Lekha Dinesh kumar added an answer:Can shRNA or SiRNA knockdown a highly expressed gene as efficiently as genes that are expressed at a lower level?
This might be a naive question, but I just wonder if there are more transcripts in the cells, would it be harder for the knockdown mechanism to degrade all these transcripts? Imaging for a same target gene, which expresses at a very high level in cell type A but at a lower level in cell type B, if you use a same shRNA or siRNA, would you expect differences in knockdown efficiency?
I feel it will be more efficient to shut down a higly expressed gene since the siRNA signals will be quite amplified,thus resulting in an efficient knock down!Following
Lesya Holets added an answer:Troubles with RNA extraction from mouse skin.I'm trying to obtain RNA from mouse skin but the results have not been satisfatory. I observed chemical contamination and also very low amounts of RNA (1-5ng). I'm working with a murine model who has collagen overproduction and usually freeze the samples directly in -80°C in RNAlater.
Qiagen support told me that the RLT buffer can crystallize and that I should use a water bath at 37°C after this specific step to dissolve the crystals (this is not recommended in the kit). Do you think this is possible even working at room temperature? Any other recommendations ?
I keep the tissue in RNAlater at 4C for 1-2 weeks, about 1 mo at -20C, or -80C for longer time period.. I used GeneElute mammalian total RNA miniprep kit from Sigma for RNA extraction.Following
Jochen Wilhelm added an answer:Is it best to present your qPCR data as a ratio or log(base2) (aka, ΔΔCt)?
There seems to be a large variation in the presentation (and analysis) of qPCR data. I am wondering if it would be best to present my data as a ratio (2^-ΔΔCt) or take the log(base 2) approach (which just converts the value back to ΔΔCt)?
Assuming Efficiency =2 :
ΔCt(treatment) = Ct.target - Ct.reference
ΔCt(untreated) = Ct.target - Ct.reference
ΔΔCt = ΔCt(treatment) - ΔCt(untreated) (from Livak & Schmittgen, 2001)
Additionally, as far as I have understood the literature and previous posts here, it is necessary to do statistical analysis (ANOVA, t-test, etc.) on the ΔΔCt value, not the ratio, correct?
Thank you very much for your input and help!
Yes, it's actually quite simple :)
Thank you for your feedbak. I am happy that I could help.Following
Tomas Pereira added an answer:Why is my qPCR threshold line is below the background?
We are having trouble to determine the threshold line of qPCR in a ABI 7500. We always used automatic settings for determination of the threshold line, but recently the software is setting the threshold line below reaction background. Do you have any idea about what is causing this problem and how we can fix it? Attached is following an amplification plot of the wrong threshold. Thank you.
I would like to thank you all for your answers and for trying to helping solving our problem.
We recalibrate our system using a new Spectral Calibration Kit as recommended by Laurent and other researchers, and we performed a new qPCR and the same problem occurred. After that, we contacted Life Techlonogies support and I explained our problem. We discovered that the real problem actually was in the FAM probe. Life Tech is actually providing a new probe for us.
We think that a new probe, our problem will be solved. Thank you Kevin for your answer and help.Following
Jason W Hoskins added an answer:What are the possible mechanisms through which SNPs in the promoter region of a gene could affect clinical outcomes ?
SNPs in the promoter region of a gene would be expected to modify the binding affinity of the promoter to transcription factors thereby regulating gene expression. Are there alternative explanations for evidence linking promoter SNPs to diseases ?
On another note, if this promoter SNP is not linked to the risk of onset of a disease but to adverse clinical outcomes such as mortality or readmission rate, what explanations could be provided to explain such an association ? Could it represent a role in early versus late phases of the disease ?
A few notes:
First, the functional SNP most likely mediates altered protein binding, though other possible mechanisms could include altered CpG site affecting methylation of the region, or altered ncRNA binding. However, the latter 2 possibilities are more theoretical since the causality for the association between DNA methylation and expression is still murky, and I'm unaware of any study showing a functional SNP altering a ncRNA binding in a promoter. The effects of altered protein binding vary greatly depending on the protein, but could include loss/gain of enhancer/repressor TF binding, nucleosome repositioning and altered long-range chromatin interactions. There are numerous methods for determining allele-specific protein binding, including in silico binding prediction, supershift EMSA and mass spec of oligo pulldowns.
Second, even when you have identified the actual functional variant (which is not simple), determining the gene or genes affected by the variant is no trivial matter, even if the variant lies in or near a gene's promoter. As the downstream biological implications of the functional variant will stem from the gene(s) affected, this needs to be established. I have seen cases where a functional variant is very close to the promoter of a gene, but rather than affect that gene, it alters expression of a more distant gene. Tools like eQTL analysis, 4C-seq and CRISPR/Cas9 genome editing are great for clarifying this.
Third, in regards to your questions about the SNPs effect on clinical outcomes, I do not think there is any way to even begin to answer such questions without establishing the gene(s) that is/are affected. The mechanisms for how these SNPs might effect clinical outcomes are as numerous as the effects of genes and gene networks. I don't think there is any shortcut to get from SNP to disease measure mechanism without identifying the gene(s).Following
Leavy Zhang added an answer:Is there any method to classify epigenetic peaks according to peak shape?
Recently, I was doing Histone modification analysis using ChIP-seq. I found that different peak shapes occurred for one specific modification (i.e. H3K4me3). So, I want to do a clustering according to their peak shape? Perhaps simple methods like K-means or hierarchical clustering might not be the very choices for this.
Could someone giving me any related advices? Like signal recognition or model-based clustering method?
Thank you, Anil Panigrahi! I do agree with your comment on factors impacting histone modification peak shapes. However, it's a great pity that my data sets have no biological or technical replicates. So, it's difficult for me to investigate peak shapes across experimental or biological replicates. What I am always concerning about is that what these peak shapes are related to gene functions and competitions between different histone modifications, and this is the very story that I want to see with my scanty data sources.Following
Kirill Makedonski added an answer:How can I do semi-quantitative measurements for ChIP assay?
I am performing Chromatin Immunoprecipitation assays (ChIP) and wanted to do semiquantitative measurements for my different ChIP experiments. Can anyone suggest me the simple and reliable method for this. Also which software can be used if I have saved all my gel images into .tif format. If I take all conditions same for WT and mutant then is it necessary to include WCE (Input) into calculation?
I would perform radioactive PCR and like in previous answer perform 3-4 different amounts of cycles, starting from 20 (20, 22, 24, 26). of course depend on your control gene.
Then running the 4% acrylamide gel, dry it and expose and calculating the bands on every sutable software in your facilityFollowing
Pranay Amruth Maroju added an answer:What is the maximum number of missing loci allowed to exclude an individual from an analysis (e.g. Geneland, Structure)?
I'm trying to figure it out what is the maximum number of missing loci allowed to exclude an individual from an genetic analysis (e.g. Geneland, Structure)?
I'll be grateful for any help
It depends on total number of loci you have, population size and relatedness of the individuals for microsatellite data. CERVUS software will tell u whether you have enough loci to perform genetic analysis in the form of a line graph (Probability of identity on Y axis Vs No. of loci on X axis). Also if your population size is less hardly you can allow any missing locus. More related individuals require more scored loci.Following
David A Armstrong added an answer:Has anyone used the spike-in for circulating microRNA?I would like to know if there is anyone who has worked specifically with QIAGEN cel-miR 39 spike-in using the QIAGEN miRneasy mini kit.
I have not used this specific spike in , but keep in mind that spike-in's are only a control for sample processing, not for biological variation... there has been no widely established normalizer for circulating microRNAs yet - best choice so far geometric mean.Following
Rafal Bartoszewski added an answer:How does miRNA regulate protein fold?
Animal microRNAs (miRNAs) regulate gene expression by inhibiting translation and/or by inducing degradation of target messenger RNAs. As we know, in addition to downregulating mRNA levels, miRNAs also directly repress translation of hundreds of genes. MiRNA can, by direct or indirect effects, tune protein synthesis from thousands of genes. But it is unknown how can miRNAs regulate protein fold.
One could expect that the main answer to UPR would be decreasing protein and mRNA levels in order to unload ER. However, base on ours and other groups studies very limited number of mRNA is downregulated. Hence miRNA would rather have more complicated role then just reducing ER protein load. We speculate that they are adjusting crucial UPR TFs levels in order to determine cell fate during this stress answerFollowing
Tomasz Jurkowski added an answer:Why do methylation changes occur so quickly as compared to transcriptional reprogramming?
Ngn3,Mafa,Pdx1 mediated lineage conversion ,converts acinar cell to beta cells .Analysis found that DNA methylation changes in 10 days and acinar cells appear to be insulin + but transcriptional reprogramming takes large time ,,,about 2 month
Can any body help me to find out why ?Following
Tommaso Andreani added an answer:Does anybody here have experience indexing vcf files using tabix and vcf-sort?
I am trying to use vcf-merge on 2 of my vcf files in order to carry out an Fst analysis in the software; for that I need to use tabix and vcf-sort to gunzip, sort, and index my file.
Ive successfully gunzipped and sorted the files. I am just now having troubles indexing them, because tabix returns the "Chromosome blocks at [position] are not continuous. Has the file been sorted?"
Any help would be appreciated from any bioinformatics superstars!
Next time you do not need to gunzipp 'cause it takes more time.. Happy you have solved!!
Cheers and vote!Following
Anna Git added an answer:When is it useful to apply the 40-DeltaCt method for calculating relative gene expression?
I have recently come across a clinical study that expressed gene expression in the following way: "RNA results were then reported as 40-DeltaCt values, which would correlate proportionally to the mRNA expression level of the target gene." (Where delta Ct was the difference between the Ct values of the gene of interest and a reference gene. In this case 40 cycles were used for amplification.) In what type of experiments is it useful to apply this (40 - delta Ct) calculation? How does this relate to the more frequently applied 2(deltaCt) - method?
I completely agree with the emptiness of "40-Ct" transformation. Some of my experiments only run for 30 cycles (abundant snRNAs) or 45 cycles (pre-amp + amp of rare miRNAs).
In general, I'd be very very cautious in applying ANY of the above Ct-based calculations, as they assume that the amplification of both genes (reference and interest) is of equal efficiency. This is rarely the case, and over a large number of cycles, differences creep up. We include a standard curve for each gene and convert Ct values to relative input, which can then be normalised in any way you see fit. Also include a titration of RNA input into the reverse transcription. You'd be surprised how many commercial kits are saturating their reactions, which affects abundant and rare transcripts in different ways!
We also stopped using a single reference gene, but use a geometric mean of 3-4. This cannot easily be done with ddct, unless you accept averaging Ct as geo-averaging input etc. When dealing with a perturbation experiment in cell lines, it does not matter. But when dealing with heterogeneous samples (e.g. patient material), it is a much more robust measure.
Lastly, I would urge everyone to consider the MiQE guidelines championed by S.A. Bustin.
Stefano Campanaro added an answer:Can I reduce FDR cut-off of a list of DEG when I run GO analysis to obtain more enriched GO terms?
I did a microarray experiment and I got a list of 1.000 DEG using a FDR cut-off <0.001. Then used these 1.000 DEG to run a GO analysis by DAVID but I didn’t got any enriched GO terms. However when I used a more stringent FDR (for example cut-off <0.0005) I obtained some enriched GO terms. I wonder if this operation is possible or it is wrong from a conceptual point of view.
I do not know exactly the details of the results, but did you verify if the DEG have a difference in gene expression of at least two fold? Sometimes I have found lists of DEG with low FDR but tiny differences in gene expression. Sometimes a very small difference in gene expression cannot be biologically relevant and the list of genes does not show any Gene Ontology enrichment.
Krzysztof Treder added an answer:What is the significance of +1 G in the T7 promoter?
I am looking at getting an equiprobable distribution of A,T, C and G on my RNA +1 using the t7 RNA production kit. The ideal promoter requirements is TAATACGACTCACTATAGGG. Does anyone have information on the importance of +1 G and if it can be replaced by other bases?
You re welcome ;). This is anyway exciting model to learn.Following
Amar Kumar added an answer:What is the procedure to label and quantify neutrophil elastase or chromatin in Neutrophil extracellular trap (NET) formation ?
I don't want to use quantification method under or for microscope
Alexander Zhavoronkov added an answer:What are the suitable and most recent algorithms for comparing microarray data with RNASeq data with microarray data at the gene level?
What are the best and most recent algorithms for comparing microarray data with RNASeq data with microarray data at the gene level?
Thank you, Manvendra. We are looking for a more expanded view on E (batch effect) and cross-platform normalization. A more recent version of: http://nar.oxfordjournals.org/content/early/2012/01/18/nar.gkr1265.fullFollowing
Narges Ghaderi asked a question:Do you think that SOTA is better or usual GSOMs for gene ranking in microarray?
Thanks in advance for your replies.Following
Ada Lampert added an answer:How many RNA editing sites are transferred to next generation ?
RNA editing in ALU sites in the brain is much greater in humans than chimps - this epigenetic phenomena should be heritable in order to influence human cognitive advantage
Dear Hisashi Iizasa
Thanks for your answer.Following
Sriram Kannan added an answer:What is the difference on gene silencing effects between 5-hydroxymethylcytosine and 5-methylcytosine?
I am interested in terms of their effects on gene silencing. I had not heard of hydroxymethylcytosine until today and would like to find out how this modifications differs in terms of silencing to methylcytosine?
Methylcytosine could get transformed into thymine which might become a pathogenic SNP or mutation but possibly TET's action leading to hydroxymethylcytosine could prevent such mutations (computationally, i hypothesized a related concept in http://www.academicjournals.org/article/article1379937955_Kannan.pdf)Following
Israel Ausin added an answer:How can I validate DNA methylation in plants?
What are the methods available for the validation of DNA methylation in plants.
You can also try McrBC digestion and then PCRFollowing
Dimitar Angelov added an answer:What is the effect of 0.5 positioning site on the formation and stability of nucleosomes?
Kindly tell me about the affinity of histone proteins for the 0.5 positioning element.
I have constructed nucleosomes with different sequences at 0.5 site instead of 1.5 positioning site. How does the 0.5 site affect the nucleosomal positioning?
Andrey has right. Your question is unclear. The term "0.5 (1.5) positioning element has no real meaning unless eventually speculations of theoreticians. Up to now, no one has been able to design nucleosome positioning sequences theoretically (ab initio) based on what you call "positioning elements" I.e., to solve the revers problem. There is a lot of paper on the subject, and you should read many of them before moving to "construction" of positioned nucleosome sequences and checking them experimentally. This is really a tough business, that need a very competent environment and there is risk for loosing time for nothing. The boom on sequence-dependent nucleosome positioning is over, and now, peoples agree that epigenetic factors predominate over genetic on nucleosome alignment in nuclear chromatin.Following
Luca Pinello added an answer:How to identify transcription factors binding to a specific DNA sequence?I have an idea to identify the transcription factors binding to a specific DNA region. I don't have any transcription factor candidate, only what I have is a potential promoter region of a gene. Does anyone have any idea what I should do, what techniques should I use? or any software to tell the transcription factor and gene binding?
We have recently a new software pipeline called Haystack find TF enriched :
It also integrate gene expression data and epigenetic data if you have.
We validated the pipeline in this PNAS paper:
Any feedback is well appreciated!Following
Raul Loera Valencia added an answer:Can someone please suggest online free software for the analysis of relative gene expression after obtaining delta ct values on real time pcr?It would be more convenient if someone could suggest some excel sheet method/software to calculate delta ct and folds expression. Manually one by one is quite complex and time consuming.
the REST software is NOT compatible with x64 Windows 8.Following
Catarina A. Marques added an answer:Is cDNA quantification from RT-reaction a good alternative method for normalization qPCR data?
In both of the following papers Rihnn H. describes cDNA quantification as an alternative method for normalization of qPCR data; and thus, avoid the endogenous control issue.
is it really an appropriate method?
Hi, I guess it will depend on the type of analysis you want to use? If it is relative quantification you'll always need an endogenous control do apply the deltadeltaCt method. If it is an absolute quantification qPCR, I guess that would be ok (maybe look for someone that has done it)? I would just look for a really good way to check for cDNA concentration, like the picoGreen kit or so, I would not trust that reverse-transcribing the same amount of RNA would result in exactly the same amount of cDNA, I would measure the cDNA itself, and make sure it is only measuring it, and not the RNA leftovers and dNTPs left by the reaction. Good luck!Following
Virginia Rebecca Falkenberg added an answer:What is the consensus binding motif of Dexamethasone?I want to know if Dexamethasone serves as either a transcription factor to enhance or repress transcriptional regulation.
Which bioinformatic program can I use?
You can do bioinformatic analysis using transcription factor binding site matrices to at least predict the possibility of GR-binding. However, it is the case that only an experiment, such as ChIP or EMSA, can prove the interaction is possible and a functional assay is necessary to prove the transcription factor binding can regulate transcription.
I think or that https://www.genomatix.de/ has outstanding matrices that are well curated and quite good. You can upload your sequence of interest and I think there are trial uses available for academic researchers.
You are looking for binding sites in the nuclear-receptor factor family if you are interested in dexamethasone treatment. In particular you want to look for NR3C1 (Glucocorticoid receptor) binding which are labeled as putative GRE (Glucocorticoid response elements).
If you would prefer a free program there are matrices freely accessible online in the TRANSfac database and there are numerous programs free online that use Transfac matrices to search your sequence of interest. (One example is SiteSeer). Go ahead and do at least a cursory sequence analysis before you invest in an in depth analysis. Hope this helps, and good luck!
Sachin D Honguntikar added an answer:Can anyone help with RNA quantification before performing real time PCR?
Hello, I am extracting RNA from very few number of cells ( 500 cells) from an embryo and it cannot be detected in nanodrop, So I used same volume of RNA for both my control and test samples and synthesize cDNA. Next I amplified endogenous control and my target gene using step one real time PCR. I got result but standard error is more. So I want to know whether normalizing with endogenous control gene without quantifying RNA is acceptable method for publication?
Thank you all for your wonderful suggestions.Following
Samah Jassam added an answer:How can I stimulate the human umbilical vein endothelial cells with histamine and TNFa within separated experiments?I need to stimulate the humvaan umbilical vein endothelial cells with Histamine and TNFa in order to see the effect of that on the barrier function of vascular endothelial cadherin catenin actin complex, so my questions:
1. How much can i use concentration in that for both Histamine and TNFa?
2. what is the time points to see after stimulation of the cells, for ex: after 1 min, 5min..etc?
3. what is the best intracellular protein that related to Vacular endothelial adherent junction and affected by Histamine and TNFa at the same time?
Please, remember, you need to keep the concentration of TNF-a close to the real level in human body, for example 12.5 Pg/mL is the concentration of TNF-a in cancer patients blood. However, to activate the expression of Slectins (CD62-E,L,P) on the endothelial cells I am using 10µg/mL. you need to apply TNF-a about 6-18 hours. I have tried this concentration and it truly worked well for me. I hope that was helpful. Good luck.Following
Ian Teo added an answer:What are the advantages and disadvantages of using Standard curve in stead of delta delta Ct method in Real Time Gene expression assay?When we want to assess the gene expression assay by Real Time PCR, there are mainly Taqman gene expression assay or SYBR green gene expression method in which we need to set up the primer set by ourselves. By SYBR method, again there are Standard curve or delta delta Ct methods for detection of gene expression. Is there anyone who has had experience in comparing these two methods? Can you suggest which of the Standard curve or delta delta Ct method better?
I am planning to use two Endogenous controls but I don't have any positive control for this experiment. Please help me.I would always go for a standard curve method based upon diluted standards (eg plasmid ) of known concentrations with both the target gene and a reference (housekeeping) gene measured. The simple reason for this is that real copy numbers give you hard data which you can relate to your input. If I put the cDNA from the equivalent of 100,000 cells and did a PCR for a known highly expressed gene (eg ribosomal RNA) and got 100 copies, then I have reason to question my sample or cDNA. If you generate a Ct of eg 39 what does it tell you? The difference between a Ct of 45 and a Ct of 50 is the same as that between Ct 30 and Ct 35, but in terms of copy numbers a Ct of 50 in most instances is less than single copies. delta Cts and delta-delta Cts are fast , easy and lazy ways of getting data. When analysing someone else's data, unless you know the range of Cts from which the delta Cts were obtained it is difficult to determine whether the data is truly within the range of believable results.(someone once presented me a graph of a 30 fold difference based a upon a Ct difference of 50-55 cycles ). A value of 1000 copies of x per million copies of ribosomal RNA is a hard number.These real values can then be used to determine the fold change in expression. It also makes direct comparison between experiments more realistic.
Remember: High impact factor = High input effortFollowing
Yasser Cabansag added an answer:Can anyone help me regarding real-time PCR using the sybr green method?I am looking for gene expression by using the sybr green method. In my melt curve analysis, I am getting multiple tm peaks for target gene, whereas my endogenous control gene melt curve is perfect. However when I checked the PCR product of my target gene on the gel, I am getting single band. Can anyone suggest how to overcome this problem?I think your control is more optimized on your thermal profile compare to your target. Multiple peaks sometimes suggest that you did not get the right annealing temperature for your primer. Also, qPCR is more sensitive compare to conventional PCR. Even a very small amount of DNA can be amplified. Maybe the peaks are too small in amount that you can not able to see them in gel, that is why you can only see single bands.Following
About Quantitative Gene Regulation
A group for scientists interested in quantitative descriptions of gene regulation in pro- and eukaryotes (equilibrium and non-equilibrium protein binding, chromatin rearrangements, covalent modifications, input-output cis-regulatory functions, etc) using approaches of biophysics, molecular and cell biology, bioinformatics, systems biology and synthetic biology.