Puriney

#### Author: http://web1.sph.emory.edu/users/hwu30/
#### example scripts to play with BAM files.
library(GenomicRanges)
library(GenomicFeatures)
library(Rsamtools)

## first get gene information from UCSC. This will take 2 minutes or so.
## Or if you have these saved you can load them in using: refGene.hg18=loadFeatures("refGene.hg18.GR.sqlite")
refGene.hg18=makeTranscriptDbFromUCSC(genom="hg18",tablename="refGene")
tx=transcripts(refGene.hg18)

Puriney

require(pROC)
set.seed(1026)
obs<-rep(0:1, each=50)
pred<-c(runif(50,min=0,max=0.8),runif(50,min=0.3,max=0.6))
plot(roc(obs,pred),print.auc=TRUE, auc.polygon=TRUE,max.auc.polygon=TRUE,auc.polygon.col="#DEEBE8",grid=c(0.1,0.2),grid.col=c("blue","red"),print.thres=TRUE)

Puriney

#!/usr/bin/python 
from math import *
strG = str(raw_input("Genome sequence: "))
strR = str(raw_input("Read sequence: "))
strG = strG.upper()
strR = strR.upper()
print "Your genome ref input : " +strG
print "Your read ref input   : " +strR

matrix = []

Puriney

EOS = "$"
'''
All sort algorithm here use the Built-in `sort` function inside Python
''' 
refSeq = raw_input("Your ref: ")
if not refSeq:
	refSeq = "ACAACG"

def print_table(table):
	''' 

Puriney

# Copied From: https://svn.transvar.org/repos/genomes/trunk/pub/src/spliceAnal.py

#!/usr/bin/env python
"""
This file contains code for analyzing novel splicing events from
FindJunctions or Tophat junction features.

If running this interactively, use the writeJunctions method to create
a file that classifies junctions according to whether they are new or
novel, internal or external to a known gene, and their frequency of

Puriney

#!/usr/bin/perl -w 
use strict;

# Notice: 
# BED annotation should be sorted by the GeneID or GeneName ahead
# otherwise duplication in output 
my $inBed = shift || die $!;
my $outUnion = shift || die $!;

open OUT, "> $outUnion";

Puriney

library(ShortRead)

# Root of .bam filename
sname <- "HG00100.unmapped.ILLUMINA.bwa.GBR.low_coverage.20130415"

# Construct path to file
fname <- file.path("alignments",paste(sname,".bam",sep=""))

# Read alignments
bm <- readGappedAlignments(fname)

Puriney

## Demo for HMM
## Reproduce "What is HMM", Sean R Eddy, Nature Biotechnology 2004

S <- c(0,1,rep(0,3))
E <- c(0, 0.9, 0.1, 0, 0)
SS5 <- c(rep(0,3), 1, 0)
I <- c(rep(0,3), 0.9, 0.1)
N <- c(rep(0,4), 1)
myTransMtx <- rbind(S,E, SS5, I, N)
colnames(myTransMtx) <- rownames(myTransMtx)

Puriney

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Puriney

%!TEX TS-program = xelatex
\documentclass[12pt]{scrartcl}  

% The declaration of the document class:

% The second line here, i.e.
% \documentclass[12pt]{scrartcl} 
% is a standard LaTeX document class declaration: 
% we say what kind of document we are making in curly brackets, 
% and specify any options in square brackets.