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According to the HGVS guidelines, a letter prefix should be used to indicate the reference sequence used. Accepted prefixes are: “g.” for a genomic reference sequence “m.” for a mitochondrial reference sequence “c.” for a coding DNA reference sequence “n.” for a non-coding DNA reference sequence “r.” for an RNA reference sequence (transcript) “p.” for a ...


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ID mapping This is called ID mapping. It used to be a headache as programmatic access was the only real way, but it is pretty trivial these days. As mentioned in the comments, by far the most popular and easy method is to use Uniprot's list uploader for mapping. The corresponding publication can be found here. You must convert from Gene name to Uniprot KB ...


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ID mapping This is called ID mapping. It used to be a headache as programmatic access was the only real way, but it is pretty trivial these days. As mentioned in the comments, by far the most popular and easy method is to use Uniprot's list uploader for mapping. The corresponding publication can be found here. Example Programmatic access The web user ...


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If you look at the DNA sequence in the patent, you will see that it does not start with ATG and does not end with a stop codon. The disclosed sequence has some additional bases in it, therefore the discrepancy in protein and DNA length. Those additional bases almost always occur in cDNA, e.g. because of polyadenylation, Kozak sequences, etc.


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Briefly speaking you expect more or less uniform coverage for reads. So if you see a systematic difference in quality scores or coverage for minor and major alleles or for forward and reverse strand, it could be an SNV, but most likely it is a technical issue. That's a 'dubious variant'. A lot of such biases are described in "Genotype and SNP calling from ...


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That is a very bad test case. The problem is that the sequences are too short and involve a long repetition. This means that the default gap penalties and the gap-length penalties are not applicable. They are designed to work with longer sequences, where the penalty of inserting a gap can be offset by an increase in matches. In any case you can get bad ...


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Your guess is made without knowing protein substitution probabilities. Optimality of alignment in BLAST is a metrics of the scoring function. The scoring function depends on the word size (length of the seed that initiates an alignment), rewards and penalties for matches and mismatches - gap cost and substitution matrix. In general, BLAST uses BLOSSUM and ...


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Needleman-Wunsch does an end-to-end (global) alignment (BLAST uses Smith-Waterman). Needle from the EMBOSS toolkit performs Needleman-Wunsch alignment. It will report the highest scoring alignment. I am not sure which alignment it reports when there are two of them with equal scores (I don't think it is random). I just tried your case: replaced B with W as ...


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I don't know if this is still needed but Here is a link to the paper where these esoteric variables are defined along with the git-hub site that contains the code for cuffcompare. http://www.sciencedirect.com/science/article/pii/S0888754396902980 https://github.com/cole-trapnell-lab/cufflinks/blob/master/src/cuffcompare.cpp It helped me reason through the ...


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I presume that these epitopes represent peptides that have been eluted from MHC molecules and then sequenced by mass spec. You can then compare these peptide sequences to a reference genome (more specifically to the proteins predicted to arise from the reference genome). If the peptide matches perfectly to a predicted protein, then you assign it that ...


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If you want to compare two specific sequences that you already have, then BLAST itself is not the program you need. BLAST is a program that uses heuristics for rapid searching of a large database for sequences similar to a query sequence. Each of the highest scoring sequences obtained is ‘finished’ for presentation by running an implementation of quite ...


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tBLASTn, BLASTx and tBLASTx can be used. It is clearly illustrated here. If you have just two sequences you need not do a search against the database. You can simply align them using Smith-Waterman or Needleman-Wunsch algorithms, as David mentioned. However, you still need to translate your DNA to protein before doing the alignment. Again, as pointed out ...


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In the biological context, membrane-partitioning is usually referring to the stage in which the transmembrane-destined region of a protein moves from interacting with the water, to interact with the interface of the membrane. In the diagram below showing a four step thermodynamic cycle, the partitioning free energy can be referred to as as ΔGwiu in terms ...


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There have been several Turing machines constructed using DNA computing. One of these machines has been used to solve the boolean satisfiability problem, another was used to solve the bounded post correspondence problem, both NP-hard combinatorial problems which are difficult for conventional computers to solve. Also, a DNA computer was constructed that ...


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An amino acid is an organic molecule containing a carboxylic acid ($-COOH$) group on one end and an amine ($-NH_2$) on the other end. There exist in nature many more amino acids than the 20 proteinogenic α-amino acids. Examples include selenocysteine, pyrrolysine, and carnitine. The structure and character of these are all known, so there would be no ...


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I know the question is old, but for the record: the RCSB PDB is currently working on a project to compress the PDB structural data with a new file format, called MMTF (MacroMolecular Transmission Format). The format uses MessagePack for serialization and does custom compression, achieving ~ 5x advantage over mmCIF gzipped files. Currently the whole PDB ...


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There are three "omic" definitions that might help you. The DNA is the long term storage of the information. You're probably familiar with this concept, and the study of this type of information is genomics. One of the major functions of DNA is it's use as the blueprint for the production of proteins. Proteins carry out all sorts of structural and ...


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At least PacBio do provide some example datasets on their DevNet resource. You should be able to get a lot of different Illumina data from the public data in BaseSpace. I am not sure where to look for official samples from Roche 454, but I wouldn't bother too much as it is discontinued anyway. Rather go for MinIon or IonTorrent data, if you want more.


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You just need to specify the output format with the outfmt flag. clustalo -i sequence.fasta -o output.clu -outfmt=clu should give you the desired output. How to share data? Try using an abbreviated example which people can copy&paste, for longer data you could use something like http://pastebin.com/ but the URL might expire. gi|1709777|...


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I am unsure that how much does it answer your question, but have a look at the homepage of DeltaSVM - In his work, Lee et al. have used similar datasets (only for GRCH37 I reckon) and I am sure they will address some of your queries. As you yourself can better determine which dataset(s) are ones you need.


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To rephrase what you already mentioned in your question: For a KEGG Module to be completed (so that an organism can perform a certain function) you need a certain set of functional units or enzymes. So to evaluate the abilities of an organism you would check its genome for the gene sequences related to the module by performing the logical operation you ...


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At the command line: wget ftp://ftp.ensembl.org/pub/release-84/gtf/gasterosteus_aculeatus/Gasterosteus_aculeatus.BROADS1.84.gtf.gz gunzip Gasterosteus_aculeatus.BROADS1.84.gtf.gz Then in R: library(GenomicFeatures) txdb = makeTxDbFromGFF("Gasterosteus_aculeatus.BROADS1.84.gtf") hist(log10(width(unique(exons(txdb))))) # exons hist(log10(width(unique(...


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The reference genome is a composite sequence from multiple individuals therefore scenario A should hardly occur, if at all. The unsatistying answer is that it is very unlikely that the rare variants of interest to you will be in the reference.



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