Introduction to RNA-Seq Transcriptome profiling with iPlant Jason Williams iPlant / Cold Spring...

Introduction to RNA-SeqTranscriptome profiling with iPlant

Jason Williams iPlant / Cold Spring Harbor Laboratory

This training module is designed to demonstrate a workflow in the iPlant Discovery Environment using RNA-Seq for transcriptome profiling.

Question: How can we compare gene expression levels using RNA-Seq data in Arabidopsis WT and hy5 genetic backgrounds?

RNA-Seq in the Discovery Environment

Scientific Objective

LONG HYPOCOTYL 5 (HY5) is a basic leucine zipper transcription factor (TF).

Mutations cause aberrant phenotypes in Arabidopsis morphology, pigmentation and hormonal response.

We will use RNA-Seq to compare WT and hy5 to identify HY5-regulated genes.

Source: http://www.gla.ac.uk/media/media_73736_en.jpg

Sample Dataset

• 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.

RNA-Seq Conceptual Overview

Image source: http://www.bgisequence.com

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

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

…Now What?

RNA-Seq Data - FastQ

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

Bioinformatician

0100110

10 1

*Graphics taken from these publications

The Tuxedo Protocol

*TopHat and Cufflinks require a sequenced genome

The Tuxedo Protocol

Most of RNA-Seq happens before analysisENCODE Project RNA-Seq Standards

$ 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 RNA-Seq Data

Your transformed RNA-Seq Data

RNA-Seq Analysis Workflow

Tophat (bowtie)

Cufflinks

Cuffmerge

Cuffdiff

CummeRbund

Your Data

iPlant Data Store

FASTQ

Disco

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Moving your data in

www.iplantc.org/ds2

Moving your data in

iDrop Desktop – Java Program

Moving your data in

iCommands

The iPlant Discovery Environment

Data preparation

Decompress your data?

Data preparation

Pre-process sequences if needed (e.g., Sabre for de-multiplexing reads, and Scythe for removing primer/adapter sequences)

Image from: http://www.westburg.eu/lp/rna-seq-library-preparation

Data preparation

FASTQC – Quality Control

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Data preparation

Per Base Sequence Quality

BAD GOOD

• 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

Data preparation

BAD GOOD

Fail: most frequently observed mean quality is below 20 (1% error rate)

Per Sequence Quality Scores

Data preparation

BAD GOOD

Per Base N Content

Fail: any position shows an N content of >20%.


GOOD

Sequence Length Distribution

Fail: error if any of the sequences have zero length.


BAD

Overrepresented Sequences

Fail: module will issue an error if any sequence is found to representmore than 1% of the total

Tophat

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 BWA, MAQ, OLego, Stampy, Novoalign, etc.

TopHat

TopHat has a number of parameters and options, and their default values are tuned for processing mammalian RNA-Seq reads.

If you would like to use TopHat for another class of organism, we recommend setting some of the parameters with more strict, conservative values than their defaults.

Usually, setting the maximum intron size to 4 or 5 Kb is sufficient to discover most junctions while keeping the number of false positives low.

- TopHat User Manual

TopHat outputs in IGV

Assembling the Transcripts


Provide a mask file (gtf/gff)

• Tells Cufflinks to ignore all reads that could have come from transcripts in this GTF file.

• Annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore.

- Cufflinks User Manual


1) transcripts.gtfThis GTF file contains Cufflinks' assembled isoforms. The first 7 columns are standard GTF, and the last column contains attributes, some of which are also standardized ("gene_id", and "transcript_id"). There one GTF record per row, and each record represents either a transcript or an exon within a transcript.

2) isoforms.fpkm_trackingThis file contains the estimated isoform-level expression values (FPKM).

3) genes.fpkm_trackingThis file contains the estimated gene-level expression values (FPKM).


Merging the Transcriptomes

Cuffmerge is a meta-assembler; Assembly of Cufflinks transcripts / Reference based assembly

Comparing wild-type to hy5 transcriptomes

• Cuffdiff evaluates variation in read counts for each gene across the replicates this estimate is used to calculate significance of expression changes

• Cuffdiff can identify genes that are differentially spliced or differentially regulated via promoter switching. Isoforms of a gene that have the same TSS are grouped

• Detection rate of differentially expressed genes/transcripts is strongly dependent on sequencing depth



Changes in fragment counts ≠ changes in expression

True expression is estimated by the sum of the length-normalized isoform read counts so the entire transcript must be taken into account.

Cuffdiff Results1. FPKM tracking filesCuffdiff calculates the FPKM of each transcript, primary transcript, and gene in each sample. Primary transcript and gene FPKMs are computed by summing the FPKMs of transcripts in each primary transcript group or gene group.

(tss_groups.fpkm_tracking tracks summed FPKM of transcripts sharing tss_ids)

2) Count tracking filesEstimate of the number of fragments that originated from each transcript, primary transcript, and gene in each sample.

3) Read group tracking filesExpression and fragment count for each transcript, primary transcript, and gene in each replicate.

4) Differential expression testsTab delimited file lists the results of differential expression testing between samples for spliced transcripts, primary transcripts, genes, and coding sequences.

Plus several other outputs (diff splicing, CDS, promoter, etc.)

Differentially expressed genes

Example filtered Cuffdiff results generated in the Discovery Environment.

Differentially expressed transcripts

Example filtered Cuffdiff results generated in the Discovery Environment.

Density Plot

Scatter Plot

Volcano Plot

Expression Plots

Introduction to RNA-Seq Transcriptome profiling with iPlant Jason Williams iPlant / Cold Spring...

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