymerases, tested cDNA already synthesized by various library preps and results have appeared similar in each situation. Any suggestions would be greatly appreciated!
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Did you figure out the issue?
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Dear all, I am trying to use CD-hit to remove the duplicates from the file that is the output from trinity (RNA seq assembly).
I used the following parameters:
cd-hit-est -i in.fasta -o out_cdhit90.fasta -c 0.90 -n 9 -d 0 -M 0 -T 0
But the output file still contains lots of small or fragmented sequence plus the best one. How can I remove those small or fragmented duplicates by changing the parameters?
thanks
ZQ
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Hello, do you know any tool DIFFERENT from CD-hit to filter CDS unigenes.?
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I've recently sent off E. coli RNA samples for RNA-seq. The company we have used for sequencing have replied and said all samples failed QC due to degradation. I am looking at the Tapestation values and some of these samples have a RIN up to 9.5 but then a DV200 of 25. I believe this is due to the large band at approximately 95 bp- which I believed were tRNA. I am now unsure as to whether to proceed with sequencing as they cannot guarantee sequencing results. Has anyone had experience of such a contrast in numbers previously? And then gone on to successfully perform the sequencing?
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Interesting. I am also using chemical based extractions (Trizol) and the company (Oxford Genomics) have tried 3 times and the library prep fails. They have included some silica membrane prepped samples as controls and they work fine.
Thank you for such a quick response. This is driving me nuts as the samples look great in all QC analysis.
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Hi All
I am currently looking for a high-throughput, low cost RNA-seq method that can give me data on the SNPs in a large scale transcriptomic study of Eucalyptus. I see that 3'tag RNA seq comes up a lot in the 'low cost, high throughput' criteria, but I can't find studies where it has been used for SNP detection/profiling. Anyone use it successfully for this purpose? Any other suggestions of RNA-seq technologies that might be applicable? We are looking at a sample size of at least 400 trees, so RNA-seq would be breaking the bank a bit. I am a bit of a novice with NGS technology, so any advice would be greatly appreciated!
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To the best of my knowledge, 3' RNAseq amplify and sequence just a portion of the gene close to the 3' end for each transcript. I wouldn't use it for SNP detection
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Hi everyone. Have you guys tried Metascape for your downstream analysis? Appreciate your feedbacks.
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Deepyaman Das I have the DEGs already. Just looking at different downstream tools that are simpler for biologist to use. I actually have found the answer. http://www.webgestalt.org/ is recommended by my bioinformatician
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When we do RNA_sequencing especially with low RIN score RNA samples, it becomes difficult to get the efficient library and good sequencing data. What solutions you are using and how its helping you???
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RNAsin should be used simultaneously.
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Hi,
We are checking the integrity of RNA isolated (using MagMAX 96 Total RNA Isolation kit) from mice tissue on a 1% agarose gel that contains a bit of bleach to get rid of RNases. Our 28S/18S ratios are less than 2 for most samples. Is this due to RNA degradation or is something wrong with our gel?
We sequenced some of our samples anyway using AmpliSeq RNA Transcriptome Mouse Gene Expression on the Ion Torrent S5. The amplicons that read end-to-end were around 13 000-14000 out of the total of 23 930 amplicons for all samples. Is this an indication of degradation or is this a normal value for RNA sequencing? We are wondering if we can continue sequencing the rest of our samples if we get similar results on the gels?
Thanks!
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Dear Belinda, to check the integrity of RNA, it is always good to go for Biaoanlayzer or tapestation, specially when planning for RNA-sequencing. Because most of the RNA library prep kits will have their own requirement of Integrity scores like RIN greater than 8. These scores you cannnot get in gels, you can just find the intensity of 18s and 28s. When you have highly degraded RNA samples then you wont be able to see 18s and 28s peaks, in such cases you might need to calculate the DV200 degradation percentage metric.
There are many kits available from Takara Bio for total transcriptome sequencing, which can deal with degraded RNA as well with RIN score 2 to 10.
If would like to know more details, will be happy to share that as well.
Thanks and regards
Kiran
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Hi all,
I'm working with an RNA-seq data set consisting of a large number of samples, sequenced at around 50-80M reads. There's a bit of uncertainty as to what the precise experimental workflow was for generating these data, but my best understanding at the moment is that the TruSeq RNA sample preparation kit was used (https://www.illumina.com/documents/products/datasheets/datasheet_truseq_sample_prep_kits.pdf).
This kit starts with total RNA, uses oligo-dT beads to bind polyA+ mRNA, then fragments the mRNA and carries out cDNA synthesis with random hexamer primers.
The data I've seen thus far show a very strong bias towards the 3' end of transcripts, in some cases so extreme that only the exons at the very 3' end are covered, with the rest of the regions having close to no reads at all. This bias is particularly pronounced in genes with long transcripts.
I'm aware that using oligo-dT priming is known to introduce a 3' bias into RNA-seq data as the reverse transcriptase will not always be processive enough to reverse transcribe in one go, but I'm at a loss to explain why the approach above might generate 3' bias if random hexamers were used.
Could anyone suggest any ideas as to what the possible causes of 3' bias in RNA-seq data might be? Are there any causes other than oligo-dT priming?
Would also really appreciate a link to a paper if one exists. Thank you!
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This could be due to the mRNA being somewhat fragmented even before the polyA+ capture. RNA is fragile stuff.
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I have received RNA seq data from three KO mice and wild type mice. The mice are littermates from commom father and two mother and age of 2-month old. I got the RPKM values and performed GSEA analysis. But I am not getting heat map with clear distinction. The sequencing they performed were Single paired. Here i am attaching heatmap for the hallmark gene set. I am a non-bioinformatician , please provide suggestions.
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You could check all your samples in a PCA like analysis to see how different are them. Instead of only showing the heatmap of some genes, you could in the first instance, generate the heatmap with all your genes, in order to see differences in abundances between the control vs wt. Maybe you could identify cluster of genes in which their abundances change berween these two condition. Finally, the best way to identify genes up/down-regulated in one or another condition is to perform a differential gene expression analysis.
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Hi, I am planning to extract nuclei from a mouse brain tissue and perform scRNA seq using 10x chromium. When extracting the nuclei it is recommended to use RNase inhibitor (non specified which one) and all the papers I've read so far use the one from Takara (#2313A). It would be faster for us to order one from Sigma or Thermofisher but I am reluctant since all the papers use Takara's one. Anyone has any recommendation/experience?
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I suppose different RNAse inhibitors from different manufacturers might differ in efficiency or effectiveness, but they will do similar job. So, using one in place of other should not matter much. Sometimes, the best in the market also fails to do the job.
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Hello everyone!
We have fibroblasts isolated from hepatocellular carcinoma tissue samples. We are planning transcriptomics. However, I am not sure if it is doable since there are only 2500-3000 cells. Which method do you advise? Might Nanostring work?
Thanks in advance for your time and consideration.
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The above links include a single-cell RNA seq that may be helpful. Good luck.
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Hi all,
I am a complete beginner in terms of bioinformatics analysis and I am hoping to complete some functional analysis on some differentially expressed gene lists of some RNA seq data. However, I am a bit lost on how/best way to start: Below are the columns of the DE gene lists that I am operating upon (which seems to be quite different from other example data I’ve seen from various vignettes)
Ensembl Gene ID, RPKM of condtion 1, RPKM of condition 2, FDR 0.05, gene start, gene end, gene strand, gene name, gene description
Does anyone have any suggestions on how to import/modify this data into R so that will allow me to use a tutorial/vignette of sorts to perform GO or KEGG pathway analysis etc? (should/can I convert the RPKM to log2foldchange and pvalues? If so, how would I go about doing this?)
Thank you!
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What is the latest pipeline to run the RNA-Seq genomic data ?
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Try tuxedo pipeline II. here is the reference https://www.nature.com/articles/nprot.2016.095
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Hi all,
I am currently looking into how the presence of an oncogene influences cytokines responsiveness (primes cells to be more responsive to cytokines) within RNA-seq data. What elements and tools should I be looking into?
Would upregulation transcription factors be something that would explain such a phenomenon? If so could someone direct me to a resource/literature that would possibly explain the relationship between transcription factors and cytokines responsiveness?
Thank you!
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Look at the properties of the STAT family and its canonical and non-canonical pathways.
good luck
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Hello all,
I'm in the process of validating RNA-seq data with qPCR experiments. Currently I have my TPM data from RNA-sequencing and CT values from qPCR. Any recommendations for the best way to go about correlating/validating? I've read that delta CT will linearly correlate with log(RPKM/FPKM) but not sure if this would apply to my case? If it does, would I simply plot log(TPM) values to delta CT values?
Thanks in advance!
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Yes, that sounds reasonable.
You should just make sure if you want to correlate the expression levels or the differential expression levels. It's usually more intersting to see how the differential expression levels correlate. Expression levels will anyway show a rather high correlation across genes with large differences in their expression values. It depends on the specific case if such a correlation would give you a useful information.
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Hi Everyone,
I have done differential analysis of RNA Seq data. I have got the list of 2000 upregulated genes. I have drawn the heat map of top 100 genes. Can I use these top 100 genes for further analysis like GO Term and Network analysis. I am very confused as I have a huge list of genes how I narrow down and focus on the new genes which are not identified before.
Thanks,
Sarah
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Hi Sarah,
As for heatmap visualization, you could select the top 100 or 200 DEGs to show the differential expression. I think it's a right and wise way. By the way, you could change the cut-off (fold change/logFC, p-value/q-value) arbitrarily.
However, if you would like to conduct functional analysis such as GO or KEGG, I recommand you should input all DEGs into software or webpage (DAVID or R). The reason is that most biologic variations derive from the transcriptome changes, which include signaling pathways cascade and downstream effectors.
So, if you want to further narrow down, network analysis is indeed a good way. Cytoscape software and its additional plugins will be a better choice for you to find the hub gene.
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After downloading the RNA-seq Metadata of breast cancer with the gdcRNAtools package in R, I ran across this error message while processing and filtering RNA-seq Metadata:
Error in file(con, "r") : cannot open the connection
In addition: Warning message:
In file(con, "r") : InternetOpenUrl failed: 'The server name or address could not be resolved.'
I encounter with this problem when i use this code (metaMatrix.RNA - gdcParseMetadata(project.id = 'TCGA-BRCA, data.type = 'RNAseq, write.meta = FALSE):
My internet connection is working correctly, and my device is disconnected from the VPN, and I followed all the protocols in the Bioconductor site perfectly ( http://bioconductor.org/packages/devel/bioc/vignettes/GDCRNATools/inst/doc/GDCRNATools.html ).
How can I resolve this issue?
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Pejman Morovat Because I know you live in Iran and using an Iranian server, I recommend that use a server from another country. Using a VPN could help you resolve internet connection issue in your R console window.
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I've lost the raw data of RNA-Seq in rice, can I just upload the clean data to NCBI for a paper pubishment? Which data base can I upload them? Thanks so much!
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Yes, you can upload it. It is acceptable.
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Hi everyone. I'm having problem with quantifying viral reads from my RNA-seq data, which is part of my Final Year Project. Basically, I have filtered the host reads by aligning to host organism genome. The unmapped reads was aligned to viral database with BLASTn, which illustrated the present of some specific virus. Then, I tried to align the unmapped reads to the genome of these virus that I have found using HISAT2, but the output is 0% matched reads. I need some suggestion to solve my problem. Thank you very much!
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You may try with STAR aligner for mapping RNASeq reads.
Close reference - I meant the similar reference genome.
Kindly check the trinity manual (github.com/trinityrnaseq/trinityrnaseq/wiki/Post-Transcriptome-Assembly-Downstream-Analyses). Once you get the virus genome reads and assembled contigs of the virus, you can easily quantify the virus using the trinity protocol.
Thank you
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Hi researchers,
I want to do RNAseq using the flow sorted cells from mouse retinae tissue. The RNA is the pool of many samples and the total yield is between 50-100ng and two samples have 5.5 RIN value. Although the bioanalyzer gel picture is okay and showing good rRNA bands. My understanding is that with this limitations I should go with rRNA depletion. I need experts advise that should I go with the poly A capture or rRNA depletion? Please share a good comparative research article if possible.
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Thanks Anil Tiwari
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I have done the DEG analysis of RNA Seq by edge R software. I have a list of genes that have adjusted p values and log fold change 1.5. Upregulated genes are 2000 and downregulated are 1800. Now I am confused that should I used all the UP and down regulated genes for further DAVID and PPI analysis? Kindly give me some suggestions.
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You can use the lists of up and down-regulated genes for over-representation analysis (ORA) in DAVID. Looking at enriched pathways or biological processes within these lists can give you an idea of what to look into further.
Since you also have the relative expression values, you can also use gene set enrichment analysis (GSEA) of the entire DEG list. This is a bit better than simple over-representation analysis since it also preserves the directionality of the expression change.
My preferred tool for doing quick exploratory analysis on DEG lists is Webgestalt (http://www.webgestalt.org/). The website lets you run ORA, GSEA, and network topology analyses using a variety of reference databases.
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I find myself in a predicament that I'm not sure how to resolve:
I have managed to isolate a particular multi-cellular structure from postmortem human brain tissue with the intention of isolating RNA from that structure and building libraries for RNA-sequencing.
Reagents and kits used:
Single Cell RNA Purification Kit from Norgen
RNase-Free DNase I Kit from Norgen (on column DNase treatment)
SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian library kit from Takara
So far, RNA extraction from that structure has been successful and so has library building with one issue: There is a second, consistently present, smaller peak at a larger size than a library. So I either have the recurring issue of either gDNA (despite performing DNAse treatment) and/or over-amplified products in library traces. No matter how I have tried to adjust RNA input and PCR cycle, a second peak keeps cropping up in library traces.
I think I’ve managed to reduce the size of the second peak as much as I can to the extent where I don’t think my libraries are over-amplified and whatever it is, is too large from my library to be sequenced (see attached file Takara V3 Library Kit Optimization Conditions 6, specifically well B1). Would such a library be adequate for sequencing? I have received the criticism that my desired library peak may also have gDNA - how likely is this, given the shape of the trace? I know some genomic contamination is inevitable but I'm hoping to keep it as low as possible.
Side note for those who don't work with postmortem brain: lower RINe, lower yields in general, and lower quality "everything" is to be expected. So I’m also concerned that lowering amplification more will not be sufficient for a number of lower quality samples.
Any advice would be greatly appreciated!
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Hi! I'm not certain about your reagents and the procedures. I've never used Norgen, is it similar to Qiagen? That being said. You can still get good information if you have an appropriate "relative comparison. analysis method with a good number of biological replicates and a matching number of control samples. It is possible to remove, or mask, the sequences that correspond to contaminating RNA, like ribosomal RNA on alignment. From the count matrix for the gene subset, if you then do PCA and the differences between control and sample are larger than the differences between the replicates you'll get some answers. From this you can develop methods for refinement. There are probably some alternative RNA cleanups that will help, but if your intended analysis us "profiling" you'llhave bigger downstream problems than simple extraction and library production. Good luck!!
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Hi everyone
How can I count mapped RNA-seq reads using linux?
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You can get counts using featurecounts, htseq and other tools.
Detailed help is available in the tool manuals.
Thank you
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One last comment. I though you were doing bulk RNA-seq. Sorry. I wouldnt use nanostring unless someone gave them to me for free. It is old technology, like microarrays. There is a GeoMX from that company, but its spatial. If you find out who your rep is for Novogene, bulk RNA-seq is probably very similar in cost.
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I am currently analysing some RNA-Seq data from human primary fibroblasts. I noticed in the following paper (https://www.nature.com/articles/ncomms15824) that the expression of hox genes was used as a proxy for biopsy site.
Would anyone have any potential scripts or other resources they could point me to? I'm just not sure how to code it/which hox genes to include.
Many thanks for your time,
Rob.
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I think your answer is in the supplementary materials of this paper that you have cited https://static-content.springer.com/esm/art%3A10.1038%2Fncomms15824/MediaObjects/41467_2017_BFncomms15824_MOESM399_ESM.pdf on pages 25-26.
The authors have used the expression of hox genes from all the samples to cluster that data matrix and then used those clusters to create a new factor variable with x levels and used it as an adjustment factor in the regression model.
If you are asking how to create this cluster variable, then one way would be to use the 2 functions in R, you can google for a detailed usage
hclust and then cutree
You can also see a quick example from one of our project code
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Hello, I'm trying to calculate the standard error in order to calculate the 95% confidence interval to represent error bars on a graph and I am stuck on the calculations when using RPKM. The data I have is from a colleague's RNA seq and I'm unsure how to do the calculations. I would appreciate any help I can get, thank you!
The data is as follows:
Total linear RPKM
R1 R2 Fold change pvalue
Control 8.82 1.85 2.24 0.622
Sample 1 355.63 245.66 164.46 0.049
Sample 2 10.42 11.49 6.09 0.26
*Note R1/2= Replicates
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Thank you Adria-Jaume Roura , I'll look into the link you sent me.
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I'm performing RNA-Seq data analysis for differentially expressed genes. as I'm new to this kind of work, so I performed these three gene count tools separately on the same bam file from RNA-Star alignment. I assumed that I might get a slite variation between these three tools' results. and I got some P-adj value variation for the same genes. so I want to know which one of these is better statiscally.
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If you are not limited to these three tools I would recommend you to look at Salmon (https://www.nature.com/articles/nmeth.4197 ) or Kalisto (https://www.nature.com/articles/nbt.3519). You can always scan the existing literature to see the comparisons, for example like this paper .
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I want to perform RNA-Seq data analysis for DEG's, by taking RAW reads from the NCBI-SRA database, of DENV1, DENV2, DENV3, DENV4. I want to perform this analysis on a galaxy web server. I'm a bit confused about the datasets from SRA. My confusion is, in this accession no from GEO-database- GSE69602, there is a total of 116 data are present. and I took only Total cell lysate data. In total cell lysate, there are two biological replicates at each time interval, like 6hr, 12hr, 24hr, 48hr, 72hr, and the other one is mock. I performed one analysis by taking two biological replicates of 72 hr and two mocks. workflow is, FastQC-Trimmomatic-RNA-STAR, StringTie, DEseq2. I want to know that is the right way or I'm doing anything wrong & if I have to take all the data from the respective time intervals, what is the protocol to specify those data at DEseq2?
All datas are singel-end data,
if you need to see my galaxy history I can share it with you.
A big thank you in advance
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Hi, The workflow created by you is correct you can also try EdgeR for Differential expression.
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I am planning for single nuclear RNA seq from the mouse pancreas tissue. It is pretty hard to get good quality RNA from the mouse pancreatic tissue. Is there any way to QC the nuclei prior to the 10X run? Also any tips on inhibiting the RNAses activity during the isolation process would be helpful. Currently I am using a lot of RNAse inhibitors in the solution but would like a better/ cheaper alternative. Thank you!
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Thank you Malcolm Nobre . I do take care of these things but it still is hard to inhibit RNAses activity. Actually I am isolating the nuclei from the pancreatic tissue for single nuclear RNA sequencing and hence as per my knowledge there is no way to figure out the quality before subjecting it to 10X. But would be nice to know if there is any way to do that? When I am doing the optimization of nuclei isolation, I am planning to run the gel and check in bioanalyzer. Since it is a nuclei prep it will not have rRNAs.
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RNA input without adding the anti-m6A antibody would be enough?
Thank you in advance,
Alexandre Magno Vicente
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Great question! Did it work? Or did you solve it, develop any protocol? Please share some advice.
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Hello, everyone. I obtained DEGs from RNAseq analysis for normal and infected samples. Then I decreased the number of them by some downstream analysis. Now I have 120 DEGs, and I want to select between them the best combination of biomarkers that can recognize normal from infected samples (biomarker panel). So I want to use machine learning methods (At first, I want to perform feature selection and then draw ROC curve, count MCC, Spe, Sen, ....for the combined set of selected biomarkers by different algorithms such as the neural network and random forest). Because I don't have experience in machine learning, I have some questions. And please let me know if you think I am doing any steps that explain here wrong!
1- What kind of RNAseq files should I enter into machine learning software? count file, FPKM, tpm, or any other files?
2- Should that be normalized?
3- Should the entry be log2 transformed?
4- Can the training and discovery dataset be the same?
5- Is what I write below a correct study design?: The use of a dataset for obtaining DEGs then, partitioning it into k subsets of equal size. Of the k subsets, a single subset is retained as the test data set. The remaining k - 1 subset is used as training data sets. The cross-validation process is then repeated k times, with each of the k subsets used exactly once as the test data. The k results from the k iterations are averaged (or otherwise combined) to produce a single estimation. And then performing a test for the model with an external dataset to validate the model.
6- Can the validation dataset be from a different technology like microarray? Is any pre-processing needed for the datasets to be tuned before performing machine learning methods in this case?
Thank you to answer my questions
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fpkm values of your DEGs
Have a look on data from TCGA database. The same way you can prepare your data.
FPKM is not a perfect normalization method. I'd suggest you extract normalized counts from DESeq2 then prepare the dataset for ML.
Best!!
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Hi. We are planning to do a single-cell RNA seq in combination with an ONT system for sequencing instead of Illumina. So is there anyone who has done it before? There are a few steps in the library construction protocol after GEM generation and cDNA amplification/cleanup steps that we can change as per my understanding. I need expert advice on this. Looking forward to your valuable suggestions?
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Dear community,
I was wondering if anesthesia (most likely isofluran) of animals before euthanasia and sampling of internal organs (here reproductive tracts in lizards) for RNA seq might ater the mRNA expression profile?
Would you recommand to perfor the euthanasia without the anesthesia or would you anesthetized them?
Thanks a lot for your answers,
Morgane
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Anesthesia is an intervention that one should expect to alter RNA expression!
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Hi all,
I plan on running qPCR to validate RNA-seq data but wasn't sure on the starting material (or the amount). Right now, I have extracted mRNA (extracted using the NEBNext mRNA Isolation Kit) and cDNA libraries (synthesized using the NEBNext Ultra II Directional Library Prep Kit).
If using mRNA as the starting material, how much mRNA would be ideal to create cDNA for qPCR? Before, I've used total RNA as starting material and this seems to be the common starter used in other qPCR papers. Additionally, is it possible to just use the cDNA libraries I have created as starting material for qPCR? If so, is there a general protocol for it?
Thanks in advance!
Aaron
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Dear Aaron,
greetings to the sunny state :) For a 20 µl RT reaction volume we use 1 µg of total RNA, which is about 5 - 10 µl in our case. I assume that 1 µg of mRNA should also work just fine. Although assuming that usually a large amount of total RNA is rRNA you could probably try even lower amounts here like 0.5 µg. But this is just an educated guess. As I don't know your mRNA concentration it might be that you have to concentrate your sample prior this step.
Afterwards we dilute our samples 5x with water and then use 4.2 µl of that for a 10 µl qPCR reaction. However, this is a little bit try and error as depending on your target gene you might need a little bit more sample amount if the copy numbers are very low (due to a low expressed gene).
I would recommend, to first determine your primers efficiency as well as the efficiency of your house keeping gene primers (which you need anyways for a proper data normalization), as this will give you an idea which dilution level returns you optimal Ct values.
## Is it possible to just use the cDNA libraries I have created as starting material for qPCR?
I would say yes - but if your template copy number is so low that you have to use your undiluted cDNA sample to obtain reasonable Ct values it could mean that you might have a hard time determining you primers efficiency with your dilution gradient. Of course if this is the case then you simply have to live with it that you only can assume certain primer efficiency but not determine it.
I recommend you to check out Rao's paper on the delta-delta-Ct method:
Good luck Aaron
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Hi!
Does anyone have experience of shipping extracted total RNA at ambient temperatures, for RNA seq;
(i) using DNA/RNAshield using Zymogen? (or recommend an alternate product. RNAstable has been discontinued)
(ii) ethanol precipitated RNA (ethanol, 3M sodium acetate)?
Thanks!
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Hi Mehdi,
I have transported RNA at ambient temperature for sequencing and works good. Firstly, I precipitated with of 0.1 volumes of 3 M sodium acetate (pH 5.2), 2.5
volumes of ethanol and 2 μl of glycogen (10 mg/ml) (ON). Secondly RNA was centrifuged at 14,000 g for 30 min at 4C, washed in 75% ethanol and dried in speed-back. Everything has to be RNAase-free.
Other options are RNA later or RNA Transport.
Good luck!
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Hello all,
I was wondering if there is a negative effect of RNase inhibitors (like RNase Out, RNasin, etc) on low input RNA samples at higher concentrations? I find that many single-cell RNAseq methods are using concentrations as low as 0.01U/uL where standard RNA-seq protocols are using 1U/uL. Or instead is this simply to avoid reagent waste, i.e. less RNase inhibitor needed for less RNA?
Thanks!
Morgan
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The way it works, RNasin tightly binds to some ribonucleases and blocks their active site. Commercial preps represent a recombinant RNasin, which might contain traces of RNasin-RNase complexes originating from interactions between the overexpressed RNasin and RNases of the host. If your RNasin-protected RNA samples do not undergo a heat treatment, such a contamination may not reveal itself. However, if you have to heat them above 50-60 oC, RNasin will be denatured while RNases may still retain their activity with obvious consequences for RNA integrity. Thus, high RNasin loads may paradoxically be more dangerous for your RNA than no RNasin at all (but only for scenarios involving RNasin denaturation). Therefore, people try to keep its concentration at minimum.
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There are many markers for ferroptosis as listed in the link below:
And different literature probes different set of biomarkers. Some markers (NRF2, FTH1, ACSL4, SLC7A11, etc.) were examined in some literatures while they were not in others. I would like to detect ferroptosis efficiently because budgets are limited for primary antibodies for detection of ferroptosis using Western blot. Guys, is there any suggestion on narrowing the to-blot list? I guess it needs taking into consideration what ferroptotic sub-pathway my research subject is involved. Maybe some preliminary experiments such as RNA-seq can help me out to determine the sets of markers I will be blotting?
Your help is appreciated!
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Dear Dr. Yue Young,
The 3F3-FMA and TfR1 H68.4 antibodies are selective and effective to detect ferroptosis using Western Blot, I would suggest to take TfR1 or TfR1 H68.4 as a selective biomarker for the detection of ferroptosis using western blot.
For more detail you can go through a published paper by Feng et al., in Cell Report (Volume 30, Issue 10, 10 March 2020, Pages 3411-3423.e7)
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There are few steps to make heatmap of your qRT-PCR data (fold change or relative quantification) using R.
Data file preparation:
Make excel file of your data in which your will place your gene of interest in column and your treatment or conditions in row.
Save the file in *csv extension.
Import data file in R:
By using following codes, import your data file into R,
data2 <- read.csv("data1.csv")
~ data1.csv will be file name your data file your created in excel and data2 is the name of your data in R. You can use your own names instead of data1 or data2 and you can even give your data a single name at both places.
When you will import the data, you will see first column composed of serial numbers. We need to replace the numbers with the names of actual column of your data that contain your gene of interest. To do this use this code:
rownames(data2) <- data2$Name
~ Name is first column
This will replace the serial numbers with your first column. But now you have two columns with your genes of interest. To remove duplicate, use this code:
data2$Name <- NULL
Now your data is ready to create heatmap.
Developing heatmap:
First create matrix of your data by using following code:
data2 <- as.matrix(data2)
Now install a package to create heatmap "pheatmap" by following code:
install.packages("pheatmap")
after installing you will call that package every time when you want to use it by following code:
library("pheatmap")
Then give a command to make heatmap of your data by following codes:
pheatmap(data2)
Usually we show fold change/relative quantification value inside our heatmap to add them modify your code in the following way:
pheatmap(data2, display_numbers = TRUE)
- You can customize your heatmap in many ways. Contact me any time if your any help.
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Good luck
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Plant research (Genomics, epigenetic, proteomics)
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@Desirée
Please specify from which origin you are going to do RNA sequence? If plant species, you may contact through message .
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Hi everyone,
I'm currently delving into proteomics head first, which is entirely new to me. My collaborators will be carrying out tandem mass tag spectrometry with fractionation on my samples (cases versus controls, tissue is postmortem brain tissue) and will be sending processed results my way, which include # Peptides, # Unique Peptides, values scaled to QC, % CV, abundances, normalized abundances. I'm interested in case versus control differences so what would be the best analyses to do? I'm only familiar with RNA-seq analyses, so any workshops, youtube tutorials, tips and advice would be GREATLY appreciated!
Thanks,
Marina
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You can impute the normalized data in Perseus and do this kind of analysis (Cases versus controls). If you have any questions you can watch the MaxQuant Summer School videos.
My reference:
Perseus link:
MaxQuant Summer School:
Any questions send me a direct message.
Best regards,
Leite
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Hello everyone,
I'm doing the mRNA library prep using Illumina TruSeq Stranded mRNA kit.
The kit recommends anything between 100-1000ng of total RNA for the prep that is quite a range. My samples are from Zebrafish embryos.
What is the best amount to use?
Would appreciate advice from more experienced users.
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