fomightez

#script to accompany https://www.biostars.org/p/64078/

from Bio import Entrez
Entrez.email = "[email protected]"

protein_accn_numbers = ["ABR17211.1", "XP_002864745.1", "AAT45004.1", "XP_003642916.1" ]
protein_gi_numbers = []

print "The Accession numbers for protein sequence provided:"
print protein_accn_numbers

michaeluzzi

import random
from markov import MarkovGenerator
from markov_by_char import CharacterMarkovGenerator

# word MarkovGenerator
generator = MarkovGenerator(n=2, max=500)

# character MarkovGenerator
#generator = CharacterMarkovGenerator(n=3, max=100)

nathanhaigh

#!/bin/bash
# Usage: deinterleave_fastq.sh < interleaved.fastq f.fastq r.fastq [compress]
# 
# Deinterleaves a FASTQ file of paired reads into two FASTQ
# files specified on the command line. Optionally GZip compresses the output
# FASTQ files using pigz if the 3rd command line argument is the word "compress"
# 
# Can deinterleave 100 million paired reads (200 million total
# reads; a 43Gbyte file), in memory (/dev/shm), in 4m15s (255s)
# 

andreiolariu

# for more info check out http://webmining.olariu.org/interview-with-a-lady-gaga-fan
# made to be run in the ipython console

import urllib, urllib2, time, random
import simplejson as json
  
def fetch_url(url, get=None, post=None):
    user_agent = 'Andrei Olariu\'s Web Mining for Dummies'
    headers = {'User-Agent': user_agent}
    if get:

senko

#!/bin/bash
#
# Watch current directory (recursively) for file changes, and execute
# a command when a file or directory is created, modified or deleted.
#
# Written by: Senko Rasic <[email protected]>
#
# Requires Linux, bash and inotifywait (from inotify-tools package).
#
# To avoid executing the command multiple times when a sequence of

ehazlett

#!/usr/bin/env python
# numpy and biopython are required -- pip install numpy biopython

from Bio import Entrez
from Bio import Medline

MAX_COUNT = 10
TERM = 'Tuberculosis'

print('Getting {0} publications containing {1}...'.format(MAX_COUNT, TERM))