2013 july 25 systems biology rna seq v2

Cancer Systems Biology: RNA-‐Seq and Differen;al Expression Analysis

Taking advantage of a Measurement Revolu;on

July 25, 2013 Anne DeslaLes Mays

Wellstein/Riegel Laboratory Mentor: Anton Wellstein, MD, PhD

7/25/13 Wellstein/Riegel Laboratory 1

Talk Outline

•  On the Shoulders of Giants •  Sequencing Timeline •  RNASeq for Everyone •  RNA-‐Sequencing Details •  Differen;al Expression Analysis •  Causality •  Cancer Therapeu;cs Example •  Ask Bigger Ques;ons – Sequencing Everything



Rosalind Franklin “pioneered use of x-‐rays to create images of unorganized maLer – such as

large biological molecules – not just single crystals”

hLp://www.pbs.org/wgbh/aso/databank/entries/bofran.html

“Franklin made equipment adjustments to produce an extremely fine beam of x-‐rays. She extracted finer DNA fibers than ever before and arranged them in parallel bundles. Studied fibers’ reac;ons to humid condi;ons. … allowed her to discover cruical keys to DNA’s structure…. Wilkins shared this with Watson & Crick at Cambridge without her knowledge…”


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Sequencing Timeline


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Human Sequencing Timeline

Key Technical Advances: Celera Human Sequence done in one loca;on on the largest super computer in private hands at that ;me








Cancer Systems Biology Taking advantage of measurement revolu3on

Declining sequencing costs, decreasing compu3ng costs How do you leverage all this data?

GEO May 25, 2012

GEO June 25, 2013

Here is an example RNA-‐Seq Workflow


Experimental Design

Sample Collec;on

Quality Control Read Trimming

Differen;al Analysis

Transcript Iden;fica;on

Pathway Analysis

Feature Discovery

Sequencing


hLp://rnaseq.uoregon.edu/index.html

7/25/13 Wellstein/Riegel Laboratory 16 hLp://rnaseq.uoregon.edu/index.html








Replicates: Type I and Type II errors


Detec;ng Signal vs. Noise



What is the goal of the sequencing experiment?





Before Library Construc;on 1.  Most vendors and cores will assess

the quality of the RNA before sequencing

2.  Important to determine before sequencing begins

Garbage – in == Garbage out

Before library construc;on, RNA quality must be assessed


RNA-‐seq


Three steps to get to a fresh sequence with the Illumina Genome Sequence Analyzer

•  Library genera;on •  Cluster genera;on •  Sequencing


Before Library Construc;on 1.  Poly-‐A Selec;on (Total RNA -‐>

mRNA) 2.  mRNA fragmenta;on 3.  First strand synthesis (here we stop

if we want to maintain strand specificity

4.  Second strand synthesis

Other techniques 1.  Ribozero 2.  Ribominus

Library Construc;on: Messenger RNA are Poly-‐A selected from Total RNA, fragmented and cDNA synthesized


cDNA (single or double stranded) 1.  cDNA is blunt end-‐repaired and

phosphorylated (B.) 2.  A-‐base added to prepare for

indexed adapter liga;on (C.)

Library Construc;on: End repair and adenyla;on results in adapter liga;on ready constructs


Index adapter liga;on and product ready for amplifica;on on cBot or the cluster sta;on 1.  Strand specific tags are added to

the A base – ligate index adapter (D)

2.  Denature and amplify for final product (E)

Library Construc;on: Adapter liga;on results in cluster-‐genera;on-‐ready constructs


Single DNA molecules hybridize to the lawn of oligos graped to the surface of the flow cell 1.  Oligo lawn 2.  Oligos hybridize to the adapters

that had been ligated to the library fragments which flow through the cell

Cluster Genera;on: In the illumina Cbot system, single molecules are isothermally amplified in a flow cell to prepare them for sequencing


Bridge amplifica;ons resul;ng in 100s of millions of unique clusters 1.  Each fragment is clonally

amplified through a series of extensions and isothermal bridge amplifica;ons

2.  Reverse strands cleaved and washed away

3.  Ends are blocked 4.  Sequencing primer hybridized to

the DNA template 5.  Libraries are ready for

sequencing

Cluster genera;on: Bound fragments are extended to make copies and reverse strands cleaved and washed away


4 fluorescently labeled reversibly terminated nucleo;des 1.  Each base competes for addi;on 2.  Natural compe;;on ensures

highest accuracy 3.  Aper each round of synthesis,

clusters are excited by a laser emiqng a color that iden;fies the newly added base

4.  Fluorescent label and blocking group are removed allowing for addi;on of next nucleo;de

5.  Proprietary (Illumina) chemistry reads a base in each cycle

6.  Allows for accurate sequencing through difficult regions such as homopolymers and repe;;ve sequence

Sequencing: 100s of millions of clusters sequenced simultaneously

There are other ways to Inquire about the Transcriptome

•  Array Based Technologies –  Affymetrix –  Agilent –  Known genes and hybridiza;on protocols

•  Microarray –  20,000+ array experiments on a single platorm –  Edge effects –  False posi;ves / false nega;ves

•  Bead-‐based arrays •  Tiling arrays •  SAGE 7/25/13 Wellstein/Riegel Laboratory 37

What is unique about RNA-‐Seq?

•  Allows you to discover and profile the en;re transcriptome of any organism

•  No probes or primers to design •  Novel transcripts •  Novel isoforms •  Alterna;ve splice sites •  Rare transcripts •  cSNPS – all of this in one experiment



Aper sequencing… 1.  Quality control – trim your reads 2.  Count Reads

•  Align to genome •  Align to transcriptome

3.  Interpret Data •  Sta;s;cal tests (differen;al

expression analysis) •  Visualiza;on (mapped

reads) •  Pathway analysis

Not so simple – big data, big compute requirements

Aper sequencing, we must then perform RNA-‐Seq Data Analysis



RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html Step 1: align-reads:

FASTQ PE* reads

Reference Genome Assembly WGS

Exis;ng Gene models

(gt files w/ tss ids)*

Gene models mapped to reference

gsnap

trimmoma;c FASTQC

trimmed PE* reads

Quality control consensus

per read length graphs

•  Tss ids = transcription start site ids, in a gtf file format •  PE – paired end •  The gene models that are built with the pasa pipeline can be input to tophat

Shadeless rectangle An unshaded rectangle represents code to be run – a process

Shaded rectangle

A shaded rectangle is a file or a graphic which may be an input and/or an output

Legend

Gsnap aligned Bam files

Dark rectangle Dark rectangle represents a file that can be displayed as a track in crop-pedia

Align-reads: Gsnap is used to align reads to the genome sequence.

samtools Gsnap.CoordSorted.bam

RNA Alterna;ve Splicing: Why you need gapped aligners


RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html Step 2: assemble-reads:

Prep_rnaseq_ alignments_for

genome_assisted_ assembly.pl



Shaded rectangle


Legend


assemble-reads: Trinity is used to assemble the RNA-Seq reads in each partition. This can be done in a massiviely parallel manner, typically requiring little RAM as compared to whole de novo RNA-Seq assemblies, and can be executed using standard hardware. The firs step (pre_rnaseq_alignments_for genome_assisted_assembly.pl – partitions the reads according to covered regions

Gsnap.CoordSorted.bam

Find Dir_* -‐name “*reads” > read_files.list

Read_files.list

GG_write_trinity_ cmds.pl

ParaFly

Trinity_GG.cmds

Find Dir_* -‐name “*inity.fasta” –exec cat {} |

Inchworm_accession_incrementer.pl > Trinity_GG.fasta

Trinity_GG.fasta

RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html Steps 3 and 4: align-transcripts and assemble-transcript alignments

Launch_PASA_pipeline.pl



Shaded rectangle


Legend


Trinity_GG.fasta

Pasa_databasename .pasa_assemblies.denovo_ transcript_isoforms.gt

Pasa_databasename .pasa_assemblies.denovo_ transcript_isoforms.bed

Pasa_databasename .pasa_assemblies.denovo_ transcript_isoforms.gff3

Pasa_databasename .pasa_assemblies.denovo_ transcript_isoforms.fasta

RNASeq flow chart – Step 5 – Tuxedo Suite – using the output of the trinity-genome-guided assembly and the pasa and keygene annotation pipelines à call tuxedo suite (in parallel with then calling the abundancy estimator RSEM



Shaded rectangle


Legend


Gff3 (gene model)

Gff3togt (convert to gt format

Gt (gene model)

tophat Calls Bow;e2

Junc;ons.bed

Accepted.hits. sam

RNASeq Quantitation and Differential Analysis



Shaded rectangle


Legend

Quantitation (matrix file with counts per isoform) Model building/Differential analysis

Trinity.fasta


Tuxedo suite

Trinity genome guided assembly Abundance es;ma;on RSEM

Transcripts .gt/.gff*

trimmed PE* reads

RSEM.isoform. results

Limma Model Design/contrast

matrix building

randomForest pcAlg

Genie3.R DREAM4

Accepted.hits. sam

cuffdiff2

•  Transcript annotation file produced by cufflinks, cuffcompare or other source

•  Counts and read group tracking files also created

Isoforms.fpkm_tracking

Genes.fpkm.tracking

Cds.fpkm.tracking

Tss_groups.fpkm.tracking

Isoform_exp.diff

Gene_exp.diff

Tss_group_exp.diff

Cds_exp.diff


How much RNA-‐sequencing data? 1.  20 million paired end reads ~ 2 GB of data 2.  100 million paired end reads ~ 10 GB of data How much computa;on power? 1.  More memory, more processors, less ;me it takes to compute 2.  Outsource the analysis, s;ll will need to store the results somewhere

Amazon web services S3 storage EC elas;c cloud on demand computa;onal facility Georgetown University High Performance Computer Core matrix.georgetown.edu UPENN Galaxy services

How much RNA-‐sequencing data, how much computa;on power and where do you go to compute?


A growing number of tools enable RNA-‐Seq analysis


What percentage of reads are covered? What percentage of reads are mapped?

3’ Bias on transcript reads 1.  60-‐80% of reads are mapped 2.  Highest percentage or 3’ end of

reads are mapped 3.  Reads need to be quality trimmed

Mapping tools bias exons to known genes


Galaxy is a web based tool commiLed to enable a researcher (more than just for RNA-‐Seq)


How to visualize mapped results?

•  UCSC Genome Browser (Gbrowse) •  Integrated Genome Browser (IGB) •  Integrated Genome Viewer (IGV)

Many shared formats, reading many of the outputs generated by the programs, ability to generate ones own tracks



Scalechr21:

DNase Clusters

Multiz Align

Human mRNAs

K562 CTCF Int 1

K562 Pol2 Int 1

HeLaS3 Pol2 Int 1

GM12878 1H1-hESC 1

K562 1HeLa-S3 1

HepG2 1GM12878H1-hESC

K562HeLa-S3

HepG2HUVEC

GM12878 PkH1-hESC Pk

K562 PkHeLa-S3 Pk

50 kb hg1923,600,000 23,650,000

C7 Random

C7 Targeted

Transcription Factor ChIP-seq from ENCODE

SwitchGear Genomics Transcription Start SitesH3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODE

RefSeq GenesHuman ESTs That Have Been Spliced

Digital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODEVertebrate Multiz Alignment & Conservation (46 Species)

UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)Simple Nucleotide Polymorphisms (dbSNP 137) Found in >= 1% of Samples

Individual matches for article Przybylski2010Sequences in Articles: PubmedCentral and Elsevier

SNPs in PublicationsHuman mRNAs from GenBank

Regulatory elements from ORegAnnoChromatin Interaction Analysis Paired-End Tags (ChIA-PET) from ENCODE/GIS-Ruan

DNA Methylation by Reduced Representation Bisulfite Seq from ENCODE/HudsonAlpha

CpG Methylation by Methyl 450K Bead Arrays from ENCODE/HAIB

Chromatin Interactions by 5C from ENCODE/Dekker Univ. Mass.

HWI-ST1129:97:D0LRDACXX:6:2208:3356:23592_2:N:0:CACTCAHWI-ST1129:97:D0LRDACXX:6:2208:3356:23592_1:N:0:CACTCA




HWI-ST1129:97:D0LRDACXX:6:2208:7212:116648_1:N:0:CACTCAHWI-ST1129:97:D0LRDACXX:6:2208:7212:116648_2:N:0:CACTCAHWI-ST1129:97:D0LRDACXX:6:2205:11321:72079_1:N:0:CACTCA

HWI-ST1129:97:D0LRDACXX:6:1203:1649:66972_1:N:0:CTCTCAHWI-ST1129:97:D0LRDACXX:6:1203:1649:66972_2:N:0:CTCTCA



HWI-ST1129:97:D0LRDACXX:6:2108:5000:141429_2:N:0:CACTCAHWI-ST1129:97:D0LRDACXX:6:2108:5000:141429_1:N:0:CACTCAHWI-ST1129:97:D0LRDACXX:6:1303:16417:184679_2:N:0:CACTCCHWI-ST1129:97:D0LRDACXX:6:1303:16417:184679_1:N:0:CACTCC



HWI-ST1129:299:C18KJACXX:6:1305:12160:63303_1:N:0:ATCACGHWI-ST1129:299:C18KJACXX:6:1102:19732:75986_1:N:0:ATCACGHWI-ST1129:299:C18KJACXX:6:1305:12160:63303_2:N:0:ATCACGHWI-ST1129:299:C18KJACXX:6:1102:19732:75986_2:N:0:ATCACGKCEBPB

LMafK_(ab50322)KTAL1_(SC-12984)

KCEBPB KKYY1KTBPKE2F4KTAF1KELF1_(SC-631)KPol2-4H8KHEY1KE2F6_(H-50)KCEBPBKTFIIIC-110

ggNFKBGgPU.1GBATFGIRF4_(M-17)GBCL11A

GgPU.1

gPU.1 KCEBPB

DA743484BF207587

Delgado-Olguin2004

Layered H3K27Ac100 _

0 _

Mammal Cons

K562 CTCF Sig 1

K562 Pol2 Sig 1

HeLaS3 Pol2 Sig 1



What do RNA-‐Seq reads look like for GAPDH?

Repeat masked allowing 1/2 mismatched bases blat’d reads viewed in IGB 6.7.2


RNA-‐Seq Differen;al Expression analysis

What does GAPDH look like in terms of quan;ta;on?

TOTAL BM HPP RPKM 3SEQ Counts BLAT Reads RPKM 3SEQ Counts BLAT Reads

CD34 0.7 340 230 8 8 14 BST1 19.7 5374 31 31 CD133 0.2 173 176 16 16 33 THY1 0 7 4 4 A12 1 0 A5 0 0 ALK 0 9 24 0 0 3 B9 0 0 C1 0 0 C2 0 0 C7 0 0 E7 0 0 E9 2 0 F6 0 0 G12 0 0 GAPDH 3013.2 727831 356289 120.8 5559 2670 H3 0 0

Blat read raw counts ra;o == 3Seq counts ra;o ~= 130 to 1 RPKM ra;o ~= 24.3


RNA-‐Seq Quan;fica;on Challenge: A problem that exists with RNA-‐Seq data that doesn’t exist with array data: Longer transcripts produce more reads than shorter transcripts

One solu;on to account for this is RPKM (FPKM used by Cufflinks) RPKM = 10^9 x C / NL, which is really just simply C/N C(gene)= the number of mappable reads that fall onto a gene's exons N= total number of mappable reads in the experiment L(gene)= the sum of the exons in base pairs. Wold (2008) RPKM – reads per kilo base per million CPM – counts per million


RNA-‐Seq Quan;fica;on Challenge: DESeq Method uses the geometric mean of counts in all samples

DESeq Method: Construct a "reference sample" by taking, for each gene, the geometric mean of the counts in all samples. To get the sequencing depth of a sample rela;ve to the reference, calculate for each gene the quo;ent of the counts in your sample divided by the counts of the reference sample. Now you have, for each gene, an es;mate of the depth ra;o. Simply take the median of all the quo;ents to get the rela;ve depth of the library. 'es;mateSizeFactors' func;on of DESeq package does this calcula;on.

DESeq: an R package that works with Raw Counts to determine genes differen;ally expressed across samples

•  Simon Anders





Given a list of differen;ally expressed Genes now enrichment analysis should be performed

•  Enrichment analysis allows the researcher to leverage documented experiments which provide evidence for genes roles in pathways and func;ons that enable the researcher to determine the results and significance of their experiments

•  DAVID –  Gene ontology –  Func;onal ontology

•  Revigo –  Output of David may be placed in REVIGO for further interpreta;on and sta;s;cal explora;on of significance of discovered sets of genes


Using differen;ally expressed genes, biological pathways should be explored

•  Differen;ally expressed genes are put into programs such as pathway studio or ingenuity

•  Shortest path programs and •  Canonical pathway analysis •  Enables a researcher to reverse engineer the pathways

expressed in the course of a healthy response to a diseased response

•  Ideally a pathway reveals the observed phenotype – connec;ng the expressed gene expression program with the phenotype – genotype – gene expression program to phenotype


RNA-‐Sequencing: What is it good for?

•  Transcript Annota;on – Muta;on iden;fica;on –  Isoform determina;on –  Alterna;ve Splice Varia;on

•  Differen;al Gene Expression –  Phenotypically segrega;ng experiments –  Allows us to get at the How in looking at the response of an organism within a par;cular cell popula;on to events

–  Good and careful design will allow us to unfold the dynamics of this response and iden;fy targets for altering disease responses to improve ones chances of surviving




hLp://bayes.cs.ucla.edu/home.htm





Acknowledgements Dr. Anton Wellstein Dr. Anna Riegel

Dr. Marcel Schmidt Dr. Elena Tassi The en;re lab: Elena, Virginie, Ghada, Ivana, Eveline, Khalid, Eric the en;re Wellstein/Riegel laboratory My CommiLee Dr. Yuri Gusev Dr. Anatoly Dritschilo Dr. Michael Johnson Dr. Christopher Loffredo Dr. Habtom Ressom Dr. Terry Ryan (external commiLee member) High Performance Core Group, Steve Moore, especially Woonki Chung Amazon Cloud Services Dr. Ann Loraine, UNC, IGB Developer Brian Haas, Author Trinity Suite

Some Resources

•  hLp://rnaseq.uoregon.edu/index.html •  hLp://dx.doi.org/10.1038/npre.2010.4282.1 (DESeq) •  hLp://galaxy.psu.edu/ •  hLp://seqanswers.com/ •  hLp://www.broadins;tute.org/igv/ •  hLp://bioviz.org/igb/index.html •  hLp://www.illumina.com •  hLp://www.otogene;cs.com •  hLp://www.dnanexus.com •  hLp://bioconductor.org/packages/2.12/bioc/html/limma.html •  hLp://trinityrnaseq.sourceforge.net/ •  hLp://trinityrnaseq.sourceforge.net/genome_guided_trinity.html •  hLp://cufflinks.cbcb.umd.edu/ •  hLp://brb.nci.nih.gov/BRB-‐ArrayTools.html •  hLp://www.modernatx.com/


Systems Biology History (wikipedia)

•  Systems biology roots found in –  Quan;ta;ve modeling of enzyme kine;cs –  Mathema;cal modeling of popula;on growth –  Simula;ons to study neurophysiology –  Control theory and cyberne;cs

•  Theorists –  Ludwig von Bertalanffy – General Systems Theory –  Alan Lloyd Hodgkin and Andrew Fielding Huxley – constructed a

mathema;cal model that explained poten;al propaga;ng along the axon of a neuron cell

–  Denis Nobel – first computer model of the heart Pacemaker


Scien;fic knowledge is limited (and advanced) by the limits (and advancements) of measurement


•  Ilya Shmulevich Genomic Signal Processing “Validity of the model involves observa;on and measurement, scien;fic knowledge is limited by the limits of measurement”

•  Erwin Shrödinger Science Theory and Man: “It really is the ul;mate purpose of all schemes and models to serve as scaffolding for any observa;ons that are at all means observable”

Technology

2013 july 25 systems biology rna seq v2