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7

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 ...


6

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


5

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.


3

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 ...


2

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 ...


2

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


2

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 ...


1

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 ...


1

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 ...


1

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 ...


1

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.


1

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


1

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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