bioinformatics

I am beginning to delve deeper into Perl, but am having trouble writing "Perl-ly" code instead of writing C in Perl. How can I change the following code to use more Perl idioms, and how should I go about learning the idioms? Just an explanation of what it is doing: This routine is part of a module that aligns DNA or amino acid sequences(using Needelman-Wunch if you care about such things). It creates two 2d arrays, one to store a score for each position in the two sequences, and one to keep track of the path so the highest-scoring alignment can be recreated later. It works fine, but I know I

I'm trying to get the mean length of fasta sequences using Erlang . A fasta file looks like this >title1 ATGACTAGCTAGCAGCGATCGACCGTCGTACGC ATCGATCGCATCGATGCTACGATCGATCATATA ATGACTAGCTAGCAGCGATCGACCGTCGTACGC ATCGATCGCATCGATGCTACGATCTCGTACGC >title2 ATCGATCGCATCGATGCTACGATCTCGTACGC ATGACTAGCTAGCAGCGATCGACCGTCGTACGC ATCGATCGCATCGATGCTACGATCGATCATATA ATGACTAGCTAGCAGCGATCGACCGTCGTACGC >title3 ATCGATCGCATCGAT(...) I tried to answser this question using the following Erlang code: -module(golf). -export([test/0]). line([],{Sequences,Total}) -> {Sequences,Total}; line(">" ++ Rest,{Sequences,Total}) ->

I'm trying to implement the Smith-Waterman algorithm for local sequence alignment using the affine gap penalty function. I think I understand how to initiate and compute the matrices required for calculating alignment scores, but am clueless as to how to then traceback to find the alignment. To generate the 3 matrices required I have the following code for j in range(1, len2): for i in range(1, len1): fxOpen = F[i][j-1] + gap xExtend = Ix[i][j-1] + extend Ix[i][j] = max(fxOpen, xExtend) fyOpen = F[i-1][j] + gap yExtend = Iy[i-1][j] + extend Iy[i][j] = max(fyOpen, yExtend) matchScore = (F[i-1]

I would like to generate a plot depicting 14 linear chromosomes for the organism I work on, to scale, with coloured bars at specified locations along each chromosome. Ideally I'd like to use R as this is the only programming language I have experience with. I have explored various ways of doing this e.g. with GenomeGraphs but I have found this is all more complicated than what I want/ displays a lot more data than what I have (e.g. displaying cytogenic bands) and is often specific for human chromosomes. All I essentially want is 14 grey bars of the following sizes: chromosome size 1 640851 2

Closed . This question needs to be more focused. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it focuses on one problem only by editing this post . Which functional programming languages have bioinformatics libraries easily available? (Don't include multi-paradigm languages such as Ruby ) Update : Listing which major functional programming languages don't currently have easy access to bioinformatics libraries is also welcome. Do you consider R as a functional and not multi-paradigm language? If so, R has the biggest set of libraries

I am trying to pass BioPython sequences to Ilya Stepanov's implementation of Ukkonen's suffix tree algorithm in iPython's notebook environment. I am stumbling on the argparse component. I have never had to deal directly with argparse before. How can I use this without rewriting main()? By the by, this writeup of Ukkonen's algorithm is fantastic . I've had a similar problem before, but using optparse instead of argparse . You don't need to change anything in the original script, just assign a new list to sys.argv like so: if __name__ == "__main__": from Bio import SeqIO path = '/path/to

I have a Newick tree that is built by comparing similarity (euclidean distance) of Position Weight Matrices (PWMs or PSSMs) of putative DNA regulatory motifs that are 4-9 bp long DNA sequences. An interactive version of the tree is up on iTol ( here ), which you can freely play with - just press "update tree" after setting your parameters: My specific goal: to collapse the motifs (tips/terminal nodes/leaves) together if their average distances to the nearest parent clade is < X ( ETE2 Python package ). This is biologically interesting since some of the gene regulatory DNA motifs may be