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If all your samples are, say, E,coli K12 MG1655, there will be very little difference between the published sequence and what you have. If you have another strain of E.coli, you can't count on there being no indels at all between the two strains.


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DNA is a chemical, and therefore, its interactions are governed by its shape. There is no way to look at a DNA sequence and know all the ramifications that changing a letter will have on its shape. I could tell you that changing the first two or last two letters of an intron are highly likely to destroy a splice site, but you can't make hard and fast ...


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This isn't a question with a really well accepted answer yet, and comes up quite a lot in e.g. studies of population variation in transcription factor motifs. Usually, we approximate the sequence preferences of a DNA-binding protein with a position weight matrix. A weight matrix will given you a score for two sequences, so the simplest means of quantifying ...


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There is no standard software, programming language, or library used for computing and graphing biological data. The R language is commonly used for statistical work, but Python (in conjunction with the SciPy stack) and C++ also gets used a lot. Before going further, I should point out you are asking two questions. One about computation and the other about ...


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This is basically metagenomics. Congratulations, you already did the most time consuming step. There are several ways to go from there, but I will talk about the one I know best. There is the metagenomic analysis tool MEGAN. It can read your Blast Output, if it is in the correct format (normal XML or tabular) and will automatically do what you want. For a ...


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It turns out if you set the font.size in the graphNEL object, you can see the node names in the plot of the inducedTermGraph. Setting font.size to >20 worked for me!


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Clustal has reinvented itself as Clustal Omega using Hidden Markov Models, and is particularly suited to the alignment of very many sequences.


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There are multiple ways of doing genome assembly. The term you are probably looking for is "De Bruijn-Graph based assembly". Using this you can find a lot more different explanations of how it is done. Another frequently used method is "Overlap Layout Consensus assembly", which in fact is not based on k-mer counting.


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The FTP download files are documented on the UCSC site (from which they also may be downloaded from a web browser). The page for the human genome is http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/. I don't know which files you downloaded, but I quote three of the descriptions: hg38.2bit - contains the complete human/hg38 genome sequence in ...


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Answer You can't. Clustal doesn't provide the options of setting the sort of restraints you would like. Multiple Sequence Alignment (MSA) is difficult to program and the authors of Clustal have been refining their algorithm for years. If it were perfect and completely robust they might be in a position to add more user tweaking, but at the moment you're ...


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The IUPAC Nomenclature symbol table for RNA and DNA nucleotide sequences (via Wikipedia)


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Partial answer: As for a book on the topic of mathematical modeling of coupled neural oscillators, you can start with: Wilson, H. R. (1999) Spikes, Decisions & Actions: Dynamical Foundations of Neuroscience, Oxford University Press, Oxford UK. author's copy, amz


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Lowercase letters indicate repeat-masked regions. N's represent gaps. See: https://groups.google.com/a/soe.ucsc.edu/d/msg/genome/S4Sx8UdJAwM/tLTpVVzdhFMJ


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Sequence in caps are usually regions of interest, such as exons. N in the DNA alphabet refers to "unknown nucleotide" It can refer to any of A/T/C/G when the actual underlying base is unknown.


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I'll copy/paste my answer from StackOverflow here also. The following code: import csv from ete3 import NCBITaxa ncbi = NCBITaxa() def get_desired_ranks(taxid, desired_ranks): lineage = ncbi.get_lineage(taxid) lineage2ranks = ncbi.get_rank(lineage) ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) return ...


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The methods that come immediately to mind are mostly related to next-generation sequencing. You can do deep sequencing on your sample, which is just increasing the coverage as much as possible to find rare events. You can do RNA-seq to look at the transcriptome, ChIP-seq to look at chromatin modifications, and single-cell sequencing (a form of deep ...


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Roche's Biochemical Pathways works as a big png image and just put labels on the map. But you could try to extract data using queries like http://biochemical-pathways.com/pol/fts/query?query=Glutarate It seems to be legal as it's not prohibited.


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I recommend to filter using transcript_type value from description column. You need only proteine_coding genes. Now you have extra ~10K unprocessed pseudogenes, ~5K antisense genes, ~4K miRNA, ~7K lincRNA and more than thirty other categories of unprocessed pseudogenic stuff. As far as I know current release for GRCh37 is 19th version, not 18.


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I am reluctant to answer with code, but it seems that the community decided that was an appropriate question for Biology.SE. So here is my solution. The idea is to "compress" the two bit that represent each nucleotide, such that each nucleotide will contribute 0 or 1 (not more) to the distance. You could use binary operations to do something like this ...


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A good baseline for this type of research in human genetics is Standards and guidelines for the interpretation of sequence variants from ACMG. It is a guideline for clinicians, and it gives a good sense of good variants data, bad variants data and setting up confidence level. Try to consolidate data from: Population databases GWAS databases Exome ...


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This encoding doesn't make sense as nucleotides are not in the Hamming space. Hamming distance between every two nucleotides is constantly 1, but in binary encoding, it varies from 1 to 2.



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