Try import MySQLdb db = MySQLdb.connect(host=”localhost”, user=”user”, passwd=”password”, db=”database”) cur = db.cursor() cur.execute(“SELECT common_name , genus , species FROM table WHERE sequence LIKE ‘MDPSSID%'”) 1 solved Searching through database with python
To get started with, FASTA is text file format. To write text files check MATLAB documentation of fopen, fprintf and fclose. To load the text from the data files you’ve written you can use fopen, fscanf and fclose. Actually, MATLAB has fastainfo, fastaread and fastawrite too. You should check MATLAB documentation of these commands and … Read more
These are the steps I would take: 1) align the sequences 2) read each column of the alignment and produce a list of the different possible amino acids in each position 3) each position can now be represented by a list which is easily converted to a regular expression For 1st three positions it would … Read more
Many options here. Unfortunately you don’t show any effort to resolve the problem or even to make it clear and reproducible. For example, using ifelse : dat <- read.table(text=””aaa” 28 “aac” 8 “aag” 20″) dat$V1 <- with(dat,ifelse(V1 %in% c(‘aaa’,’aag’),’K’,’N’)) dat V1 V2 1 K 28 2 N 8 3 K 20 EDIT If I want … Read more
I suppose you have a sequence of nucleotides from DNA and you want to know if there is any gen there in any of the three frames of the sequence. I recommend to use Biopython to do it but there are other ways to make it without using the “standard” solution. Here I present one … Read more
create the spans with javascript and style the spans with css: http://codepen.io/bhlaird/pen/Jdiye Javascript $(‘document’).ready(function() { $(‘.protein’).each(function() { var target = $(this).html(); target = target.split(“”); var result = “”; for (var i = 0, len = target.length; i < len; i++) { result += ‘<span class=”‘ + target[i] + ‘”>’ + target[i] + ‘</span>’; } $(this).html(result); … Read more
I think something like this should do what you want. We use a hash to gather the ‘reference’ file and turn it into a set of keys with an empty array. Then we iterate on the other files, extracting ‘3 values’ as key, and the last value as an actual value. And then we compare … Read more
Item 1: I’m not versed in k-mer counting and the definition of x is a bit shaky. If you could update your question, then I’ll try to address point 1. But, to me it seems as if you just need to use an algorithm that converts from base 10 back to base 4? Item 2: … Read more
We may use either of the following: do.call(function (…) paste(…, sep = “-“), rld[groups]) do.call(paste, c(rld[groups], sep = “-“)) We can consider a small, reproducible example: rld <- mtcars[1:5, ] groups <- names(mtcars)[c(1,3,5,6,8)] do.call(paste, c(rld[groups], sep = “-“)) #[1] “21-160-3.9-2.62-0” “21-160-3.9-2.875-0” “22.8-108-3.85-2.32-1” #[4] “21.4-258-3.08-3.215-1” “18.7-360-3.15-3.44-0” Note, it is your responsibility to ensure all(groups %in% names(rld)) … Read more
The d-neighborhood of the k-mer Pattern is the collection of all k-mers that are at most Hamming distance d from Pattern solved The d-neighborhood of the k-mer Pattern is the collection of all k-mers that are at most Hamming distance d from Pattern
Why not learn the numerical representations for each base? This is a common problem in Neural Machine Translation, where we seek to encode “words” with a meaning as (naively) numbers. The core idea is that different words should not be represented with simple numbers, but with a learned dense vector. The process of finding this … Read more
Not completely clear on what you want; maybe this: my $seq; my %idSeq; while ( my $line = <$INPUT> ) { if ( my ($name) = $line =~ /^>(.*)/ ) { $idSeq{$name} = length $seq || 0; } else { chomp $line; $seq .= $line; } } which produces: $seq = ‘ACGTACGTACGTACCCCGGCCCCTAAAAAAAAAAAT’; %idSeq = ( … Read more