Quantitative Gene Regulation

Quantitative Gene Regulation

  • Silke Werth added an answer:
    Can anyone recommend a protocol for Sybr Green Master mix?
    Does anyone have a good recipe for homemade Sybr Green Master mix? I have tried a couple, and to my understanding the brand/variant of the DNA polymerase is a major factor. Any experiences as to which polymerase works the best?
    Silke Werth

    Hi, Kristian,

    What mix did you end up using?

  • Riva Verma 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

    Riva Verma

    Try the Cayman NETs quantification kit, worked for me!

  • Ahmed Sallam added an answer:
    How can I estimate heritability from R?

    How can I estimate heritability from R? I am using lm and aov

    thanks in advance

    Ahmed Sallam

    Hi Mohammed, I was so lazy to calculate this because I do have much time. But it seems that I should do this....

  • Tonis Org added an answer:
    What determines that an enhancer is active?

    Dear researchers,
    I read that enhancer DNA sequence could bind to transcription factor, which then stimulates RNA pol to make more mRNA. My question is what determines that an enhancer is active? In other words, DNA sequence is present anyway, so activity/presence of a TF that binds to enhancer is more important. This led me to consider chromatin state of the enhancer. Still don't fully understand what makes an enhancer active. Thanks for help! cheers

    Tonis Org

    The activity of enhancers is determined by a combination of factors. Transcription factors being probably the most important. However, it is well documented that the same transcription factors have very different binding sites in different cell types. There are two factors that might explain the different binding sites - first, different interaction partners - second, different chromatin accessibility that determines which sites are accessible to transcription factors. That brings along the question what opens up the specific regions in cells? Ken Zaret has propagated the idea of so called pioneer transcription factors that can access DNA even in the nucleosomal contact. These factors are believed to initiate the activation of enhancer, followed by recruitment of other factors such as coactivators (p300), chromatin remodelers, histone modifiers, other transcription factors, polymerase II etc. To experimentally determine an active enhancer it usually has, as stated before, H3K4me1and H3K27ac modifications and p300 binding.

  • Julia Fine asked a question:
    Has anyone used or even heard of the IAPV detection kit from Genesig?

    I need to quantify IAPV in a single sample.  I was hoping to save some time with this, but I can't seem to find a single reference to it in any journal. http://www.genesig.com/assets/files/iapv.pdf

  • Richard J Edwards added an answer:
    RNA-Seq data normalization?
    Raw RNA-seq data are discrete data but normalized RNA-seq data (RPM or RPKM or FPKM) are not discrete, i.e. continuous data. Shouldn't this change in the data's nature change our understanding of the data? In other words, why can't the microarray analysis tools be used to analyze the normalized RNA-seq data-both are continuous data types?

    From a statistical viewpoint, is it necessary to maintain the discreteness or continuity of datasets? If yes, should we round the normalized RNA-seq data to maintain its discreteness?

    It annoys me to see gene expression value reported in a real number. For instance, what does it mean when a gene expression value is 10.5987 RPM/RPKM/FPKM. How is the fraction "0.5987" is expressed in reality?
    Richard J Edwards

    @Ioannis, I would be very careful about using NO normalisation method. You cannot compare raw RNA-Seq values as there will be differences in read depth etc. It is just that RPKM and FPKM are designed for WITHIN sample comparisons and do not normalise BETWEEN samples. Other methods (TPM, DESeq etc.) do normalise between samples but always based on certain assumptions, such as constant total RNA, constant total transcript counts, or lack of change in most genes. It is best to try multiple methods and check the assumptions to be sure that you understand how your data seems to be behaving. 

  • Fausto Sánchez-Muñoz 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.
    Fausto Sánchez-Muñoz

    We corrected that problem that we were experincing we are getting Cq values arroud cycle 20. 

  • Leavy Zhang added an answer:
    Is there a method for comment chromosome bias for specific genomic features?

    Hi all,

    Is there any way to statistically assess chromosomal bias of genomic locations of a certain kind of feature (e.g. miRNA or repeats)? 

    For example, there were 300 MYB transcription factors and 200 of them were located in Chr1, Chr6 and Chr11. But only gene number counts in each chromosome is not enough, p-value or FDR will be additionally appreciated.

    Thank you!

    Leavy Zhang

    Thank you for all answers and suggestions!

    After comprehensive consideration. I used fisher test and Bonferroni correction to meet my request and it really did well!

    Thank you again, especially for @Juan Luis Mateo  and @Matej Lexa

  • Constantin Polychronakos added an answer:
    What is the relationship between CTCF binding sites defined chromatin domain and disequlibrium linkage group?

    Does the linkage group cross the CTCF defined insulator region/loop region or overlap with the region?

    Constantin Polychronakos

    Without wishing to belabour the point: this is an interesting finding but it does not show any mechanistic relationship between LD and CTCF binding. It only shows that QTLs determining CTCF binding are physically close to the binding site which is what one might expect for a QTL where the quantitative phenotype is a single molecular event at a specific locus. This reasonable expectation has been amply confirmed from the study of eQTLs and, more recently, QTLs for epigenetic marks other than CTCF binding.

  • Mikael Kubista added an answer:
    How small of a "fold-change" in gene expression can be reliably measured by RT-qPCR?
    When using Sybr green or TaqMan qPCR assays to measure gene expression changes, can differences that are less than at least two-fold be taken as credible? What minimum fold-change or percent change is reliable?
    Mikael Kubista

    Yes, that's indeed the case. When we report assay/test performance to clients we always specify separately LOD, LOQ in addition to classical parameters such s dynamic range and PCR efficiency.

  • Yongzhong Zhao added an answer:
    What are the statistics involved in dividing an expression ratio by another?

    I am trying to reproduce a methodology described to identify changes in gene translation ratios using microarray experiments described in PubMed ID19804760, page 158. To calculate translational ratios, one divides the translation ratios from the experimental group by the translation ratio for the control group. My issue is that the translation ratio (Rn) in each group (control or experimental) comes from a microarray experiment that divides expression levels in ribosomal polysomes by the expression in ribosomal monosomes. Therefore each Rn (R1, R2, etc) has its own statistical significance. So for R1 it is p1 and R2 it is p2 (ANOVA analysis). How do we calculate the statistics of the ratio between R1 and R2 for example? 

    Yongzhong Zhao

    'Dividing an expression ratio by another' can be treated as a log-scale difference if with transformation. Once the GSE16738 data are log-transformed and normalized, it should be straightforward to conduct the analysis  by using the 'limma' package with a 2-factor-2 level (3 repeats) design matrix. 

  • Lucie Grodecká added an answer:
    How do I predict the function of a splice site mutation in silico?

    If a mutation is in the splice site (donor), how can we bioinformatically predict its function in silico? For example if the site is broken or not, is there any exon skipping, if no then how to find the next splice site?

    Many mutations of the splice site have been published, for
    example, IVS1þ1G>A, IVS3þ5_6GA>AC, and IVS5-1G>C
    in EVC, and IVS4þ2T>C in EVC2. Tompson et al. [2007]
    also reported mutation in EVC (IVS13þ5G>T) produced three alternative splicing in affected individuals.

    How can we defend this mutation without expression studies, which is very difficult in Pakistan.

    Lucie Grodecká


    I agree with Florent. Recent publications conclude that one may use the in silico predictions as a first selection of mutations that possibly affect splicing. But they are still inaccurate even in determinig whether a variant will affect splicing or not - and they are not capable of predicting the splicing outcome (exon skipping, cryptic splice site utilization, intron retention). So the experimental validation is necessary.

    If you want to try anyway, I prefer Sroogle engine (covering several programs for both the splice sites and the splicing regulatory elements predictions). In addition, there is a nice publication evaluating the outcome of splice site predictors: Houdayer et al., 2012. You might be interested.

    All the best, 


    + 1 more attachment

  • Florence Mia added an answer:
    How to detect histone methylation forms?

    I'm trying to measure the activity of a methyltransferase enzyme for histones and would like to be able to quantify the results. I have tried using 3H labeled SAM and measuring the activity by scintillation counting, but the counts are usually very low and only 2-3 fold above background. I could potentially use a film and leave it for several days to increase the signal, but would like to try a faster method or a non-radioactive one. We need to be able to collect signal from mono- di- and trimethylatiion and don't have the antibodies available for all forms to do wb. Also,the amount of substrate that I have is very limited.What is the best way to do this?

    Florence Mia

    A bit of clarification here: I'm doing an in vitro enzyme assay, so I have purified the methyltransferase I'm working with and have the histone substrates and I measure the activity by using 3H-labeled SAM and scintillation counting, but the counts are very low and doing wb is not an option either since antibodies are not available. What other method could I possibly use?

  • Alexander Ruthenburg added an answer:
    Did anyone also recognize a loss in efficiency over time of Zymo-Spin CHIP kit?

    We use the CHIP and purification kit from ZYMO research for CHIP experiments. Now we even took double amount of cells (neurons) but get almost no chromatin anymore (<10ng/ul). The first experiment was fine, but each time we repeated it we got less DNA after shearing, reverse crosslinking and purification. Is there any possibility that we lose DNA somewhere? Or does multiple loading affect the efficency of the columns? Can the columns lose their quality somehow?

    Thanks a lot!

    Alexander Ruthenburg

    Agree with Dan about sonication-- bath sonicators actually decline over time in energy delivered to sample (I suspect this is due to the sonication element being glued to the bath and this coupling erodes with use/time).  

    Also, we never use kits, but I would imagine that formaldehyde degrading in solution rapidly is a problem here.  We always make it fresh from PFA.  See other posts on fresh paraformaldehyde cracking. 

  • Iman Aldybiat added an answer:
    Does anyone have experience with DAVID bioinformatic program?

    I am doing the analysis of my data with David programme. The problem or what I get from the analysis is :similar results when I do interpretation of two conditions when these are up or down regulated. For example: the analysis of A vs B up regulated gives: phosphoprotein 35%, P value 1.2 E10-7. the analysis of A vs B down regulated gives also phosphoprotein 31%, P value 7.4E10-2. The same thing with other molecular functions (ion binding, metal ion binding, zinc finger....). Is this normal ? or shall I use another more specific parameter than percentage of involved genes? thanks a lot 

    Iman Aldybiat

    Thank you Marco. My gene-expression data are from microarray. It is true that I did not looked at the background which is Mus musculus in my experiment. When I do analysis with DAVID:  I do upload of my data and then I choose  the identifier (i.e. Agilent oligo) and gene list  before submit list. I am actually concerning on doing the analysis via IPA ingenuity. What was useful for me and avoided  to see a lot of molecular functions (ion binding, cation binding,.....etc) is that I reduced the number of genes before being treated with DAVID.

  • Tamás I Orbán added an answer:
    MicroRNA binding sites at mRNA?

    Many papers show  that microRNA regulates gene expression at the post transcriptional level (If microRNA increases and its target gene decreases or else microRNA decreases and its target gene increase). 

    My question is whether microRNA and mRNA are both positive regulation. (If microRNA increase and its target gene increase or microRNA decrease and its target gene decrease).

    Tamás I Orbán

    Hi Prabu,

    Just a small addition: there was a paper showng that the same miRNA can also increase TRANSLATION from its target mRNA in a cell cycle dependent manner (Vasudevan, S., Tong, Y., Steitz, J.A., Science, 2007, 318:1931). In such cases, the problem is always to exclude the indirect effects - but in that article the results seemed convincing.

  • Doinita Ispas added an answer:
    Optimal primer concentration for real time PCR?
    Can anyone please tell working concentration of primers (in nanograms/picograms) for real time PCR experiment. Because I have primers stock at 2 microgram concentration.
    Doinita Ispas

    --6126 g/mol means 6126 ug/umol

    --if 6126 ug is 1 umol, then 198.4 ug is 198.4/6126= 0.0323 umol, i.e 32.3 nmol

    --dissolving 32.3 nmol in 323 uL pure water is giving a stock solution of 100 uM (or 100 pmol/uL)

    Use this stock or make a less concentrated intermediate working stock, to your convenience.

  • Zhongyi Hu added an answer:
    What is the appropriate svm type and kernal type and parameters (c,gamma, etc...) in LIBSVM for microarray data?

    I am working on optimizing Gene selection in microarray data for Cancer Classification. I am going to use SVM in (libsvm) as wrapper approach to evaluate Gene subsets using 10 K fold cross validation.

    Microarray data consider as huge dimensional data ( i.e Lymphoma data set consists 4026 Genes 'features' and  62 instances and 3 class labels).

    Does libsvm support multiclass classification, As in my work, Lymphoma & MLL has 3 classes?

    What is the appropriate svm type and kernal type and parameters for the chosen kernal (c,gamma, etc...) in LIBSVM multi class classification  like microarray data?

    Zhongyi Hu

    two papers should be refered to when you employ Libsvm:

    LIBSVM: A Library for Support Vector Machines

    A Practical Guide to Support Vector Classification.

    You can google it to find the pdfs.

    the famous and simple way for parameter optimization of svm is grid search, please refer to:
    A Practical Guide to Support Vector Classification.

    Some of my studies use meta-heuristic methods to optimize the parameters, please read the following paper for a brief overview about parameter optimization of svm:
    A PSO and pattern search based memetic algorithm for SVMs parameters optimization http://www.sciencedirect.com/science/article/pii/S0925231213002038

  • Nicholas Wagner added an answer:
    Can someone advise me on DNA methylation studies in animal (rat) tissues?

    I am doing bisulfite conversion experiment and I don't know how to design my experiment. I use rats from different ages to see if there is a difference of the methylation status of one promoter during development.

    But I don't know how to organize my groups of animals. In literature, this part is poorly developed so I don't know if I can look at the methylation status of pool(s) of animals or animals individually. And is one pool enough to make a comparison on the methylation status between 2 ages? 

    The first experiment I did was one pool of 6 young rats and one pool of 4 old rats, but I don't know if this is correct, if I have to replicate with other pools or if I have to assess the methylation status of each animal individually.

    I think this method is more qualitative than quantitative.

    Thank you for your answer!! Have a nice day :)

    Nicholas Wagner

    Question 1: I don't know how to organize my groups of animals. In literature, this part is poorly developed so I don't know if I can look at the methylation status of pool(s) of animals or animals individually.

    I know that there are very well written publications in this field - look for the ones that are well-written (and be more careful criticizing the publications of people in your field...)! I am quite sure that details are well-described well in the publication I was part of attached below (Kovacheva et al.). If you have any more questions please message me directly here on RG I will be glad to help.

    However, as a quick answer for all, usually each animal is measuerd individually, and then the results are combined/pooled afterwards if necessary, as already stated by Leopold Fröhlich. This of course means (and I would seriously recommend) doing the bisulfite treatment individually for each DNA. After this you have the option of pooling for measurment (which I would not recommend, unless inevitable), or measuring and then pooling results.  The latter is the more common and what we do in our lab. There are, of course, exceptions: if material is limited, for example when investigating oocytes, then you may be forced to pool before bisulfite treatment or measurement to have enough material - but this should not be the case for rat tissues, which are comparatively large. 


    Question 2: The first experiment I did was one pool of 6 young rats and one pool of 4 old rats, but I don't know if this is correct, if I have to replicate with other pools or if I have to assess the methylation status of each animal individually.

    For a first-off check I guess this would have been ok. It would have been better if you had measured animals individually, as pooling may obscure small differences. The consequence that I think this has for you is this: if you see differences between young and old in your analysis as it is now, it makes sense to do a more strictly planned second analysis. However, if you do not see differences, it may be because pooling is hiding small differences.

    In general I would say there are better ways to approach a longitudinal study/ study over time. For example, you could draw blood from the same animals at different ages, as Somnath suggested. That way you can follow the methylation of each animal over time. This will not be possible if you need to sacrifice the animals for your analyses. Then it would be best to compare siblings from the same litter (or at the very least form the same mating pair), and sacrifice some at young and some at old age. You will need setups like these as otherwise other factors, such as the differences between animals of one strain, may influence/obscure your results too heavily and hide the methylation increase over time that you are looking for. 


    Question 3: I think this method is more qualitative than quantitative.

    There are qualitative and quantiative ways to measure bisulfite-treated DNA. You do not state which method you are using after bisulfite conversion. I guess it is Sanger sequencing? If this is the case you will need to sequence mulitple clones and quantify based on that - it will not be possible to quanitfy based on the peaks of only electropherogram! Could you please go a bit more into detail? Then I can tell you more :-)

    Hope this helps and good luck with your analyses!


    • [Show abstract] [Hide abstract]
      ABSTRACT: During gestation there is a high demand for the essential nutrient choline. Adult rats supplemented with choline during embryonic days (E) 11-17 have improved memory performance and do not exhibit age-related memory decline, whereas prenatally choline-deficient animals have memory deficits. Choline, via betaine, provides methyl groups for the production of S-adenosylmethionine, a substrate of DNA methyltransferases (DNMTs). We describe an apparently adaptive epigenomic response to varied gestational choline supply in rat fetal liver and brain. S-Adenosylmethionine levels increased in both organs of E17 fetuses whose mothers consumed a choline-supplemented diet. Surprisingly, global DNA methylation increased in choline-deficient animals, and this was accompanied by overexpression of Dnmt1 mRNA. Previous studies showed that the prenatal choline supply affects the expression of multiple genes, including insulin-like growth factor 2 (Igf2), whose expression is regulated in a DNA methylation-dependent manner. The differentially methylated region 2 of Igf2 was hypermethylated in the liver of E17 choline-deficient fetuses, and this as well as Igf2 mRNA levels correlated with the expression of Dnmt1 and with hypomethylation of a regulatory CpG within the Dnmt1 locus. Moreover, mRNA expression of brain and liver Dnmt3a and methyl CpG-binding domain 2 (Mbd2) protein as well as cerebral Dnmt3l was inversely correlated to the intake of choline. Thus, choline deficiency modulates fetal DNA methylation machinery in a complex fashion that includes hypomethylation of the regulatory CpGs within the Dnmt1 gene, leading to its overexpression and the resultant increased global and gene-specific (e.g. Igf2) DNA methylation. These epigenomic responses to gestational choline supply may initiate the long term developmental changes observed in rats exposed to varied choline intake in utero.
      No preview · Article · Nov 2007 · Journal of Biological Chemistry
  • Yair Botbol added an answer:
    Why do I have upregulated expression in PCR and microarray but I see downregulation in Western blot test?

    I worked about mRNA expression on RCC. One primer shows upregulaton in PCR and microarray but express downregulation in western blot.

    Yair Botbol

    2 possibilities appear to me so far:

    1-post transcriptional: possibility of non-coding mRNA but I am not enough familiar with this field so I cannot give you suggestions.

    2-post translational: to test this hypothesis I recommend to start with the exp I suggested above (protein degradation test)

  • Radoslaw K Ejsmont added an answer:
    What is the best way to identify known transcriptional regulators of a Drosophila gene set?

    e.g. 299 genes have altered mRNA expression in our tissue/mutant/etc. of interest.  I want to find out all the transcriptional regulators of those genes.  We're working in Drosophila melanogaster. 

    Radoslaw K Ejsmont

    You can try i-cisTarget - it's a tool to identify overrepresented regulatory motifs (and thus known TFs) in the specified gene set.

    + 1 more attachment

  • Bharath Reddy added an answer:
    Can I calculate heritability for augmented design with 1 rep & 1 loc for 1 repeated and 2 random checks?

    In 2014, I had planted 210 lines, 3 checks (1 repeated check & 2 random checks) in augmented design 1 replication, 1 location ( 2 loc were planted but lost 1 loc for late freeze damage). These 210 lines comes from 12 populations (Family structure is complex I have 7 wild relatives back crossed to 2 elite parents). In 2015 out of 210 lines, 93 were advanced to next generation based on tillering ability (alpha lattice, 2 replications, 4 locations). I have done BLUPs and BLUEs for 2015 using META & I got heritability for 2015. I have done moving mean analysis using Agro Base Gen II for 2014. End of the day I have to calculate genetic gain we have achieved for grain yield by indirect selection for tillering ability. Please guide me step by step

    Bharath Reddy

    Thank you sir I will talk to Rupa & other statisticians. If I can't answer I will get back to you.



  • 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?

    Lekha Dinesh kumar

    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! 

  • 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 ?
    Lesya Holets

    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.

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.

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