anpefi

#!/bin/bash

# fasta2oneline.sh

# Convert fasta file in one line: header[tab]sequence (useful for fastqc contaminant file, for example)
# Output to the stdout, redirect it to a file
# Usage: fast2oneline.sh x_example.fa > z_contaminants.txt

INPUT_FILE=$1
cat ${INPUT_FILE} | sed '/^$/d' | sed -n '/^>/!{H;$!b};s/$/ \t/;x;1b;s/\n//g;p'

anpefi

#!/bin/sh
# Script to send web request to MAKER channel in IFTTT.com.
# Arguments:
# $1: Event_name (this is the trigger for IFTTT and SHOULD BE configured in a IFTTT recipe in advance)) i.e: cluster_job
# $2: value1  (To be parsed as Maker variable, and be usable in the triggered event) OPTIONAL
# $3: value2
# $4: value3

# EXAMPLE
# Using recipe https://ifttt.com/recipes/301964 If Maker Event "mailto", then send an email to "value1" with subject "value2" and body "value3"

anpefi

### AMOVA between the three methylation types obtained by the package msap


# AMOVA requires a distance matrix between all samples. 
# Such distance matrix could be only be built with quantitative data or with binary data. 
# In the case of three category variables, we cannot set a distance between two samples 
# (is distance between pattern I and III larger than between II and III?). 
# This is why population studies of MSAP needs to join patterns II and III as methylated in order to 
# build a binary variable to be analysed by AMOVA/PCoA. 
# Anyway, we can made a hack *assuming that distance between different states is equal to 1 

anpefi

#! /usr/bin/env python
__author__="Andres Perez-Figueroa"
__date__ ="$14-jan-2016$"
__version_="2.0"

### Conversor from genepop to metapop
### Requires Biopython

### run: python g2m.py
### The program will ask for your datafile in genepop format

anpefi

#!/bin/bash
#
#$ -j y
#$ -v LD_LIBRARY_PATH
#
#######################################################
#      rEncodeFASTQ.sh
#
# A script to convert a gzipped FASTQ file in Solexa 1.3+ 
# quality offset (QUAL+64 encoding) to a FASTQ with

anpefi

#!/usr/bin/env python
# Git clone all my gists
 
import json
import urllib
from urllib import urlopen

from subprocess import call

import os

anpefi

# Assuming you have your aflp matrix data with samples in rows and 
# loci in columns. The file is a csv file with the format of msap

#Read data
data <- read.csv("aflp.csv", header=TRUE)

#convert to matrix
matN <- as.matrix(data[,4:length(data[1,])])

#Function for sannon index

anpefi

---
title: "Export PCoA plot in other formats"
output: html_notebook
---

## Problem

Some users of the *msap* package could be interested in exporting plots (PCoA) in a format other than png. Currently this is not implemented in msap.

## Solution

anpefi

---
title: "msap: tweak NJ plot"
output: html_notebook
---

## Problem
The NJ plot form msap is awful, specially when the number of samples is big (>10). It was not intended to be very exahsutive, just as an exploratory analysis, but people seems to want to show it.

## Solution
We need to replot the NJ modifying the source code form msap.

anpefi

#Configuration file for Metapop2
# This configuration file is an example of configuration file
# See full specification in https://github.com/anpefi/metapop2 or in the README.md file

# This file would run only the population analysis, skipping all the management

name = Test     # Identification for this set of parameters. it will be included as part of results' filenames. Do not use withespaces
convert = none        # Conversion of the input file to another format. It ignores the remaining configurations. [none/mtp2gp]
debug = false           # Debug mode. Set true to print extra info. [true/false]
dec = 4                 # Floating point precision