3

This example seems to have information on this, looks like it should have this format: A G C T U * A 1 -1 -1 -1 -1 -1 G -1 1 -1 -1 -1 -1 C -1 -1 1 -1 -1 -1 T -1 -1 -1 1 -1 -1 U -1 -1 -1 -1 1 -1 * -1 -1 -1 -1 -1 -1 However, the page I linked is specifically about a bug regarding ClustalW ignoring custom ...


2

Ok, the answer was right here and I missed it, posting it in case anyone else will do the same mistake as me in the future. U = unpaired P = paired H = hairpin B = bulge I = internal loop M = multiloop S = stem (or stack) E = external loop


2

You could use samtools coverage as explained in the manual of samtoools. Here is a example which is also described on the manual site. samtools coverage -r chr1:1M-12M input.bam #rname startpos endpos numreads covbases coverage meandepth meanbaseq meanmapq chr1 1000000 12000000 528695 1069995 9.72723 3.50281 34.4 55.8


2

Most of the reference genomes on NCBI are made of contigs or scaffolds (which is just a fancy name for contigs glued together by a bunch of unknown nucleotides). Hence, by download the whole genome, you also download all the contigs. If you search for a specific contig (presumably a known locus) of a specific species, you can search NCBI for the species name ...


1

There are 190 pairs of different amino acids: $$ {20\choose 2} = 190$$ and 20 pairs of identical amino acids (AA, TT, WW, etc). Combining these, you get all 210 pairs referenced in the article. Another way of seeing this is dividing the 20x20 matrix into 3 parts: the diagonal, all values above the diagonal, and all values below. We have 20 values on the ...


1

Why is this approach not used? Are there any algorithms that use it? As shown in this paper, the MEDIAN STRING problem is NP-Complete on alphabets of carnality greater than 3. While the problem isn't used directly in approaches due to the complexity, the idea of trying to create a consensus sequence between pairs of input and using that as building blocks ...


1

There are a lot of possible ways to count the number of perfect matches in an alignment, and the details will depend on which alignment tool you are using. But, if you use the built-in pairwise aligner in BioPython (Bio.Align.PairwiseAligner(), https://biopython.org/docs/1.75/api/Bio.Align.html), then the easiest way is to use the formatted string ...


1

The following is not the clearest explanation of dN/dS humankind has seen, but I hope you get the gist and core idea of likelihood is absolutely correct. I am wondering if you are thinking of a different test to the way I would do this calculation. This ain't a text book clarity answer. Anyway what you are proposing is a likelihood ratio test (LRT) for ...


1

A very basic solution in python: from Bio import SeqIO seq_dict = {i : rec.seq for i,rec in enumerate(SeqIO.parse("your_msa.aln", "fasta"))} for pos in range(len(seq_dict[0])): pos_bases = ([seqio[i] for seqio in [*seq_dict.values()]]) # If position contains more than one unique base if len([*set(pos_bases)])>1: ...


1

Answer from @cephbirk, converted from comment: What I ended up doing was to choose the read coverage for the lowest covered nucleotide in each codon as each residue's read coverage.


1

As suggested in the comments, the built-in BioPython pairwise aligner should work for this. You can find a short sequence match in a longer sequence using a global alignment with the right score parameters (query_end_... = 0). The other scoring parameters are values that I've found to be good for aligning similar sequences. Example code: from Bio import ...


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