Genome

Visualization

IGV

• Quick start
• UserGuide

RNA seq

   BWA/Bowtie     samtools        
fa ---------> sam ------> sam/bam (sorted indexed, short reads), vcf
   or tophat

Rsamtools    GenomeFeatures                  edgeR (normalization)
--------->   --------------> table of counts --------->

Youtube videos

• David Coil from UC Davis Genoem Center

Download the raw fastq data GSE19602 from GEO and uncompress fastq.bz2 to fastq (~700MB) file.

1. upload one fastq data
2. FASTQ Grommer. Convert the data to Galaxy needs. FASTQ quality scores type: Sanger. (~10 minutes)

Bowtie

Extremely fast, general purpose short read aligner

Tophat

Aligns RNA-Seq reads to the genome using Bowtie/Discovers splice sites.

Linux part.

$ type -a tophat # Find out which command the shell executes:
tophat is /home/mli/binary/tophat
$ ls -l ~/binary

Quick test of Tophat program

$ wget http://tophat.cbcb.umd.edu/downloads/test_data.tar.gz
$ tar xzvf test_data.tar.gz
$ cd ~/tophat_test_data/test_data
$ PATH=$PATH:/home/mli/bowtie-0.12.8
$ export PATH
$ ls
reads_1.fq      test_ref.1.ebwt  test_ref.3.bt2  test_ref.rev.1.bt2   test_ref.rev.2.ebwt
reads_2.fq      test_ref.2.bt2   test_ref.4.bt2  test_ref.rev.1.ebwt
test_ref.1.bt2  test_ref.2.ebwt  test_ref.fa     test_ref.rev.2.bt2
$ tophat -r 20 test_ref reads_1.fq reads_2.fq
$ # This will generate a new folder <tophat_out>
$ ls tophat_out
accepted_hits.bam  deletions.bed  insertions.bed  junctions.bed  logs  prep_reads.info  unmapped.bam

TopHat accepts FASTQ and FASTA files of sequencing reads as input. Alignments are reported in BAM files. BAM is the compressed, binary version of SAM43, a flexible and general purpose read alignment format. SAM and BAM files are produced by most next-generation sequence alignment tools as output, and many downstream analysis tools accept SAM and BAM as input. There are also numerous utilities for viewing and manipulating SAM and BAM files. Perhaps most popular among these are the SAM tools (http://samtools.sourceforge.net/) and the Picard tools (http://picard.sourceforge.net/).

Cufflinks package

Both Cufflinks and Cuffdiff accept SAM and BAM files as input. It is not uncommon for a single lane of Illumina HiSeq sequencing to produce FASTQ and BAM files with a combined size of 20 GB or larger. Laboratories planning to perform more than a small number of RNA-seq experiments should consider investing in robust storage infrastructure, either by purchasing their own hardware or through cloud storage services.

Cufflinks

Cufflinks uses this map (done from Tophat) against the genome to assemble the reads into transcripts.

Cuffcompare

Compares transcript assemblies to annotation

Cuffmerge

Merges two or more transcript assemblies

Cuffdiff

Finds differentially expressed genes and transcripts/Detect differential splicing and promoter use.

Cuffdiff takes the aligned reads from two or more conditions and reports genes and transcripts that are differentially expressed using a rigorous statistical analysis.

Follow the tutorial, we can quickly test the cuffdiff program.

$ wget http://cufflinks.cbcb.umd.edu/downloads/test_data.sam
$ cufflinks ./test_data.sam
$ ls -l
total 56
-rw-rw-r-- 1 mli mli   221 2013-03-05 15:51 genes.fpkm_tracking
-rw-rw-r-- 1 mli mli   231 2013-03-05 15:51 isoforms.fpkm_tracking
-rw-rw-r-- 1 mli mli     0 2013-03-05 15:51 skipped.gtf
-rw-rw-r-- 1 mli mli 41526 2009-09-26 19:15 test_data.sam
-rw-rw-r-- 1 mli mli   887 2013-03-05 15:51 transcripts.gtf

CummeRbund

Plots abundance and differential expression results from Cuffdiff.

Other software

IPA from Ingenuity

Login: There are web started version https://analysis.ingenuity.com/pa and Java applet version https://analysis.ingenuity.com/pa/login/choice.jsp. We can double click the file <IpaApplication.jnlp> in my machine's download folder.

Features:

• easily search the scientific literature/integrate diverse biological information.
• build dynamic pathway models
• quickly analyze experimental data/Functional discovery: assign function to genes
• share research and collaborate. On the other hand, IPA is web based, so it takes time for running analyses. Once submitted analyses are done, an email will be sent to the user.

Start Here

Expression data -> New core analysis -> Functions/Diseases -> Network analysis
                                        Canonical pathways        |
                                              |                   |
Simple or advanced search --------------------+                   |
                                              |                   |
                                              v                   |
                                        My pathways, Lists <------+
                                              ^
                                              |
Creating a custom pathway --------------------+

Resource:

• http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf
• http://libguides.mit.edu/content.php?pid=14149&sid=843471
• http://people.mbi.ohio-state.edu/baguda/PathwayAnalysis/
• IPA 5.5 manual http://people.mbi.ohio-state.edu/baguda/PathwayAnalysis/ipa_help_manual_5.5_v1.pdf
• Help and supports
• Tutorials which includes
  • Search for genes
  • Analysis results
  • Upload and analyze example data
  • Upload and analyze your own expression data
  • Visualize connections among genes
  • Learn more special features
  • Human isoform view
  • Transcription factor analysis
  • Downstream effects analysis

Notes:

• The input data file can be an Excel file with at least one gene ID and expression value at the end of columns (just what BRB-ArrayTools requires in general format importer).
• The data to be uploaded (because IPA is web-based; the projects/analyses will not be saved locally) can be in different forms. See http://ingenuity.force.com/ipa/articles/Feature_Description/Data-Upload-definitions. It uses the term Single/Multiple Observation. An Observation is a list of molecule identifiers and their corresponding expression values for a given experimental treatment. A dataset file may contain a single observation or multiple observations. A Single Observation dataset contains only one experimental condition (i.e. wild-type). A Multiple Observation dataset contains more than one experimental condition (i.e. a time course experiment, a dose response experiment, etc) and can be uploaded into IPA in a single file (e.g. Excel). A maximum of 20 observations in a single file may be uploaded into IPA.
• The instruction http://ingenuity.force.com/ipa/articles/Feature_Description/Data-Upload-definitions shows what kind of gene identifier types IPA accepts.
• In this prostate example data tutorial, the term 'fold change' was used to replace log2 gene expression. The tutorial also uses 1.5 as the fold change expression cutoff.
• The gene table given on the analysis output contains columns 'Fold change', 'ID', 'Notes', 'Symbol' (with tooltip), 'Entrez Gene Name', 'Location', 'Types', 'Drugs'. See a screenshot below.

Screenshots: