Galaxy RNA-Seq Analysis: Tuxedo Protocol

ChangBum Hong, KT Bioinformatics, GenomeCloud SCIC genome-cloud.com

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Introduction• RNA-Seq

• Transcriptome assembly

• Qualitative identification of expressed sequence

• Differential expression analysis

• Quantitative measurement of transcript expression

• RNA-Seq Applications

• Annotation: Identify novel genes, transcripts, exons, splicing events, ncRNAs

• Detecting RNA editing and SNPs

• Measurements: RNA quantification and differential gene expression

Experimental design• What are my goals?

• Transcriptome assembly?

• Differential expression analysis?

• Identify rare transcripts?

• What are the characteristics of my system?

• Large, complex genome?

• Introns and high degree of alternative splicing?

• No reference genome or transcriptome?

Splicing

AssemblyExpression

Differentially expressed

Experimental Outputs

Sequencing• Platforms

• Library preparation

• Multiplexing

• Sequence reads

• File names

• Fastq format(Formats vary)

• 4 lines per readIllumina Read ID

Data Quality Control

• Data Quality Assessment

• Identify poor/bad sample

• Identify contaminates

• Trimming: remove bad bases from read

• Filtering: remove bad reads from library

Read Mapping• Alignment algorithm must be

• fast

• able to handle SNPs, indels, and sequencing errors

• allow for introns for reference genome alignment

• Input

• fastq read library

• reference genome index

• insert size mean and stddev(for paired-end libraries)

• Output

• SAM (text) / BAM (binary) alignment files

Differential Expression

• Cuffdiff (Cufflinks package)

• Pairwise comparisons

• Differnetial gene, transcript, and primary transcript expression

• Easy to use, well documented

• Input: transcriptome, SAM/BAM read alignments

Transcriptome Assembly• RNA-Seq

• Reference genome

• Reference transcriptome

• RNA-Seq

• Reference genome

• No reference transcriptome

• RNA-Seq

• No reference genome

• No reference transcriptome

RNA

FASTQ

Reads

Experimental Design

Sequencing

Reference Genome

FASTA

Referecne Transcriptome

GFF/GTF

Data Quality Control

FASTQ

Tuxedo protocol

Combining tools in a pipeline• Linux Command-line Tools

• Shell script, Makefile

• GUI Based pipeline

• DNANexus

• SevenBridegs Genomics

• Galaxy

• Open Source

• Wrapper for command line utilites

• Workflows

• Save all steps you did in your analysis

• Return the entire analysis on a new dataset

• Share your workflow with other people

How to use Galaxy?

NO WAIT TIMES

NO STORAGE QUOTAS

NO JOB

SUBMISSION LIMITS

NO DATA

TRANSFER BOTTLENECKS

NO IT

EXPERIENCE REQUIRED

NO REQUIRED

INFRASTRUCTURECOST

GALAXY MAIN Free

LOCAL GALAXY Free ?

CLOUD GALAXY

(AMAZON)

동일사양 대비 약 2배 (KT의)

SLIPSTREAM GALAXY

$19,995 (2천2백만원)

KT GenomeCloud

GALAXY

시간당 740원부터

GALAXY MAIN: User disk quotas 250GB for registered users, maximum concurrent jobs: 8

Outline of tutorial

• Starting Galaxy

• Mapping with Tophat

• Workflows

• Visualizing alignment with IGV

• Computing differential expression with cuffdiff

• Cuffdiff visuaalization with CummeRbund

Starting Galaxy

• Tutorial Dataset

• Accessing Galaxy

• Import files for one sample into current history

• Set file attributes

• Run FastQC

Tutorial Dataset• FASTQ files (fastq): Sequence Reads

• Reference (fasta): Genome Sequence (galaxy default)

• Geneset (GTF / GFF3): Reference Geneset

• Bowtie2 index: Reference index files for Bowtie2 (galaxy default)

Tutorial Dataset Reference & Gene sets

• Ensembl

• http://www.ensembl.org/info/data/ftp/index.html

Tutorial Dataset Reference & Gene sets

•illumina iGenomes • The iGenomes are a collection of reference sequences and annotation files for commonly analyzed

organisms. The files have been downloaded from Ensembl, NCBI, or UCSC, and chromosome names have been changed to be simple and consistent with their download source. Each iGenome is available as a compressed file that contains sequences and annotation files for a single genomic build of an organism.

• http://support.illumina.com/sequencing/sequencing_software/igenome.ilmn

Tutorial Dataset Sequencing data

•Sequencing data (Drosophila melanogaster) • Gene Expression Omnibus at accession GSE32038

• http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32038

Biological replicates vs. technical replicates

Biological Replicates

Technical Replicates

Accessing Galaxy• Open a web browser and navigate to Galaxy website usegalaxy.org or www.genome-cloud.com

• Log in with username and password

GenomeCloud (genome-cloud.com)

select galaxy service

Tools pane

Center pane

History pane

when your galaxy is ready you will recive the e-mail

access the galaxy via public ip address

you can register via user menu > register

Import files• Open a web browser and navigate to Galaxy website usegalaxy.org or www.genome-cloud.com

• Log in with username and passwordexample fastq and gtf files are located in shared data > RNA-Seq with Drosophila melanogaster

import data into your history panel (read to analysis)

Set file attributes• In the history pane click on the pencil icon

• Enter “fastqsanger” (It will takes time)

Sanger Phread+33 fastqsanger (cassava 1.8 ▲ ) Ilumina 1.3 Phread+64 fastqillunina (cassava 1.8 ▼) Solexa Solexa+64 fastqsolexa

Tophat options --solexa-quals: Use the Solexa scale for quality values in FASTQ files --solexa1.3-quals: Phred64/Illumina 1.3~1.5 !BWA options -l : The input is in the Illumina 1.3+ read format (quality equals ASCII-64) !GenomeCloud (g-Analysis)

CASAVA 1.8.2 Quality Score (or Q-score)

Error probability

Quality Score Encoding

Run FastQC• Load the FastQC tool from the tool pane

• Set the input file (repeat this step on the C1, C2 all piar files)

When fastqc has finished running, click on the eye on the FastQC output file to display

wait running done error

Galaxy status

Per base sequence quality

Per sequence quality score

Per base sequence content

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

Per base GC content

Per sequence GC content

Per base N content

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

Sequence Length Distribution

Sequence Duplication Levels

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

illumina (in-house data)

IonTorrent (in-house data)

illumina (good dataset in FastQC homepage)

illumina (bad dataset in FastQC homepage)

Mapping with Tophat

• Initial Tophat run

• Determine insert size

• Rerun Tophat with correct insert size

• Review mapping statistics

Initial Tophat run• Use Full Tophat paramters

• Paired-end FASTQ files, Select reference genome, Use Own Juctions(Yes), Use Gene Annotation Model(Yes)

• Gene Model Anntations (use GFF file)

Determine insert size• Load the insert size tool “NGS: Picard (beta) -> Insertion size meterics”

Determine insert size• Click “eye” icon

• Identify the MIN_INSERT_SIZE (198)

Rerun Tophat• Click any one of the Tophat2 output files in the history panne

• Click on the circular blue arrow icon

• Change the “Mean Inner Distance between Mate Pairs” (198)

Tophat Output• unmapped.bam (BAM)

• accepted_hits.bam (BAM): a list of read alignments in BAM/SAM format

• junctions.bed (BED): list BED track of junctions reported by Tophat where each junction consists of two connected BED blocks where each block is as long as the max overhang of nay read spanning juction

• deletions.bed (BED): mentions the last genomic base before the deletion

• insertions.bed (BED): mentions the first genomic base of deletion

Load files into IGV• Click on the “accepted hits” file in the history pane

• Click on the “display with IGV web current”

• A file named “igv.jnlp” will be downloaded by your browser

• Open with text editor copy BAM file location

http://www.sabiosciences.com/rt_pcr_product/HTML/PADM-000Z.html

IGV with Housekeeping gene

Load files into IGV• Enter “Act42A” in the search box to view the reads aligning

• Right-click on the coverage track and select “Set Data Range” (max value to 4372)

Housekeeping gene: Act42A

Set max value

IGV with Differential Expression

Keyword: regucalcin (calcium-binding protein)

this gene has four isoforms

Load files into Trackster• Click on the “accepted hits” file in the history pane

• Click on the graph icon and select “Trackster”

• Select bam files

create new group ‘Add group’drag into new group

move to regucalcin gene

set max value

Run cuffdiff• Load the Cuffdiff tool: “NGS:RNA Analysis->Cuffdiff ”

• Perform replicate analysis(Yes)

• Add new Group / Add new Replicate

Cuffdiff output• Genes: gene differential FPKM

• Isoforms: Transcript differential FPKM

• CDS: Coding sequence differential FPKM

View and filter cuffdiff output

• Differential Gene Expression (DGE)

• Filter out genes with significant change in expression with a log fold-change of at least 1 “C14 == ‘yes’ and abs(c10)>1” in the “With following condition” text box

Cuffdiff visualization with CummeRbund

• Load the CummeRbund tool: NGS:RNA Analysis->cummerbund

• Plot type: Density, check the “Replicates” box

Samples have similar density distribution(density plot)

Samples cluster by expression condition (MDS / PCA plot)

Samples cluster by experimental condition (Dendogram)

Volcano Differential analysis results for regucalcin Expression plot shows clear differences in the expression of regucalcin across conditions C1

and C2 (four alternative isoforms)

Scatter plots highlight general similarities and specific outliers between conditions

C1 and C2

Extract workflow from current history

• Click on the small gear icon and select “Extract Workflow”

Edit workflow• Click on “Workflow” at the top of the Galaxy window

• Move the elements of the workflow

Run workflow• Load a workflow by clicking on “Workflow” ath the top of the screen

• Click on “Run”

• Select the input datas

• Public main galaxy site (user disk quotas 250GB for registered users, maximum concurrent jobs: 8)

• https://usegalaxy.org/

• Test galaxy site (beta site for galaxy main instance)

• https://test.galaxyproject.org/

• Galaxy screen cast and tutorials

• https://wiki.galaxyproject.org/Learn

• Galaxy를 이용한 NGS 분석 (Korean)

• http://hongiiv.tistory.com/701

• Galaxy를 이용한 SNP 분석 (Korean)

• http://hongiiv.tistory.com/652

• Galaxy를 이용한 부시맨 genome 분석 (Korean)

• http://hongiiv.tistory.com/655

Useful galaxy sites

Thank you

Acknowledgements:YoungGi Kim HanKyu Choi

WanPyo Hong KangJung Kim