Presentation on theme: "Before we start: Align sequence reads to the reference genome"— Presentation transcript:
1 Introductory RNA-seq Transcriptome Profiling of the hy5 mutation in Arabidopsis thaliana
2 Before we start: Align sequence reads to the reference genome The most time-consuming part of the analysis is doing the alignments of the reads (in Sanger fastq format) for all replicates against the reference genome.Make sure everyone has gotten the four replicates loaded into the new Tophat implementation that acceptsmultiple fastq files and runs them serially (TopHat-1.4.1) at the beginning of the lecture. This takes the most time but will finish for mostpeople while you do the lecture.
3 RNA-seq in the Discovery Environment Overview: This training module is designed to provide a hands on experience in using RNA-Seq for transcriptome profiling.Question:How well is the annotated transcriptome represented in RNA-seq data in Arabidopsis WT and hy5 genetic backgrounds?How can we compare gene expression levels in the two samples?
4 Scientific ObjectiveLONG HYPOCOTYL 5 (HY5) is a basic leucine zipper transcription factor (TF).Mutations in the HY5 gene cause aberrant phenotypes in Arabidopsis morphology, pigmentation and hormonal response.We will use RNA-seq to compare the transcriptomes of seedlings from WT and hy5 genetic backgrounds to identify HY5-regulated genes.
5 SamplesExperimental data downloaded from the NCBI Short Read Archive (GEO:GSM and GEO:GSM613466)Two replicates each of RNA-seq runs for Wild-type and hy5 mutant seedlings.
6 Specific Objectives By the end of this module, you should Be more familiar with the DE user interfaceUnderstand the starting data for RNA-seq analysisBe able to align short sequence reads with a reference genome in the DEBe able to analyze differential gene expression in the DEBe able to use DE text manipulation tools to explore the gene expression data
7 Quick Summary Differential Expression: CuffDiff Download Reads from SRAAlign to Genome: TopHatFind Differentially Expressed genesExport Reads to FASTQView Alignments: IGV
8 Pre-Configured: Getting the RNA-seq Data Import SRA data from NCBI SRAExtract FASTQ files from the downloaded SRA archivesThese steps are pre-done to make the work-flow fit into the module time allocation.Spend a moment explaining the provenance (ie getting the data from NCBI, SRA-lite format)Explain that the fastq dumper rescales the quality scores to the Sanger convention for fastqLet them know we did this for them in advance
9 RNA-Seq Conceptual Overview This is a quick visual overview of transcriptome profiling via RNA-seq. It does not go into comparisons but we cover thatwith CuffDiff later.Image source:
10 RNA-Seq Workflow Overview Explain reference-sequence based NGS read alignments.Explain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff
11 Step 1: Align Reads to the Genome Align the four FASTQ files to Arabidopsis genome using TopHatThey will have done this part by now.
12 It uses the BOWTIE aligner internally. TopHatTopHat is one of many applications for aligning short sequence reads to a reference genome.It uses the BOWTIE aligner internally.Other alternatives are BWA, MAQ, TopHat, Stampy, Novoalign, etc.Emphasize that the TopHat aligner is one of many choices. Let them know that others are available in the DE and they can also integrate their own if they want to.
13 RNA-seq Sample Read Statistics Genome alignments from TopHat were saved as BAM files, the binary version of SAM ().Reads retained by TopHat are shown belowSequence runWT-1WT-2hy5-1hy5-2Reads10,866,70210,276,26813,410,01112,471,462Seq. (Mbase)445.5421.3549.8511.3These are the read counts generated by TopHat as part of its alignment analysis.This is a modestly sized data set by NGS standard; good time to mention scalability, Data Store, etc.
14 Prepare BAM files for viewing Index BAM files using SAMtoolsThis is done for them.
15 Using IGV in Atmosphere We already Launched an instance of NGS Viewers in AtmosphereUse VNClient to connect to your remote desktopWe will just show them the slides. Launching an Atmosphere instance is out of scope for this module.Explain that we will cover Atmosphere later in the day/workshop.
17 Integrated Genomics Viewer (IGV) The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.IGV: Make sure you know how to run IGV yourself. Work the example. Play with configuring tracks.You don’t NEED to run IGV in Atmosphere. If that product is flaking out, show users how to do the same thing on their OWN desktop!Use IGV to inspect outputs from TopHat
18 Explain this figure:The gene on the left is differentially expressed (down-regulated in hy5).Compare to gene on right that is not differentially expressed in the two samples.ATG44120 (12S seed storage protein) significantly down-regulated in hy5 mutantBackground (> 9-fold p=0). Compare to gene on right lacking differential expression
19 Other Ways to View Alignment Data WIG->Ensembl Explain that we can also export to popular browsers like Ensembl and UCSC by using the Bam->Wig converter.
20 RNA-Seq Workflow Overview Explain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff
21 CuffDiffCuffLinks is a program that assembles aligned RNA-Seq reads into transcripts, estimates their abundances, and tests for differential expression and regulation transcriptome-wide.CuffDiff is a program within CuffLinks that compares transcript abundance between samplesExplain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff
22 Examining Differential Gene Expression Introducing CuffDiff with replicates
23 Examining the Gene Expression Data Explain that there are various text manipulation tools integrated into the DE (grep, cut, awk etc) for very configurable modular analysisOf the tabular output data from CuffDiff. Then segue into the Filter_CuffDiff_Results App, which consolidates some of these steps.
24 Differentially expressed genes Filter CuffDiff results for up or down-regulated gene expression in hy5 seedlings
25 Differentially expressed genes Example filtered CuffDiff results generated with the Filter_CuffDiff_Results toSelect genes with minimum two-fold expression differenceSelect genes with significant differential expression (q <= 0.05)Add gene descriptions