Presentation on theme: "Biological question Differentially expressed genes Sample class prediction etc. Testing Biological verification and interpretation Microarray experiment."— Presentation transcript:
Biological question Differentially expressed genes Sample class prediction etc. Testing Biological verification and interpretation Microarray experiment Estimation Experimental design Image analysis Normalization Clustering Discrimination Churchill, March 15 Bult, Lecture 5 Bult, Lecture 6 Hibbs, Lectures 10 and 11 Blake, Lecture 16 and 17
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
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
Normalize the Data Sent you all a script (2/23/2012) to RMA normalize the Ackerman array data available from my home directory
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?
This is what rma.expr.df looks like in Excel……
Plotting summarized probeset intensities across the Ackerman arrays….(non normalized) jpeg("boxplot.jpeg") boxplot(CELData, names=CELData$sample, col="blue") dev.off()
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
Gigabases Cost per Kb Lucinda Fulton, The Genome Center at Washington University CostThroughput
Sequence “Space” Roche 454 – Flow 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) –
“Standard” File formats Sequence containers FASTA FASTQ BAM/SAM Alignments BAM/SAM MAF Annotation BED GFF/GTF/GFF3 WIG Variation VCF GVF
FASTQ: Data Format FASTQ – 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:
FASTQ Example FASTQ example from: Cock et al. (2009). Nuc Acids Res 38: 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 Solexa quality scores have to be converted to PHRED quality scores.
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
Mandatory Alignment Fields
Alignment Examples Alignments in SAM format
chr nsv chr nsv chr nsv chr nsv chr nsv chr nsv chr nsv chr chr1: chr chr1: chr chr1: chr chr1: chr chr1: chr chr1: chr chr1: chr chr1: Valid BED files
Galaxy See Tutorial 1 Build and share data and analysis workflows No programming experience required Strong and growing development and user community
Tools HistoryDialog/Parameter Selection
Tutorial Web Site Tutorial 5
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
Typical RNA_Seq Project Work Flow Sequencing Tissue Sample Cufflinks TopHat FASTQ file QC Gene/Transcript/Exon Expression Visualization Total RNA mRNA cDNA Statistical Analysis JAX Computational Sciences Service
TopHat Trapnell et al. (2009). Bioinformatics 25: Figure from: Trapnell et al. (2010). Nature Biotechnology 28: 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.
Trapnell C et al. Bioinformatics 2009;25: TopHat is built on the Bowtie alignment algorithm.
Cufflinks Trapnell et al. (2010). Nature Biotechnology 28: Assembles transcripts, Estimates their abundances, and Tests for differential expression and regulation in RNA-Seq samples