Introductory RNA-Seq Transcriptome Profiling

Tutorial on the web…. http://www.iplantcollaborative.org/learning-center/discovery- environment/de-002-characterizing-differential-expression-rna-seq- tuxedo https://pods.iplantcollaborative.org/wiki/display/eot/RNA-Seq_tutorial

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 genom Make sure everyone has gotten the four replicates loaded into the new Tophat implementation that accepts multiple 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 most people while you do the lecture.

Where is the Sample Data?

Step 1: Align Reads to the Genome Align the four FASTQ files to Arabidopsis genome using TopHat They will have done this part by now.

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?

Scientific Objective LONG 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.

Samples Experimental data downloaded from the NCBI Short Read Archive (GEO:GSM613465 and GEO:GSM613466) Two replicates each of RNA-seq runs for Wild-type and hy5 mutant seedlings.

Specific Objectives By the end of this module, you should Be more familiar with the DE user interface Understand the starting data for RNA-seq analysis Be able to align short sequence reads with a reference genome in the DE Be able to analyze differential gene expression in the DE

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 that with CuffDiff later. Image source: http://www.bgisequence.com

RNA-Seq Data …Now What? @SRR070570.4 HWUSI-EAS455:3:1:1:1096 length=41 CAAGGCCCGGGAACGAATTCACCGCCGTATGGCTGACCGGC + BA?39AAA933BA05>A@A=?4,9################# @SRR070570.12 HWUSI-EAS455:3:1:2:1592 length=41 GAGGCGTTGACGGGAAAAGGGATATTAGCTCAGCTGAATCT @=:9>5+.5=?@<6>A?@6+2?:</7>,%1/=0/7/>48## @SRR070570.13 HWUSI-EAS455:3:1:2:869 length=41 TGCCAGTAGTCATATGCTTGTCTCAAAGATTAAGCCATGCA A;BAA6=A3=ABBBA84B<&78A@BA=(@B>AB2@>B@/9? @SRR070570.32 HWUSI-EAS455:3:1:4:1075 length=41 CAGTAGTTGAGCTCCATGCGAAATAGACTAGTTGGTACCAC BB9?A@>AABBBB@BCA?A8BBBAB4B@BC71=?9;B:3B? @SRR070570.40 HWUSI-EAS455:3:1:5:238 length=41 AAAAGGGTAAAAGCTCGTTTGATTCTTATTTTCAGTACGAA BBB?06-8BB@B17>9)=A91?>>8>*@<A<>>@1:B>(B@ @SRR070570.44 HWUSI-EAS455:3:1:5:1871 length=41 GTCATATGCTTGTCTCAAAGATTAAGCCATGCATGTGTAAG BBBCBCCBBBBBA@BBCCB+ABBCB@B@BB@:BAA@B@BB> @SRR070570.46 HWUSI-EAS455:3:1:5:1981 length=41 GAACAACAAAACCTATCCTTAACGGGATGGTACTCACTTTC ?A>-?B;BCBBB@BC@/>A<BB:?<?B?=75?:9@@@3=>: …Now What?

Bioinformagician @SRR070570.4 HWUSI-EAS455:3:1:1:1096 length=41 CAAGGCCCGGGAACGAATTCACCGCCGTATGGCTGACCGGC + BA?39AAA933BA05>A@A=?4,9################# @SRR070570.12 HWUSI-EAS455:3:1:2:1592 length=41 GAGGCGTTGACGGGAAAAGGGATATTAGCTCAGCTGAATCT @=:9>5+.5=?@<6>A?@6+2?:</7>,%1/=0/7/>48## @SRR070570.13 HWUSI-EAS455:3:1:2:869 length=41 TGCCAGTAGTCATATGCTTGTCTCAAAGATTAAGCCATGCA A;BAA6=A3=ABBBA84B<&78A@BA=(@B>AB2@>B@/9? @SRR070570.32 HWUSI-EAS455:3:1:4:1075 length=41 CAGTAGTTGAGCTCCATGCGAAATAGACTAGTTGGTACCAC BB9?A@>AABBBB@BCA?A8BBBAB4B@BC71=?9;B:3B? @SRR070570.40 HWUSI-EAS455:3:1:5:238 length=41 AAAAGGGTAAAAGCTCGTTTGATTCTTATTTTCAGTACGAA BBB?06-8BB@B17>9)=A91?>>8>*@<A<>>@1:B>(B@ @SRR070570.44 HWUSI-EAS455:3:1:5:1871 length=41 GTCATATGCTTGTCTCAAAGATTAAGCCATGCATGTGTAAG BBBCBCCBBBBBA@BBCCB+ABBCB@B@BB@:BAA@B@BB> @SRR070570.46 HWUSI-EAS455:3:1:5:1981 length=41 GAACAACAAAACCTATCCTTAACGGGATGGTACTCACTTTC ?A>-?B;BCBBB@BC@/>A<BB:?<?B?=75?:9@@@3=>: Bioinformagician

Your transformed RNA-Seq Data Your RNA-Seq Data $ tophat -p 8 -G genes.gtf -o C1_R1_thout genome C1_R1_1.fq C1_R1_2.fq $ tophat -p 8 -G genes.gtf -o C1_R2_thout genome C1_R2_1.fq C1_R2_2.fq $ tophat -p 8 -G genes.gtf -o C1_R3_thout genome C1_R3_1.fq C1_R3_2.fq $ tophat -p 8 -G genes.gtf -o C2_R1_thout genome C2_R1_1.fq C1_R1_2.fq $ tophat -p 8 -G genes.gtf -o C2_R2_thout genome C2_R2_1.fq C1_R2_2.fq $ tophat -p 8 -G genes.gtf -o C2_R3_thout genome C2_R3_1.fq C1_R3_2.fq $ cufflinks -p 8 -o C1_R1_clout C1_R1_thout/accepted_hits.bam $ cufflinks -p 8 -o C1_R2_clout C1_R2_thout/accepted_hits.bam $ cufflinks -p 8 -o C1_R3_clout C1_R3_thout/accepted_hits.bam $ cufflinks -p 8 -o C2_R1_clout C2_R1_thout/accepted_hits.bam $ cufflinks -p 8 -o C2_R2_clout C2_R2_thout/accepted_hits.bam $ cufflinks -p 8 -o C2_R3_clout C2_R3_thout/accepted_hits.bam $ cuffmerge -g genes.gtf -s genome.fa -p 8 assemblies.txt $ cuffdiff -o diff_out -b genome.fa -p 8 –L C1,C2 -u merged_asm/merged.gtf \ ./C1_R1_thout/accepted_hits.bam,./C1_R2_thout/accepted_hits.bam,\ ./C1_R3_thout/accepted_hits.bam \./C2_R1_thout/accepted_hits.bam,\ ./C2_R3_thout/accepted_hits.bam,./C2_R2_thout/accepted_hits.bam Your transformed RNA-Seq Data

RNA-Seq Analysis Workflow Tophat (bowtie) Cufflinks Cuffmerge Cuffdiff CummeRbund Your Data iPlant Data Store FASTQ Discovery Environment Atmosphere This is a quick visual overview of transcriptome profiling via RNA-seq. It does not go into comparisons but we cover that with CuffDiff later.

Quick Summary Differential Expression: CuffDiff Download Reads from SRA Align to Genome: TopHat Find Differentially Expressed genes Export Reads to FASTQ View Alignments: IGV

Pre-Configured: Getting the RNA-seq Data Import SRA data from NCBI SRA Extract FASTQ files from the downloaded SRA archives These 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 fastq Let them know we did this for them in advance

Examining Data Quality with FastQC

Examining Data Quality with FastQC

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

It uses the BOWTIE aligner internally. TopHat TopHat is one of many applications for aligning short sequence reads to a reference genome. It uses the BOWTIE aligner internally. Other alternatives are GSNAP, BWA, MAQ, 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.

RNA-seq Sample Read Statistics Genome alignments from TopHat were saved as BAM files, the binary version of SAM (samtools.sourceforge.net/). Reads retained by TopHat are shown below Sequence run WT-1 WT-2 hy5-1 hy5-2 Reads 10,866,702 10,276,268 13,410,011 12,471,462 Seq. (Mbase) 445.5 421.3 549.8 511.3 These 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.

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 mutant Background (> 9-fold p=0). Compare to gene on right lacking differential expression

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

CuffDiff CuffLinks 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 samples 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

Examining Differential Gene Expression Introducing CuffDiff-1.3.0 with replicates

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 analysis Of the tabular output data from CuffDiff. Then segue into the Filter_CuffDiff_Results App, which consolidates some of these steps.

Differentially expressed genes Filter CuffDiff results for up or down-regulated gene expression in hy5 seedlings

Differentially expressed genes Example filtered CuffDiff results generated with the Filter_CuffDiff_Results to Select genes with minimum two-fold expression difference Select genes with significant differential expression (q <= 0.05) Add gene descriptions