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IMGS 2012 Bioinformatics Workshop: RNA Seq using Galaxy.

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Presentation on theme: "IMGS 2012 Bioinformatics Workshop: RNA Seq using Galaxy."— Presentation transcript:

1 IMGS 2012 Bioinformatics Workshop: RNA Seq using Galaxy

2 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 Focus for Today

3 RNASeq Tasks, Tools and File Formats Quality Control Alignment Summarization FastQ, SangerFastQ Cufflinks TopHat FastQC SAM/BAM GTF Differential Gene Expression Cuffdiff,Edge, DESeq, baySeq Task Tool File Format IGV

4 Tools HistoryDialog/Parameter Selection

5 ftp://ftp.ncbi.nlm.nih.gov/pub/church/GenomeAnalysis/ h1-hESC_Sample_Dataset.fastq

6 Data upload review Our data are H1 human embryonic stem cell RNA Seq data from the CalTech encode project. Single end reads from Illumina.

7 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

8 Prior to alignment, perform some quality control (QC) assessments of the data. Here we use FastQC **. **http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

9 FastQC provides a wide range of QC checks. Here we will only look at “Per base sequence quality”

10 Sequence quality per base position  The central red line is the median value  The yellow box represents the inter-quartile range (25-75%)  The upper and lower whiskers represent the 10% and 90% points  The blue line represents the mean quality Good data  Consistent  High Quality Along the reads Bad data  High Variance  Quality Decrease with Length

11 Quality Score Position along sequencing read Our data…

12 Galaxy has several tools for trimming sequences, removing adapters, etc. prior to alignment. Using the information from FastQC, let’s trim our input sequences so that the aggregate quality score is 15.

13 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

14 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.

15 Setting parameters for TopHat in Galaxy Be sure to use the quality trimmed sequences!

16 Does it seem like your Galaxy jobs never finish?! Galaxy is increasingly popular so it can take time for some of these computationally expensive processes to run…don’t restart your job or you will go to the end of the line! Your job will continue to run on the Galaxy servers even if you shut down your computer.

17 For now we have pre- computed data to illustrate the main points!

18 Visualizing alignments in Galaxy When TopHat finishes the alignments are available in BAM format.

19 You can look at the alignments in a variety of browsers…. Which browser you choose is a matter of personal preference.

20 chr19:2,373,346-2,398,357 UCSC Browser…the track and the title of the track are made automatically for you from Galaxy. UCSC also has controls to let you display many other kinds of annotations as tracks.

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22 Click on an element in the TopHat track to see the details of the alignment…all of this information is stored in that very compact BAM file!!!

23 Launch IGV (Integrated Genome Viewer)

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26 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

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

28 There are several ways to generate annotation files for Cufflinks to use. Here we will create an annotation file using the UCSC genome browser tool in Galaxy. A.There are many options for the features to include in the annotation file. B.Cufflinks expects a GTF file format A B

29 Once you have selected your annotations…you can send them directly to your history in Galaxy.

30 Setting parameters for Cufflinks Use the reference annotations you just downloaded…

31 Example of an RNA Seq data set in NCBI’s Gene Expression Omnibus (GEO)…you don’t always need the raw sequences to do RNA Seq, you can start with a SAM or BAM file.

32 SAM files need to be converted into BAM format in order to run Cufflinks…. There’s a tool in Galaxy for that!!

33 Cufflinks output can be downloaded and viewed in Excel.

34 RPKM vs FPKM Reads Per Kilobase of transcript per Million mapped reads (RPKM) – Used for single end sequencing reads – Count # of uniquely mappable reads to a set of exons that constitute a gene prediction/model. Fragments Per Kilobase of exon per Million fragments mapped (FPKM) – Used for paired-end sequence data FPKM is an estimate of the number of reads per transcript – TopHat aligns reads to the genome – Cufflinks assembles reads into transcript models/fragments – Cufflinks counts the number of reads per fragment to estimate FPKM – FPKM is used as an indication of expression level for a gene

35 Quantification of gene expression using RNA Seq can be complicated by reads that don’t map uniquely to the genome. RNA Seq by Expectation Maximization (RSEM) takes mapping uncertainty into account when estimating expression levels.

36 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

37 Differential Gene Expression For RNA Seq data from multiple conditions, Cuffdiff can be used to detect significant differences in transcript expression.. Is the abundance of transcripts different between two samples?

38 edgeR DESeq Bayseq Is there a difference in total expression of a given gene due to treatment conditions?

39 Summing Up Alignments, Assemblies, and Annotations are essential to using Next Gen sequence data for biological investigation – Know the strengths and weaknesses of each Have Fun! But Be Careful! Don’t just go along for the ride!

40 Tutorial Web Site This site will be accessible after the meeting. Check back for updates and new tutorials.

41 / SEQanswers is a very active public discussion board on sequence analysis issues.


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