darrin t schultz conchoecia

## contig_coords_from_DNA_sequence
#!/usr/bin/env python

myseq = "ACNNNNNNNNNNNNNCATGTACTTGGATCTATCGGTGATCGGATCGGTATGCGTACGAGTGTCAGTCNNNNNNNNNNNNNNNACGTGGTATGCGGCATGCGTAGCGTCAGCTAGCTGATATTGCGTAGCNNNNNNNNNNGGTATGCGTG"

def contig_ranges(seq, sub):
    indices = list(find_all(seq, sub))
    gap_starts = []
    gap_stops  = []
    gap_ranges = []
    prev = -1

## Snakefile
"""
Author: Darrin Schultz @conchoecia
File: Transcriptome assembly, annotation, and db creation

Instructions:
  - To run this script and all of its analyses: make sure that python 3,
    snakemake, and biopython are installed on your Unix computer.
  - Execute the following command: `snakemake --cores 45`, replacing `45`
    with the number of threads available on your machine.
"""

## plot_chromatogram.py
"""
fraction	lum	NaCl_Conc	type
1	4213	0.06	step
2	5123	0.06	step
3	7813	0.06	step
4	12988	0.06	step
5	24373	0.06	step
6	24843	0.1	slope
7	7513	0.1	slope
8	5358	0.2	step

## analyze_positions.sh
#!/bin/bash
# this script makes plots of positions of linker sequences in HiC/Chicago libraries

#!/bin/bash

function processthis {
    OUT1="${LIB}fpos.txt"
    IN1="${LIB}_f.fastq.gz"
    OUT2="${LIB}rpos.txt"
    IN2="${LIB}_r.fastq.gz"

## Snakefile_locus_map_and_reassemble
# The goal of this script is to map reads to a region and to assemble it de novo.
# This is useful for scenarios in which there are gaps between known homologous sequence,
#  like what one might encounter when doing a de novo mitochondrial genome assembly
#  or assembling a gene locus from a transcript.
#

#The steps for this assembly process are.
# 1) Map all of the reads to the scaffold
# 2) Extract all of the read pairs from the original fastq files and make new fastqs.
# 3) Use the new Fastq files in a de novo Spades assembly.

## MiSeqLibraryQC(Snakefile)
import os
from pathlib import Path
import yaml

configfile: "directories.yaml"

# The yaml file should look like this below. Just an object called "directories"
#  and a list of directories in which the pipeline should look for things.
#
#  directories:

## Snakefile
import glob
import os


"""
snakemake interprets some output files as being ambiguous since the processing pipeline is not a DAG.
as a results you must use --allow-ambiguity
example usage:
snakemake --cores 90
"""

## make_notebooks.py
#!/usr/bin/env python3

# script: make_notebooks.py
# author: darrin t schultz
# date  : 20161004

# make_notebooks.py is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

## Teerb.itermcolors
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Ansi 0 Color</key>
	<dict>
		<key>Blue Component</key>
		<real>0.1098039299249649</real>
		<key>Green Component</key>
		<real>0.1098039299249649</real>
	#!/usr/bin/env python

	myseq = "ACNNNNNNNNNNNNNCATGTACTTGGATCTATCGGTGATCGGATCGGTATGCGTACGAGTGTCAGTCNNNNNNNNNNNNNNNACGTGGTATGCGGCATGCGTAGCGTCAGCTAGCTGATATTGCGTAGCNNNNNNNNNNGGTATGCGTG"

	def contig_ranges(seq, sub):
	indices = list(find_all(seq, sub))
	gap_starts = []
	gap_stops = []
	gap_ranges = []
	prev = -1
	"""
	Author: Darrin Schultz @conchoecia
	File: Transcriptome assembly, annotation, and db creation

	Instructions:
	- To run this script and all of its analyses: make sure that python 3,
	snakemake, and biopython are installed on your Unix computer.
	- Execute the following command: `snakemake --cores 45`, replacing `45`
	with the number of threads available on your machine.
	"""
	"""
	fraction lum NaCl_Conc type
	1 4213 0.06 step
	2 5123 0.06 step
	3 7813 0.06 step
	4 12988 0.06 step
	5 24373 0.06 step
	6 24843 0.1 slope
	7 7513 0.1 slope
	8 5358 0.2 step
	#!/bin/bash
	# this script makes plots of positions of linker sequences in HiC/Chicago libraries

	#!/bin/bash

	function processthis {
	OUT1="${LIB}fpos.txt"
	IN1="${LIB}_f.fastq.gz"
	OUT2="${LIB}rpos.txt"
	IN2="${LIB}_r.fastq.gz"
	# The goal of this script is to map reads to a region and to assemble it de novo.
	# This is useful for scenarios in which there are gaps between known homologous sequence,
	# like what one might encounter when doing a de novo mitochondrial genome assembly
	# or assembling a gene locus from a transcript.
	#

	#The steps for this assembly process are.
	# 1) Map all of the reads to the scaffold
	# 2) Extract all of the read pairs from the original fastq files and make new fastqs.
	# 3) Use the new Fastq files in a de novo Spades assembly.
	import os
	from pathlib import Path
	import yaml

	configfile: "directories.yaml"

	# The yaml file should look like this below. Just an object called "directories"
	# and a list of directories in which the pipeline should look for things.
	#
	# directories:
	import glob
	import os


	"""
	snakemake interprets some output files as being ambiguous since the processing pipeline is not a DAG.
	as a results you must use --allow-ambiguity
	example usage:
	snakemake --cores 90
	"""
	#!/usr/bin/env python3

	# script: make_notebooks.py
	# author: darrin t schultz
	# date : 20161004

	# make_notebooks.py is free software: you can redistribute it and/or modify
	# it under the terms of the GNU General Public License as published by
	# the Free Software Foundation, either version 3 of the License, or
	# (at your option) any later version.
	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
	<plist version="1.0">
	<dict>
	<key>Ansi 0 Color</key>
	<dict>
	<key>Blue Component</key>
	<real>0.1098039299249649</real>
	<key>Green Component</key>
	<real>0.1098039299249649</real>