bioinformatics

I am trying to implement local sequence alignment in Python using the Smith–Waterman algorithm . Here's what I have so far. It gets as far as building the similarity matrix : import sys, string from numpy import * f1=open(sys.argv[1], 'r') seq1=f1.readline() f1.close() seq1=string.strip(seq1) f2=open(sys.argv[2], 'r') seq2=f2.readline() f2.close() seq2=string.strip(seq2) a,b =len(seq1),len(seq2) penalty=-1; point=2; #generation of matrix for local alignment p=zeros((a+1,b+1)) # table calculation and matrix generation for i in range(1,a+1): for j in range(1,b+1): vertical_score =p[i-1][j]

On reading this question , I thought the following problem would be simple using StringSplit Given the following string, I want to 'cut' it to the left of every "D" such that: I get a List of fragments (with sequence unchanged) StringJoin @fragments gives back the original string (but is does not matter if I have to reorder the fragments to obtain this). That is, sequence within each fragment is important, and I do not want to lose any characters. (The example I am interested in is a protein sequence (string) where each character represents an amino acid in one-letter code. I want to obtain

I was reading this post and I wonder if someone can find the way to catch repetitive motifs into a more complex string. For example, find all the repetitive motifs in string = 'AAACACGTACGTAATTCCGTGTGTCCCCTATACGTATACGTTT' Here the repetitive motifs: 'AAAC ACGTACGT AATTCC GTGTGT CCCC TATACGTATACG TTT' So, the output should be something like this: output = {'ACGT': {'repeat': 2, 'region': (5,13)}, 'GT': {'repeat': 3, 'region': (19,24)}, 'TATACG': {'repeat': 2, 'region': (29,40)}} This example comes from a typical biological phenomena termed microsatellite which is present into the DNA. UPDATE 1:

New to coding. New to Pytho/biopython; this is my first question online, ever. How do I open a compressed fasta.gz file to extract info and perform calcuations in my function. Here is a simplified example of what I'm trying to do (I've tried different ways), and what the error is. The gzip command I'm using doesn't seem to work.? with gzip.open("practicezip.fasta.gz", "r") as handle: for record in SeqIO.parse(handle, "fasta"): print(record.id) Traceback (most recent call last): File "<ipython-input-192-a94ad3309a16>", line 2, in <module> for record in SeqIO.parse(handle, "fasta"): File "C:

I have a large Variant Call format (VCF) file (> 4GB) which has data for several samples. I have browsed Google, Stackoverflow as well as tried the VariantAnnotation package in R to somehow extract data only for a particular sample, but have not found any information on how to do that in R. Did anybody try anything like that, or maybe knows of another package that would enable this? In VariantAnnotation use a ScanVcfParam to specify the data that you'd like to extract. Using the sample VCF file included with the package library(VariantAnnotation) vcfFile = system.file(package=

Can someone show me a straightforward solution for how to calculate the longest open reading frame (ORF) in a DNA sequence? ATG is the start codon (i.e., the beginning of an ORF) and TAG , TGA , and TAA are stop codons (i.e., the end of an ORF). Here's some code that produces errors (and uses an external module called BioPython): import sys from Bio import SeqIO currentCid = '' buffer = [] for record in SeqIO.parse(open(sys.argv[1]),"fasta"): cid = str(record.description).split('.')[0][1:] if currentCid == '': currentCid = cid else: if cid != currentCid: buffer.sort(key = lambda x : len(x[1]))

This question is related to: How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid? The solution given there works but I would like to have the names for each taxonomic ids for defined ranks. I have found this on ete3 which can do the job: names = ncbi.get_taxid_translator(lineage) print [names[taxid] for taxid in lineage] but not being python programmer, I am failing to incorporate this into the code given in the link above. Here is what I have tried: import csv from ete3 import NCBITaxa ncbi = NCBITaxa() def get_desired_ranks(taxid, desired