http://www.broadinstitute.org/igv/

Address of the bookmark: https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial

• Function(data, options)

Even quit is a function

• q()

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

• Vector: a list of numbers, equivalent to a column in a table
• Data Frame = a collection of vectors. Equivalent to a table

Hint:

• Up/Down arrow keys can be use to cycle through previous commands

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

• Templates for the Data Scientist
  1. A Template for Preparing Data: *OnePageR - *R
  2. A Template for Building Models: *OnePageR - *R
• Getting Started as a Data Scientist
  1. Getting Started with R and Rattle: *Lecture - *Laboratory
  2. Introducing and Interacting with R: *Lecture - *Laboratory
  3. BasicR - OnePage(R) - Writing R scripts
• Dealing With Data
  1. Read Data into R: *OnePageR - *R
  2. Explore and Summarise Data: *OnePageR - *R
  3. Transform Data: *OnePageR - *R
  4. Dealing with Dates and Time: (PDF, R) Dates and Time
  5. Visualising Data with GGPlot2: *OnePageR - *R
  6. Visualising Data with Maps *OnePageR - *R
  7. Spatial (R) Spatial Analysis
  8. Handling Big Data *OnePageR - *R
• Descriptive Analytics
  1. Cluster Analysis: *Lecture - *OnePageR - *R
  2. Association Analysis: *Lecture
• Predictive Analytics
  1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
  2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
  3. SVM (R)
  4. KernLab (R)
  5. NeuralNetworks (R)
  6. NNet (R)
• Model Delivery
  1. Evaluating Models: *OnePageR - *R
  2. Evaluation (R)
  3. Scoring (R)
  4. PMML (R) Exporting Models for Deployment
• Advanced Topics
  1. Text Mining: *OnePageR - *R
• Advanced R Topics
  1. Plots (PDF, R) Miscellaneous Plots
  2. Functions (PDF, R) Writing Functions in R
  3. Parallel (PDF, R) Parallel Execution
  4. Packaging (R) Pulling it Together into a Package
  5. Doing R with Style: *OnePageR - *R
  6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

This book is suitable for classroom use as a general introduction to programming concepts.

More at http://tldp.org/LDP/abs/html/

Address of the bookmark: http://tldp.org/LDP/abs/html/

1. Setting up your computing environment
2. Retrieving and storing sequencing files (your own data or from public sources)
3. Checking sequence quality, trimming, general sequence manipulation
4. Mapping reads to a reference genome
5. Manipulating SAM/BAM alignment files
6. Visualizing data in a genome browser

RNA-Seq

1. De novo transcript discovery and differential analysis with Cufflinks
2. Differential expression analysis with R/Bioconductor
3. Clustering of large expression datasets (microarray or RNA-Seq)

Microarray

1. Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor

General Tips for Data Analysis

1. Excel workarounds, adding gene annotation, X-Y plots tips, etc.

Address of the bookmark: http://homer.salk.edu/homer/basicTutorial/

[uzi@quince-srv2 ~/]$ cp -r /home/opt/MScBioinformatics/linuxTutorial .
[uzi@quince-srv2 ~/]$ cd linuxTutorial
[uzi@quince-srv2 ~/linuxTutorial]$

I have deliberately chosen Awk in the exercises as it is a language in itself and is used more often to manipulate NGS data as compared to the other command line tools such as grep, sed, perl etc. Furthermore, having a command on awk will make it easier to understand advanced tutorials such as Illumina Amplicons Processing Workflow.

In Linux, we use a shell that is a program that takes your commands from the keyboard and gives them to the operating system. Most Linux systems utilize Bourne Again SHell (bash), but there are several additional shell programs on a typical Linux system such as ksh, tcsh, and zsh. To see which shell you are using, type

[uzi@quince-srv2 ~/linuxTutorial]$ echo $SHELL

/bin/bash

Address of the bookmark: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/linux.html

Address of the bookmark: http://denbi-metagenomics-workshop.readthedocs.io/en/latest/index.html

Address of the bookmark: https://astrobiomike.github.io/

Address of the bookmark: http://www.biostat.jhsph.edu/~iruczins/teaching/

1. How to perform basic quality checks on the input data
2. How to run a short read assembler on Illumina data
3. How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
4. How to improve the accuracy of a long read assembly using short reads
5. How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab