mossmatters

library(jsonlite)
library(readxl)
library(dplyr)
library(ggplot2)

#define the source data location and get a JSON with the assetIDs
url = 'https://beta.healthdata.gov/api/views/gqxm-d9w9'
document <- fromJSON(txt=url)
files = document[["metadata"]]$attachments
excels = files[endsWith(files$filename,".xlsx"),]

mossmatters

#install.packages(c('datasauRus',"ggplot2","dplyr"))
library(ggplot2)
library(datasauRus)
library(dplyr)

#Showing the importance of data visualization
# The anscombe dataset has four x and four y variables 
data("anscombe")
anscombe

mossmatters

#NEXUS

begin data;
dimensions ntax=10 nchar=16;
format datatype=standard symbols="0 1 2";


MATRIX
Anolis_outgroupus		 0000000000000000
Anolis_acutus                   0021021121101101

mossmatters

#!/usr/bin/env python

helptext='''This script identfies branch length outliers on a phylogeny. An outlier is a 
branch with a length that exceeds a percentage of the maximum depth of the tree.

The input is a file containing one tree in newick format, and an optional file containing
a list of outgroup taxa (one per line). 

The output will be an ASCII depiction of each branch with a length exceeding the threshold
(default 25% for ingroups, 75% for outgroups). A PNG file will also be generated for the 

mossmatters

#!/usr/bin/env python

import argparse,sys,os,subprocess,shutil,errno
from Bio import SeqIO

helptext='''
This script is intended to use the results of a BLAST search of a transcriptome 
against a draft genome to conduct more specific exonerate alignments. Each transcript 
is paired with its genome scaffold hit using the BioPython Indexing feature.