Florent Hube 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. 
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.
I am sorry to say that without experimental assay, you cannot tell anything ! You can reveal/hide a donor/acceptor splice site, reveal/hide a enhancer/silencer binding site, and there is competition between splicing factors as well...
Not doable without experiment, except if you do not care to conclude whatever result...Following
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.
--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.Following
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?
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/S0925231213002038Following
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 :)
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!
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.
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)Following
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.
You can try i-cisTarget - it's a tool to identify overrepresented regulatory motifs (and thus known TFs) in the specified gene set.Following
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
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?
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
Hemant Prajapati 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?
Thank you all for useful information.Following
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
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.