meren

#!/bin/bash

# this is how the directory in which you run this script looks like:
#   $ ls *bam
#   HUZ633103.bbmap.bam      HUZ634093.bbmap.bam      HUZ635444.bbmap.bam      HUZ636473.bbmap.bam
#   HUZ633103.bt1.bam        HUZ634093.bt1.bam        HUZ635444.bt1.bam        HUZ636473.bt1.bam
#   HUZ633103.bt2.bam        HUZ634093.bt2.bam        HUZ635444.bt2.bam        HUZ636473.bt2.bam
#   HUZ633103.bwa.bam        HUZ634093.bwa.bam        HUZ635444.bwa.bam        HUZ636473.bwa.bam
#   HUZ633103.clc.bam        HUZ634093.clc.bam        HUZ635444.clc.bam        HUZ636473.clc.bam
#   HUZ633103.gsnap.bam      HUZ634093.gsnap.bam      HUZ635444.gsnap.bam      HUZ636473.gsnap.bam

meren

from matplotlib.colors import LinearSegmentedColormap, Normalize, rgb2hex
import matplotlib.cm as cm

class ColorMap:
    """
    A simple class to get colors for a given list of values

    Here is an example use:

        data_dict = dict(zip(['e_%d' % e for e in range(0, 10)], range(0, 10)))

meren

meren ~/tmp $ git clone git://github.com/meren/oligotyping.git
Cloning into oligotyping...
remote: Counting objects: 71, done.
remote: Compressing objects: 100% (60/60), done.
remote: Total 71 (delta 39), reused 42 (delta 10)
Receiving objects: 100% (71/71), 27.87 KiB, done.
Resolving deltas: 100% (39/39), done.
meren ~/tmp $ ls
oligotyping

meren

>Sample-01_ReadX
GTTGAAAAAGTTAGTGGTGAAATCCCAGA
>Sample-01_ReadY
GTTGAAAAAGTTAGTGGTGAAATCCCAGA
>Sample-01_ReadZ
GGTGAAAAAGTTAGTGGTGAAATCCCAGA
>Sample-02_ReadN
GTTGAAAAAGTTAGTGGTGAAATCCCAGA
>Sample-02_ReadM
GTTGAAAAAGTTAGTGGTGAAATCCCAGA

meren

[general]
platform = 454
community = Environment-01
output_file = env01.fasta
total_sequences = 250

[member type A]
sequence = GTTGAAAAAGTTAGTGGTGAAATCCCAGAGCTTAACTCTGGAACTGCCATTAAAACTTTTCAGCTAGAGTATGATAGAGGAAAGCAGAATTTCTAGTGTAGAGGTGAAATTCGTAGATATTAGAAAGAATACCAATTGCGAAGGCAGCTTTCTGGATCATTACTGACACTGAGGAACGAAAGCATGGGTAGCGAAGAGGA
ratio = 10

meren

meren ~/tmp/sample-run $ ls mock-env-aligned-c2-s1-a1.0-A0-M0/
COLORS                MATRIX-COUNT.txt      OLIGOS.nexus          RUNINFO.cPickle
ENVIRONMENT.txt       MATRIX-PERCENT.txt    READ-DISTRIBUTION.txt RUNINFO.log
FIGURES/              OLIGOS.fasta          RUNINFO

meren

meren ~/tmp/sample-run $ ls mock-env-aligned-c2-s1-a1.0-A0/
COLORS                MATRIX-PERCENT.txt    RUNINFO
ENVIRONMENT.cPickle   OLIGO-REPRESENTATIVES RUNINFO.cPickle
ENVIRONMENT.txt       OLIGOS.fasta          STACKBAR.png
MATRIX-COUNT.txt      OLIGOS.nexus

meren

meren ~/tmp/sample-run $ ls mock-env-aligned-c2-s1-a1.0-A0-M0/OLIGO-REPRESENTATIVES
00000_GC                                 00001_AT
00000_GC_unique                          00001_AT_unique
00000_GC_unique.png                      00001_AT_unique.png
00000_GC_unique_BLAST.cPickle            00001_AT_unique_BLAST.cPickle
00000_GC_unique_BLAST.xml                00001_AT_unique_BLAST.xml
00000_GC_unique_color_per_column.cPickle 00001_AT_unique_color_per_column.cPickle
00000_GC_unique_distribution.cPickle     00001_AT_unique_distribution.cPickle
00000_GC_unique_entropy                  00001_AT_unique_entropy

meren

#!/bin/bash

#
# put this in a directory where your FASTQ files reside. when you run it, it will 
# generate an 'analyses' directory, in which you will find your perfect V6 reads
# per FASTQ file.
#
# (this script assumes that your FASTQ files are interleaved).
#

meren

import sys

matrix = open(sys.argv[1])
env = open(sys.argv[1] + '.ENV', 'w')

matrix.readline()
samples = matrix.readline().strip().split('\t')[1:]

for line in matrix.readlines():
    fields = line.strip().split('\t')