1. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Churchill, March 15
Microarray experiment
Bult, Lecture 5
Image analysis
Bult, Lecture 6
Normalization
Hibbs, Lectures 10 and 11
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation
Blake, Lecture 16 and 17

2. Project Steps Find and Download Array Data Normalize Array Data
Analyze Data
i.e., generate gene lists
Differentially expressed genes, genes in clusters, etc.
Interpret Gene Lists
Use the annotations of genes in your lists
Gene Ontology terms are available for many organisms, but not all

3. Getting The Data Search GEO (or whatever) for a data set of interest.
Download the data files
e.g., Affy .CEL files, Affy .CDF files, etc.
Upload to home directory

4. Normalize the Data
Sent you all a script (2/23/2012) to RMA normalize the Ackerman array data available from my home directory

5. library(affy)
library(makecdfenv)
Array.CDF=make.cdf.env(“MoGene-1_0-st-v1.cdf”)
CELData=ReadAffy()
rma.CELData = rma(CELData)
rma.expr = exprs(rma.CELData)
rma.expr.df = data.frame(ProbeID=row.names(rma.expr),rma.expr)
write.table(rma.expr.df,"rma.expr.dat",sep="\t",row=F,quote=F)

6. What is a library?
What does the ReadAffy() function do?What are possible arguments for the ReadAffy() function?
What class of R object is rma.CELData?
What class of R object is rma.expr?
What class of R object is rma.expr.df?

7. slotNames(CELData)
phenoData(CELData)

8. This is what rma.expr.df looks like in Excel……

9. Plotting summarized probeset intensities across the Ackerman arrays…
Plotting summarized probeset intensities across the Ackerman arrays….(non normalized)
jpeg("boxplot.jpeg")
boxplot(CELData, names=CELData$sample, col="blue")
dev.off()

10. Plotting summarized probeset intensities across the Ackerman arrays…
Plotting summarized probeset intensities across the Ackerman arrays….(normalized)
mydata=rma.expr.df
jpeg("normal_boxplot.jpg")
boxplot(mydata[-1], main = "Normalized Intensities", xlab="Array", ylab="Intensities", col="blue")
dev.off()

11. Next time Posted articles from Gary Churchill.
If you only read one article, read Churchill 2004
See also Gary’s web site:
Look at Sample Data and Tutorial
After that lecture we will begin analysis of microarray data
MAANOVA

13. Gigabases Cost per Kb Cost Throughput
Lucinda Fulton, The Genome Center at Washington University

14. Sequencing Technologies

15. Sequence “Space” Roche 454 – Flow space AB SOLiD – Color space
Measure pyrophosphate released by a nucleotide when it is added to a growing DNA chain
Flow space describes sequence in terms of these base incorporations
AB SOLiD – Color space
Sequencing by DNA ligation via synthetic DNA molecules that contain two nested known bases with a flouorescent dye
Each base sequenced twice
Illumina/Solexa – Base space
Single base extentions of fluorescent-labeled nucleotides with protected 3 ‘ OH groups
Sequencing via cycles of base addition/detection followed deprotection of the 3’ OH
GenomeTV – Next Generation Sequencing (lecture)

16. “Standard” File formats
Sequence containers
FASTA
FASTQ
BAM/SAM
Alignments
BAM/SAM
MAF
Annotation
BED
GFF/GTF/GFF3
WIG
Variation
VCF
GVF

17. Tools Alignments Transcriptomics Variant calling
BLAST: not for NGS
BWA
Bowtie
Maq …
Transcriptomics
Tophat
Cufflinks …
Variant calling
ssahaSNP
Mosaic …
Counting (Chip-Seq, etc)
FindPeaks
PeakSeq

18. FASTQ: Data Format FASTQ References/Documentation Text based
Encodes sequence calls and quality scores with ASCII characters
Stores minimal information about the sequence read
4 lines per sequence
Line 1: begins followed by sequence identifier and optional description
Line 2: the sequence
Line 3: begins with the “+” and is followed by sequence identifiers and description (both are optional)
Line 4: encoding of quality scores for the sequence in line 2
References/Documentation
Cock et al. (2009). Nuc Acids Res 38:

19. FASTQ Example
For analysis, it may be necessary to convert to the Sanger form of FASTQ…For example,
Illumina stores quality scores ranging from 0-62;
Sanger quality scores range from 0-93.
Solexa quality scores have to be converted to PHRED quality scores.
FASTQ example from: Cock et al. (2009). Nuc Acids Res 38:

20. SAM (Sequence Alignment/Map)
It may not be necessary to align reads from scratch…you can instead use existing alignments in SAM format
SAM is the output of aligners that map reads to a reference genome
Tab delimited w/ header section and alignment section
Header sections begin (are optional)
Alignment section has 11 mandatory fields
BAM is the binary format of SAM

21. Mandatory Alignment Fields

22. Alignment Examples
Alignments in SAM format

23. Valid BED files
chr nsv433165
chr nsv433166
chr nsv433167
chr nsv433168
chr nsv433169
chr nsv433170
chr nsv433171
chr chr1:
chr chr1:
chr chr1:
chr chr1:
chr chr1:
chr chr1:
chr chr1:
chr chr1:

24. Galaxy See Tutorial 1 http://main.g2.bx.psu.edu/
Build and share data and analysis workflows
No programming experience required
Strong and growing development and user community

25. Dialog/Parameter Selection
History
Tools

26. Tutorial Web Site Tutorial 5
Tutorial 5

27. RNA Seq Workflow Convert data to FASTQ Upload files to Galaxy
Quality Control
Throw out low quality sequence reads, etc.
Map reads to a reference genome
Many algorithms available
Trade off between speed and sensitivity
Data summarization
Associating alignments with genome annotations
Counts
Data Visualization
Statistical Analysis

28. Typical RNA_Seq Project Work Flow
Tissue Sample
Total RNA
mRNA
cDNA
FASTQ file
Sequencing QC
TopHat
Cufflinks
Gene/Transcript/Exon Expression
Visualization
Statistical Analysis
JAX Computational Sciences Service

29. TopHat http://tophat.cbcb.umd.edu/
TopHat is a good tool for aligning RNA Seq data compared to other aligners (Maq, BWA) because it takes splicing into account during the alignment process.
Figure from: Trapnell et al. (2010). Nature Biotechnology 28:
Trapnell et al. (2009). Bioinformatics 25:

30. TopHat is built on the Bowtie alignment algorithm.
The TopHat pipeline. RNA-Seq reads are mapped against the whole reference genome, and those reads that do not map are set aside. An initial consensus of mapped regions is computed by Maq. Sequences flanking potential donor/acceptor splice sites within neighboring regions are joined to form potential splice junctions. The IUM reads are indexed and aligned to these splice junction sequences.
Trapnell C et al. Bioinformatics 2009;25:

31. Cufflinks Assembles transcripts, Estimates their abundances, and
Assembles transcripts,
Estimates their abundances, and
Tests for differential expression and regulation in RNA-Seq samples
Trapnell et al. (2010). Nature Biotechnology 28: