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UTRs are involved in post-transcriptional regulation. See the wikipedia articles on the 5' and 3' UTRs. Alternative splicing of UTRs allows for exposing/hiding these regulation sites, which may then be useful for differential regulation in differing tissues.


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I'll walk you through the process of determining this: Note that there are two gene names presented: HLA-F and HLA-F-AS1. The "AS" part means, "anti-sense", so it's on the opposite strand...so ignore all of the HLA-F-AS1s. Note the associated chromosomes. chr6 is a normal chromosome, but things like chr6_cox_hap2 aren't. Instead, these are haplotype ...


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skymningen's suggestion of genomecov is absolutely correct. The command I use is: bedtools genomecov -bg -ibam sorted.bam > sorted.wgx This generates a BedGraph file. Unfortunately, the R package sushi had a bug that it didn't generate anything if the interval I wanted is too small. I preferred IGV.


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From your bam-file, you can generate a BED(-graph) of per base coverage with bedtools genomecov (use the -ibam, -d and if you want a BEDgraph also the -bg flags). There are multiple R packages for bioconductor which read and visualise these, including sushi.


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This is actually a very difficult problem, because a given compound may exist in multiple conformations (have multiple structures), and it might not be obvious what conformation is most relevant. For example, a seemingly simple compound like glucose has multiple stereoisomers: if you say "glucose", you probably mean alpha-D-glucose, as opposed to ...


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You are seeking a database of transcription factor binding specificities. Some model organism databases (which are manually curated), such as Wormbase contain some of this annotation. I suggest you search PubMed for papers by Tim Hughes at the University of Toronto. He has published extensively on this topic over the past 5 years. Papers he cites, and ...


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I think your supervisor wants to see if there is inter-cellular variation in the repeat length and if so, calculate the variation. This may be compared with inter-tissue or inter-individual variation. Usually when you take a pool of cells for any assay, you would average out the properties of individual cells. Using sequencing you may actually be able to ...


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It depends on what type of data you have, really. There are methods developed solely for quantifying relative expression based on count data, such as using edgeR or limma-voom. You don't need to correct for gene length to estimate fold-changes of relative expression, what you need to do is normalise by library size first (and in the process obtain log2 ...


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This will be a complete answer, but it's got code, so it won't work as a comment. Working backwards from rtree(3), here's a way to build a phylo object from component parts, which might be helpful: children <- c(5, 1, 2, 3) parents <- c(4, 5, 5, 4) x <- matrix(c(parents, children), ncol = 2) tr <- list(edge = x, tip.label = 1:3, Nnode = 2) ...


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From what I understood of your code and what you are asking I am guessing that you do the following: Generating a virtual set of 40 individuals (lines) of which you have 200 measurements (repetitions). You say that they are full siblings, so they share both parents. Then you use the lmer function (which I am not familiar with) to give you the total ...


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Going by an e-value or bitscore cutoff will give you possible homologs, but it sound like you want to remove redundant sequences. If you want to cluster and combine similar sequences to make a smaller database, you could just go via sequence identity using something like CD-HIT. This, for example, is what's done to produce the UniRef set from the UniProt ...


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Another good alternative is Swiss-PDB viewer. It is free, small, easy to install/use and is available for different OS (I had used it in Windows XP with 256MB RAM! Some features don't work well with low RAM but nonetheless it is not very resource hungry). It is good for the kind of tasks you are interested in. But it is not online (Actually I fail to ...


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This is an answer simply because I can't fit it all in a comment, or several... To install and run PyMOL, you'll need a few extras. They are all free and open-source: Python 2.7.10: Make sure you choose to install pip during the setup process, and also choose to add Python to your PATH, if that option is given. Also, note whether you installed the 32-bit ...


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If there is a such a site, I have never encountered it. Since you say you have the genomic coordinates for each primer it seems to me like all you need is a list of the genomic coordinates of every transcribed exon in the human genome. Assuming that is correct then you are in luck because such lists do exist in the form of a GFF3 file. You should be able to ...


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Well, after 2 weeks and several thousands lines of code I got a demo-version of my revamped annotator up and running, but then I was told to take a look at SnpEff. The tools makes everything I was to implement in my own one, so if anyone ever wants to annotate SNPs with regard to genomic features they hit and possible effects, give SnpEff a try. It's really ...



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