RNA-Seq: Sequencing the Transcriptome

Kasper Daniel HansenDivision of Biostatistics, UC Berkeley

Using Bioconductor for ChIP-Seq experimentsFHCRC, Seattle 12th-14th of November 2008

RNA-Seq: Comparison with Microarrays

Potential for surveying the entire transcriptome, including novel, un-annotated regions.

Potential for determining gene structure and isoform level expression using reads mapping to splice junctions.

Potential for making better presence/absence calls on regions.

Con: the assay is dependent on sequencing effort, low expressed regions will be missed.

Protocol

The current standard protocol for RNA-Seq is

Extraction of RNA, polyA purification Fragmentation of RNA RT of RNA to cDNA Ligation of adapters Size selection ~ 200bp (perhaps ~300bp) PCR amplification (15 rounds) Injection into flowcell

This produces reads from polyadenylated RNA without strand information.

Attempts are being made to make the assay strand specific and to assay total RNA as well.

Data from D. melanogaster:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Base effect - single sample

14.1m reads

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

Base effect - multiple samples

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

wt

rrp

ski

Base effect - different study (and prep)

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

WT

Nagalakshmi

Base effect - different prep

369860 369880 369900 369920 369940

0.0

00.0

10.0

20.0

30.0

4

gene: YLR110C

position

pro

port

ion o

f to

tal re

ads in r

egio

n

original

ribominus

Base effect - different aligners50 100 150

050

100

150

MAQ

position

counts

UHR(MAQ)

UHR(ELAND)

50 100 150

0.00

0.02

0.04

MAQ

position

counts

MAQ and ELAND, Human data

Base effect - conclusions

Reproducible base effect - like probe affinities in microarrays.

Seems to be prep dependent.

Creates issues for comparing differentregions in the genome.

Less of an issue for comparing thesame region across samples?

369860 369880 369900 369920 369940

0.0

00

.01

0.0

20

.03

0.0

4

gene: YLR110C

position

pro

po

rtio

n o

f to

tal re

ad

s in

re

gio

n

?

Mapping reads to the transcriptome

Transcriptome

2^Genome

Reads

Genome

Illustration from Lior Patcher

Well established

Mapping transcripts

Genome

Transcript

Length in genome space

paired-end reads

Junction reads:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Junction reads, zoom

:chrX

Conservation

d_simulansd_sechelliad_yakubad_erectad_ananassaed_pseudoobscurad_persimilisd_willistonid_virilisd_mojavensisd_grimshawia_gambiaea_melliferat_castaneum

1067300010673500106740001067450010675000106755001067600010676500106770001067750010678000106785001067900010679500106800001068050010681000

CG8144_6lane_0MM

S2_DRSC_6_lanes

FlyBase Protein-Coding Genes

RefSeq Genes

12 Flies, Mosquito, Honeybee, Beetle Multiz Alignments & phastCons Scores

Repeating Elements by RepeatMasker

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

CG15211CG15211CG15211CG15211

Ant2Ant2

sesBsesBsesBsesB

_ 1000

_ 0

_ 1000

_ 0

ps RNAi

S2 Untreated

Splice JunctionReads

GenomicReads

Splice JunctionReads

GenomicReads

Image from Brenton Gravely

Strategies for mapping to junctions

Map to known junctions.

Map to combination of known exons.

Map completely de-novo using canonical acceptor and donor sites. The combinatorics makes this an intimidating approach.

Map de-novo, but constrain the search to canonical acceptor and donor sites between and in transcribed region: transcript assembly. This is the approach taken by TopHat.

Paired-end data will make de-novo mapping a real possibility.

Mapping - conclusions

Mapping to transcript space is not easy.

But essential for really understanding alternative splicing.

Constructing all novel splice junctions based on canonical splice sites but only accepting splicing within genes (and small regions upstream/downstream of the gene) in D. Melanogaster yields 605,000,000 splice junctions.

0 5 10 15

02

04

06

08

01

00

Verified CDSdepth of 3

number of reads in millions

pe

rce

nt

bases s

eq

ue

nce

d

0 5 10 15

02

04

06

08

01

00

Dubious CDSdepth of 3

number of reads in millions

pe

rce

nt

bases s

eq

ue

nce

d

0 5 10 15

02

040

60

80

100

Uncharacterized CDSdepth of 3

number of reads in millions

pe

rce

nt

bases s

eq

ue

nce

d

0 5 10 15

02

04

06

08

01

00

Intronic Regionsdepth of 3

number of reads in millions

pe

rce

nt

ba

se

s s

eq

ue

nce

d

0 5 10 15

02

04

06

08

01

00

Background Regionsdepth of 3

number of reads in millions

pe

rce

nt

ba

se

s s

eq

ue

nce

d

wt

rrp

ski

xrn

Nagalakshmi

Detection in Cerevisiae

0 20 40 60 80 100

020

40

60

80

100

Intronic Regions

Percent introns detected

perc

ent genes d

ete

cte

d

wild type

!rrp6!lsm1!pat1

!ski2!ski8!rrp6

!xrn1!rrp6!lsm1!pat1

Nagalakshmi

0 20 40 60 80 1000

20

40

60

80

100

Background Regions

Percent background detected

perc

ent genes d

ete

cte

d

wild type

!rrp6!lsm1!pat1

!ski2!ski8!rrp6

!xrn1!rrp6!lsm1!pat1

Nagalakshmi

A. B.

Background: outside any transcribed feature, subtracted a boundary, subtracted any region detected as transcribed in recent studies

Detection in Drosophila

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

percent of intergenic regions detected

perc

ent of constitu

tive e

xons d

ete

cte

duntreated - 32.9m reads

brr2 - 15.0m reads

pasilla - 28.3m reads

1 read in 200bp, 10m reads

10 reads in 200bp, 10m reads

Replication

Sources of variation

Lane variation

Flowcell variation

Library prep variation

Biological variation

Poisson model

good fit

less good fit

Systematic differences

?: Is absolute quantification possible

Software

We have developed two R packages to help us

GenomeGraphs (Durinck, Bullard) : plots annotation and experimental data along a genome. Makes it easy to construct high quality images as well as to do data exploration. Available from Bioconductor.

Genominator (Bullard, Hansen) : provides support for managing, accessing and analyzing data oriented along a chromosome, together with annotation. Uses a SQLite backend. Works very well for unpaired reads mapped to the genome. Available from our home page.

R> summarizeByAnnotation(expData, annoData, fx)

Genome Graphs, example+

!

rrppolyA!

xrnTT!

rrp

xrn

wt

david!

snyder

nucleosome

miura!

conservation

YER015W YER016W

186500 187000 187500 188000 188500 189000

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0.3196

0.32

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00.20.40.60.8

11.2

mcl.9, chr: 5

Acknowledgements

Statistics

Jim BullardSandrine DudoitElizabeth PurdomMargaret TaubSteffen DurinckTerry Speed

RNA assembly

Cole Trapnell

S. Cerevisiae

Gavin SherlockAlbert Lee

D. Melanogaster

Brenton GravelyMike DuffLi YangSteven BrennerAngela Brooks