# Tag Info

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I would suggest use RALEE—RNALignment Editor in Emacs. It can get for you the consensus secondary structure, you can move left/right sequences and their secondary structures (you can't do it in JalView!), and more. It's an Emacs mode, so could be a bit hard to start off, but just try, you don't have to use all Emacs features to edit your alignments! The ...

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I suggest you take a look at rna-pdb-tools we do way more than you need! :-) The tools can get you a sequence, secondary structure and much more using various algorithms, and all is well documented http://rna-pdb-tools.readthedocs.io/en/latest/ To get sequence http://rna-pdb-tools.readthedocs.io/en/latest/main.html#get-sequence rna_pdb_tools.py --... 8 We sequence and therefore typically report assemblies as DNA sequences, even if they're actually RNA. 7 There's nothing really special about RNA alignments, you can use any alignment editor, including whichever one you use for protein. That said, a classic and very useful tool for this sort of thing is JalView. It can be installed locally or run as a Java webapp from your browser. Jalview has built in DNA, RNA and protein sequence and structure ... 7 I expect there was a sequencing problem during the last base, where some of the reagents were running low on the sequencer. This won't pose any real problem, RNAseq aligners like STAR will just soft-clip the last base or two if they're mismatches. It's common to see a bit of bias toward the 5' or 3' ends in RNAseq, mostly due to whether poly-A selection was ... 6 Use the following R package for Gene Set Enrichment analysis of RNA-seq data: seqGSEA There is another R package (fgsea) recently published called "Fast Gene Set Enrichment Analysis" by Alexey Sergushichev. 6 You seem to refer to the GSEA provided by the Broad institute, (there are other GSEA algorithms). 1) You can provide whatever you wish, but if you want to know if those gene sets in which side of the ordered list are they, then provide all the list (of genes) you have. 2) GSEA analyize if the order of a given list distributes in certain way the elements ... 6 In the paper mentioned, we used the ScaleData function in Seurat to regress out the number of reads, Rn45s abundance, and percent ribosomal gene transcripts. Ribosomal genes were found with the regular expression ^Rp[sl][[:digit:]]. tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s")) Here's a fuller notebook, and we'... 5 We've found ribosomal RNA to be less of a problem with sequencing that depends on polyA, which suggests the issue might be in the library preparation, rather than the selection. Many polyA RNA library preparation methods involve amplification, rather than selection, which means that existing transcripts that are present in very high abundance (such as rRNA) ... 4 Context The PDB file format is a fixed-column file format designed in 1970s for storing structural models of macromolecules. The format has been around for long time, has many uses, and although it has official spec the files in circulation may not strictly conform to it. It always has a list of atoms with coordinates (the first two lines are added to ... 4 Can’t be done. If you already sequenced then I’m afraid the money is wasted (unless of course the data is good for something else). The standard Illumina basecaller doesn’t deal well with homopolymers and will consequently call incorrect poly(A) lengths. Amongst others, Narry Kim’s lab developed a dedicated method — TAIL-seq — to solve this problem. It’s a ... 4 The edgeR authors recommend that you use a relatively low logFC threshold for glmTreat such as lfc=log2(1.2). A lfc value as high as lfc=log2(2) is seldom required and only would only be appropriate for datasets with really large amounts of DE. The first threshold you used of lfc=2 is equivalent to lfc=log2(4) and is too high for any dataset. There's a few ... 4 I have seen many posts regarding counts to RPKM and TPM. There’s your answer then: FPKM = RPKM. It’s simply a more accurate name. Speaking of RPKM for paired-end data is discouraged because the reference to “read” in this context lends itself to ambiguity. But mathematically the quantity is the same: we are counting fragments, not individual reads (of ... 4 There is no reason your t-test should reproduce edgeR. In fact, edgeR exists because t-test is inappropriate. edgeR does the tests by pooling information from all genes, because with the low number of replicates your t-test doesn't give sufficient statistical power. What you need to do is: check visually your gene and make a decision yourself based on ... 4 You have several options to approach this with WGCNA (weighted correlation network analysis). You can run a WGCNA on the combined set, identify modules and select those lncRNAs for further follow-up that have relatively high intra-modular connectivity (i.e., are intramodular hub genes). I would suggest this approach first. Second option is to run WGCNA on ... 3 Fold-change >= 2 is the same as logFC (log2(fold-change)) >= 1, so your example is doing exactly what you want. logFC is generally easier to think in and work with than fold-change, since (aside from computational reasons) then increases and decreases in expression differ only in sign and are, thus, easier to compare in magnitude (e.g., it's easier to tell ... 3 Although generally I recommend people to be wary about the warnings that FastQC gives (it tends to be overly paranoid), the GC content graphs here do look odd. It's good that you've had a look at it with another program to confirm the observation. They should have a distribution that is fairly close to normal, a bit like this: Without more information about ... 3 As @DevonRyan mentioned, it's very likely that those samples were degraded, which is good justification for excluding them from subsequent analysis. 3 If you want the values by group then subset y to contain the samples of interest and then feed that to aveLogCPM(). 3 Just look for polyA tracts at the end of sequences, and count them if they're larger than ~18 bases. I've done this with MinION cDNA reads by mapping the polyA adapter sequence (with an elongated polyA sequence) to the reads using LAST, and working out the length of aligned sequence. Unfortunately, because the cDNA adapter finishes with a TVN sequence, ... 3 The rRNA genes in that dataset are Rn45s and Rn4.5s. BTW, you have gene counts, not transcript counts. 3 A 90% loss can be rephrased as a 10% chance of detecting anything. So what we want to find is the probability of detecting 0 molecules, when we start with 7 and have 10% probability of success. Once can do that in R as follows: > pbinom(0, 7, 0.1) 0.4782969 So ~50%, as they stated. I suspect that part of the confusion arises from the fact that the ... 3 I'm not sure what you meant but you can take a look at NPDock (disclaimer we wrote that tool). If you have a structure of your protein of interest, you can dock it to the structure of your DNA/RNA of interest. Mind that this is a rigid body docking which means that the structure will not change upon binding. 3 You can usually get away with FDR < 0.1, but that's as high as you can go. This all presumes you're doing follow-up experiments of some sort, of course. I guess you could increase the FDR more, but you're then really increasing the odds that your follow-up experiments will fail. Obviously increasing the FDR will decrease the confidence in the results, it'... 3 Thelog(CPM)\$ of any low-moderately expressed gene will be negative. There is nothing unexpected there. Your statistics are inappropriate for a variety of reasons. Firstly, a CPM is not a robust value that's comparable between samples (this is why CPMs aren't used for statistics). edgeR performs more appropriate normalization and incorporates that into its ...

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Answer: There are no methods that exist for investigating transcript data inside a cell without destroying the cell. Separating RNA from cells requires essentially destroying the cells and using a chemical cocktail to split its constituent components. Alternative 1: One option is to investigate extracellular transcripts of living cells is to centrifuge ...

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Compile with gcc or remove inline from librna/fold.c:776 and librna/fold.c:846.

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Solution I found: (c/p of body) class RNASelect(Select): def accept_residue(self, residue): return 1 if ((residue.id[0] != 'H_ MG') and ((residue.get_resname() == ' A' or residue.get_resname() == ' U' or residue.get_resname() == ' G' or residue.get_resname() == ' C' or residue.get_resname() == ' T') or (residue.id[0][0:2] == 'H_' and (...

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Please look up flavivirus 'double loops' as you described them previously (post for "Coronavirus RNA') and associated RNA secondary structure anomalies for dengue virus and associated vaccine (Butantan) and the yellow fever virus and its vaccine (17D). If you are aware of "double loops", we must be aware of the association of RNA secondardy structure and ...

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To add a more complete answer: the current coronavirus is closely related to the SARS virus that caused the outbreak in 2004, and on which much research has been done. Here is a general review of the coronavirus epidemiology, life cycle etc. I haven't found yet any materials about the RNA structures in the translatable region, however the structures in the ...

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