pythseq

#!/bin/bash

# A simple loop to serially map all samples.
# referenced from within http://merenlab.org/tutorials/assembly_and_mapping/

# how many threads should each mapping task use?
NUM_THREADS=4

for sample in `awk '{print $1}' samples.txt`
do

pythseq

from collections import deque
import itertools

# Make dictionary of triplets
i = 0
triplet_index = {}
inverse_triplet = {}
for x in list(itertools.product(['A','T','G','C'], repeat=3)):
    triplet_index[''.join(x)] = i
    inverse_triplet[i] = ''.join(x)

pythseq

# requires biopython
# run like:
#   genbank_to_tbl.py "my organism name" "my strain ID" "ncbi project id" < my_sequence.gbk
#   writes seq.fsa, seq.tbl as output

import sys
from copy import copy
from Bio import SeqIO

def find_gene_entry(features, locus_tag):

pythseq

library(rentrez)
library(assertthat)
library(readr)
search_ind <- function(term){
  # get the IDs for a run ID
  # ERR457868 searched, returns 1011219
  results <- entrez_search(db="sra", term=term)$ids
  assert_that(length(results) == 1)
  results
}

pythseq

Tools to generate Jupyter Notebooks from plain (markup) text files:

(Originally posted on twitter, here incorporating responses)

Notedown

Markdown to Jupyter Notebook, and back https://github.com/aaren/notedown

Jupinx

Restructured text (rst) to Jupyter Notebook https://github.com/QuantEcon/sphinxcontrib-jupyter, see https://medium.com/quantecon-blog/introducing-jupinx-60ba9fc12f4f

pythseq

Gimp Procedures

(script-fu-round-corners run-mode image drawable value toggle value value value toggle toggle)

.

Parameter(s)

pythseq

#!/usr/bin/python

import os
import sys
import xml
import gzip
import json
import time
from collections import defaultdict
import pandas as pd

pythseq

#!/bin/bash
# Illustrating the use of job arrays in SLURM
# use the command: sbatch --array=1-100 job_array_demo.sh

# Partition for the job:
#SBATCH -p main

# Multithreaded (SMP) job: must run on one node
#SBATCH --nodes=1

pythseq

#!/bin/bash
# Illustrating the use of job arrays in SLURM
# use the command: sbatch --array=1-100 job_array_demo.sh

# Partition for the job:
#SBATCH -p main

# Multithreaded (SMP) job: must run on one node
#SBATCH --nodes=1

pythseq

# Get a list of all organisms
curl -s  "http://rest.kegg.jp/list/organism"  > organisms-all.txt

# Get just a few of interest
cat organisms-all.txt | awk '$2~/^(hsa|mmu|rno|cfa|bta|gga|xla|xtr|dre|dme|cel|ath|ehi|tgo|eco|sau|mtu|mav|cje|ccol)$/' > organisms-of-interest.txt

# Get the accession codes for each
cut -f1 organisms-of-interest.txt > organisms-of-interest-codes.txt

# Make a directory to put all the kgml files downloaded