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As far as I am aware, there are no commercially available re-usable microarrays. The reason for this is most likely that the arrays are really sensitive and you run into the trouble with false positive signals. I have once seen a microarray which has been stripped and still delivered quite some signals in areas which had a strong signal before. You have to ...


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I would recommend just downloading the database, which HUGO allows you to do free of charge. The HUGO website has a "downloads" tab at the top that takes you to the following page http://www.genenames.org/cgi-bin/statistics You will see a table of statistics relating to how many protein- or non-protein-coding genes there are catalogued, etc. Under the ...


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This is a fairly broad question and a book or two will give a more complete answer. I'm assuming you are interested in expression microarrays. Genotyping microarrays are also very popular these days and are quite different though. Where can I understand the matrix of the dataset ? This is a little unclear as a question, but I'll start with a numerical ...


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Here are 3: 1) gene knockout. Just delete the gene from the genome. The function is gone - useful for demonstrating a direct involvement of the gene in the phenotype. As a phenotype, the microarray will register all sorts of reactions to the loss of the gene in addition to the RNA in question being gone. 2) use selection to find mutants for the gene. ...


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Short answer A single pacemaker neuron can generate oscillatory behavior. Background Given our exchange in the comments, I will focus on single neurons with intrinsic oscillatory behavior. For example, thalamocortical relay neurons and inferior olive neurons have intrinsic oscillatory properties, mainly through the interaction of a hyperpolarization-...


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Q: Why not just extract the proteins… A: It’s not just a question of extracting the proteins, you would need to separate them and then isolate each of them. There is currently no practical way to do this for say, 10,000 proteins, which in any case may have multiple forms. The beauty of the RNAseq method is that it does not require physical separation or ...


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$G_k^{'}$ and $R_k^{'}$ are normalized values of $G_k$ and $R_k$. Take say G as $[1,2,3,4]$ and R as $[100,150,200,400]$ as your values and you want to normalize them. This is scaling one of them onto the other and bringing them on an equal level to compare. So in your case the factor is $85$ units. So a unit of R amounts to $85$ units in G. So to scale ...


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It is true that a gene is either being expressed, or not being expressed. However, the degree to which a gene is expressed can vary tremendously. "Degree of expression" basically means the number of times the gene is read by the transcription machinery, which (generally) correlates to the number of copies of mRNA present in the cell, which (generally) ...


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If you google HG00105, among the first hits is geo accession GSM649517 with the title HG00105/NA12878. Channel1: Characteristics gender: Male cell line: lymphoblast cell line HG00105 ethnicity: British from England and Scotland, UK (1000 Genomes codes: GBR) Channel2: gender: female cell line: lymphoblast cell line NA12878 ethnicity: Northwest European ...


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Microarray images (at least for Illumina arrays) will come in .tiff format and are huge compared to the summarized formats (.sdf/.cel). Most places will not archive the raw images (so you won't find them online) and even when you run the microarray, you will often times need to make a special request to output raw images. iirc .jpg images are useless due to ...


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Direct comparison of the course would be difficult between tissues and cultured cells. In stead of time scale, you might want to use markers. For example, Kislinger et al. show the expression peak of SIX1 is 2 days after differentiation; SMAD3, 4 days. I am not a right person who can tell which markers are good, but I believe you could find more information ...


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In RNA sequencing, total RNA is extracted, then mRNAs are purified out from the sample using polyT columns (since mRNAs have a polyA tail, this will attach to a polyT DNA chunk that is attached to some solid surface). This step is necessary because ribosomal RNAs are much more abundant than mRNAs, and for sequencing you only want to use mRNAs. Then the ...


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Single channel refers to the system used to detect the labelled probes. A single channel array, as shown in this slidedeck (slides 6-8) uses one type of molecule for detection. In the example, (slide 8) the optical label is phycoerythrin. Contrast that to a double channel array (e.g slide 7) that uses a "green" or "red" molecule used to label probes. ...


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Why not just extract the proteins and sequence them, quantify them, analyse and draw conclusions? Because proteomics is really hard. To provide one example, there is no protein equivalent of PCR. That means there is no simple way to select and amplify a particular protein from a complex mixture. The invention of PCR was central to the rapid advances in ...


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The RAW signal values from each microarray are transformed and processed to give you normalized signal values for each microarray. The transformation and processing steps are standard but different for each manufacture. Each manufacture uses a different method/technology to obtain signal values. Thus, signal values are not directly comparable between ...


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Here (mic.sgmjournals.org/content/127/1/121.full.pdf) is a link to a paper in which P. shermanii is grown in flask culture. Although the conditions are clearly designed to achieve low oxygen concentrations in the culture (the flask is almost full and is only shaken occasionally), they also indicate that oxygen is not toxic for this organism. Also, this is ...


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Normalization of expression data is a big topic with new methods being published regularly. When approaching something like this you generally want look at people who have done similar things to what you've done, and then once you understand why they did what they did, you can ask what you need to do to answer your questions. Always keep your biological ...


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The borders provide visual cues for the image analysis software to know which spot is which. The spots are also not printed all at once, but by a series of print heads, and the spaces allow for a small amount of error in the alignment of the print heads. It also allows for humans to more easily eyeball the results - the chip map can be printed in groups, and ...


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While you are correct that 'the end output' is always protein and that functional analysis on the mRNA level can ignore things translational control, there good reasons to analyse the transcriptome: 1) Like you said a lot can happen with mRNA: it can be degraded, translated or also be repressed for a time until it's activated again. Most of these control ...


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After clarification via the comments of the OPs question: 1) The procedure you describe is not for standard RNA microarray experiments, it for olgionucleotide microarray. This type of chip is special because it has multiple probes for each gene to allow detection of special features (i.e. mutations, alternative splicing, ...). 2) The reason for the ...


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Yes, it does. That is why you should include positive and negative controls and repeat the experiments multiple time to then average the results. In general, amplification by PCR is biased by sequence-dependence efficiency. For several reasons (among which GC content, annealing mismatches PCR buffer and the timing and temperatures of the cycling steps) a ...


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Anytime you have PCR in a protocol, yes, you might mess up the quantification. So you do as little PCR as you can, so that it stays in the linear range, preserving the different abundances of templates (qPCR after all is the gold standard for quantifying expression), and you hope that there are minimal PCR biases between templates.


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How do I score differential activity of cellular pathways in microarray data (not enrichment)? You would look at downstream genes, which are selective for individual pathways - or genes which have binding sites for transcription factors that sit at the end of your pathway. Depending on the existing literature, and your experiment, and the specific pathway, ...


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If you can call genomic variants from your data, depending on your coverage of the genome, you should be able to map ancestry. For example, if you have sequencing reads, you can align your reads to a reference genome and then call variants using existing pipelines. Once you have variant calls, you could use ancestry-informative markers to infer the ancestry ...


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It turns out that ArrayExpress itself uses various ontologies (dictionaries structured as trees) available at Ontology Lookup Service when it processes users' search queries. Experimental Factor Ontology is the precisely one I was searching for. Medical Subject Headings (MeSH) dictionary turned out to be handy as well.


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Interesting question. The answer lies encoded in our DNA. If you take eye color for example, the genes important here are well known. The most important are OCA2, HERC2, SLC24A4 and TYR. These are involved in different parts of the pigmentation process, any mutations in these genes lead to changes in pigmentation. Since also the variants, which lead to a ...


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If it was detectable in a microarray, the odds are very good for RT-rtPCR. If you are designing your own primers, make sure they span an exon junction, or if the gene is intronless, then span the UTR to exon junction to avoid amplifying DNA.


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Check the beautiful publication of Daniel Ramsköld et al. 2009, which holds the numbers for generally anticipated co-expression. The specific level of co-expression, which applies to your scenario, will depend upon your tissue, your thresholds, and your definition of co-expression. It you look for a co-change of some genes across different specimen (rather ...


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Generally speaking for RNA-seq data, you don't want to correct for GC content or other gene level effects (e.g. length) because you compare expression values between conditions WITHIN a gene. For this reason, it is recommended to use raw counts and not normalized values such as FPKM. See Section 2.7 of the edgeR user manual. This recent benchmark comparing ...


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From the comments: MattDMo: You can compare different samples, which may have started out as different amounts of tissue, by using control genes. The numbers reported by most standard microarray experiments are not absolutely quantitative (There are 2450 copies of this RNA per cell in this sample), but are rather a proportion as compared to a set ...


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