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Visualization
IGV
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
- Analyze public dataset by using Galaxy and IGV David Coil from UC Davis Genoem Center
Download the raw fastq data GSE19602 from GEO and uncompress fastq.bz2 to fastq (~700MB) file.
- upload one fastq data
- FASTQ Grommer. Convert the data to Galaxy needs. FASTQ quality scores type: Sanger. (~10 minutes)
- Open a new browser tab and go to ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_6.1/all.dir/. Right click the file all.cDNA and copy link location. In Galaxy click 'Upload File from your computer' paste URL to URL/Text entry.
- Scroll down Galaxy and select NGS:Mapping -> Map with BWA.
- For reference genome, choose 'Use one from the history'. Galaxy automatically find the reference file 'ftp://ftp.plantbiology....' from history.
- Library mate-paired => Single-end.
- FASTQ file => 2: FASTQ Groomer on data 1.
- BWA settings to use => Commonly Used.
- Execute (~ 15 minutes)
- We can view the alignment file (sam format) created from BWA by using UCSV or IGV (input is bam or bai format). We now use NGS: SAM Tools to convert sam file to bam file. Click 'SAM-to-BAM converts SAM format to BAM format' tool.
- Choose the source for the reference list => History
- Converts SAM file => 4: Map with BWA on data 2 and data 3.
- Using reference file => 3:ftp://ftp.plantbiology.....
- Execute (~5 minutes)
- We want to create bai file which is a shortcut to IGV. It breaks the data into smaller accessible chunks. So when you pull up a certain cDNA, it goes straight to the subset. Go to the history, click the pencil icon on the file SAM-to-BAM on data 3 and data 4.
- Look at 'Convert to new format' section. Go ahead and click 'Convert'. (< 1 minute). This will create another file.
- Use browser and go to ftp website to download all.cDNA file to desktop. The desktop should contain 3 files - all.cDNA, rice.bam and rice.bai files for IGV.
- Goto http://www.broadinstitute.org/software/igv/download to download IGV which is a java-based application. IGV by default will launch 'Human hg18' genome.
- Goto File => Import Genome. Call it 'rice' and select 'all.cDNA' sequence file. Click 'Save' button.
- Goto File => Upload from File => rice.bam.
- Top right panel is cDNA
- Middle right panel has a lot of 'boxes' which is a read. If we zoom in, we can see some read points to left (backward) while some points to right (forward). On the top is a histogram. For example, a base may be covered by a lot of reads then the histogram will show the high frequence.
- If we keep zoom in, we can see color at the Bottom right panel. Keeping zoom in, we can see the base G, C, T, A themselves.
- Using IGV, we can 1. examine coverage.
- We can 2. check 'alternative splicing'. (not for this cDNA)
- We can 3. examine SNPs for base change. If we see gray color (dark gray is hight quality read, light gray means low quality read), it means they are perfect match. If we see color, it means there is a change. For example, a read is 'C' but in fact it should be 'A'. If a case has many high quality reads, and half of them are 'G' but the reference genome shows 'A'. This is most likely a SNP. This is heterogeisity.
- Tophat - align RNA seq data to genomic DNA
- Suppose we have use Galaxy to upload 2 data. One is SRR034580 and we have run FASTQ Groomer on data 1. The second data is SRR034584 and we also have run FASTQ Groomer on data 2. We also have uploaded reference genome sequence.
- Goto Galaxy and find NGS: RNA Analysis => Tophat.
- reference genome => Use one from the history
- RNA-Seq FASTQ file => 2; FASTQ Groomer on data 1.
- Execute. This will create 2 files. One is splice junctions and the other is accepted_hits. We queue the job and run another Tophat with the 2nd 'groomer'ed data file. We are going to work on accepted_hits file.
- While the queue are running, we can click on 'pencil' icon on 'accepted_hits' job and run the utlity 'Convert to new format' (Bam to Bai). We should do this for both 'accepted_hits' files.
- Cufflinks. We will estimate transcript abundance by using FPKM (RPKM).
- SAM or BAM file of alignmed RNA-Seq reads => tophat on data 2.. accepted_hits
- Use Reference Annotation - No (choose Yes if we want annotation. This requires GTF format. See http://genome.ucsc.edu/FAQ/FAQformat.html#format4. We don't have it for rice.)
- Execute. This will create 3 files. Gene expression, transcript expression and assembled transcripts.
- We also run Cufflinks for 2nd accepted_hits file. (~ 25 minutes)
- Cuffcompare. Compare one to each other.
- GTF file produced by Cufflinks => assembled transcript from the 1st data
- Use another GTF file produced by Cufflinks => Yes. It automatically find the other one.
- Execute. (< 10 minutes). This will create 7 files. Transcript accuracy, tmap file & refmap flie from each assembled transcripts, combined transcripts and transcript tracking.
- We are interested in combined transcripts file (to use in Cuffdiff).
- Cuffdiff.
- Transcripts => combined transcripts.
- SAM or BAM file of aligned RNA-Seq reads => 1st accepted_hits
- SAM or BAM file or aligned RNA-Seq reads => 2nd accepted_hits
- Execute. This will generate 11 files. Isoform expression, gene expression, TSS groups expression, CDS Expression FPKM Tracking, isoform FPKM tracking, gene FPKM tracking, TSS groups FPKM tracking, CDS FPKM tracking, splicing diff, promoters diff, CDS diff. We are interested in 'gene expression' file. We can save it and open it in Excel.
- IGV
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
dCHIP
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 |
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Simple or advanced search --------------------+ |
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v |
My pathways, Lists <------+
^
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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:
