ANRV353-GG09-20 ARI 25 July 2008 14:57

Next-Generation DNASequencing MethodsElaine R. MardisDepartments of Genetics and Molecular Microbiology and Genome Sequencing Center,Washington University School of Medicine, St. Louis MO 63108; email: emardis@wustl.edu

Annu. Rev. Genomics Hum. Genet. 2008.9:387–402

First published online as a Review in Advance onJune 24, 2008

The Annual Review of Genomics and Human Geneticsis online at genom.annualreviews.org

This article’s doi:10.1146/annurev.genom.9.081307.164359

Copyright c© 2008 by Annual Reviews.All rights reserved

1527-8204/08/0922-0387$20.00

Key Words

massively parallel sequencing, sequencing-by-synthesis, resequencing

AbstractRecent scientific discoveries that resulted from the application of next-generation DNA sequencing technologies highlight the striking impactof these massively parallel platforms on genetics. These new meth-ods have expanded previously focused readouts from a variety of DNApreparation protocols to a genome-wide scale and have fine-tuned theirresolution to single base precision. The sequencing of RNA also hastransitioned and now includes full-length cDNA analyses, serial analysisof gene expression (SAGE)-based methods, and noncoding RNA dis-covery. Next-generation sequencing has also enabled novel applicationssuch as the sequencing of ancient DNA samples, and has substantiallywidened the scope of metagenomic analysis of environmentally derivedsamples. Taken together, an astounding potential exists for these tech-nologies to bring enormous change in genetic and biological researchand to enhance our fundamental biological knowledge.

387

Click here for quick links to

Annual Reviews content online,

including:

• Other articles in this volume

• Top cited articles

• Top downloaded articles

• Our comprehensive search

FurtherANNUALREVIEWS

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

INTRODUCTION

The sequencing of the reference humangenome was the capstone for many years of hardwork spent developing high-throughput, high-capacity production DNA sequencing and as-sociated sequence finishing pipelines. The ap-proach used >20,000 large bacterial artificialchromosome (BAC) clones that each containedan approximately 100-kb fragment of the hu-man genome, which together provided an over-lapping set or tiling path through each humanchromosome as determined by physical map-ping (31). In BAC-based sequencing, each BACclone is amplified in bacterial culture, isolatedin large quantities, and sheared to produce size-selected pieces of approximately 2−3 kb. Thesepieces are subcloned into plasmid vectors, am-plified in bacterial culture, and the DNA isselectively isolated prior to sequencing. Bygenerating approximately eightfold oversam-pling (coverage) of each BAC clone in plasmidsubclone equivalents, computer-aided assemblycan largely recreate the BAC insert sequencein contigs (contiguous stretches of assembledsequence reads). Subsequent refinement, in-cluding gap closure and sequence qualityimprovement (finishing), produces a single con-tiguous stretch of high-quality sequence (typi-cally with less than 1 error per 40,000 bases).Since the completion of the human genomeproject (HGP) (26, 51), substantive changeshave occurred in the approach to genome se-quencing that have moved away from BAC-based approaches and toward whole-genomesequencing (WGS), with changes in the ac-companying assembly algorithms. In the WGSapproach, the genomic DNA is sheared di-rectly into several distinct size classes and placedinto plasmid and fosmid subclones. Oversam-pling the ends of these subclones to gener-ate paired-end sequencing reads provides thenecessary linking information to fuel whole-genome assembly algorithms. The net result isthat genomes can be sequenced more rapidlyand more readily, but highly polymorphic orhighly repetitive genomes remain quite frag-mented after assembly.

Despite these dramatic changes in sequenc-ing and assembly approaches, the primary dataproduction for most genome sequencing sincethe HGP has relied on the same type of capillarysequencing instruments as for the HGP. How-ever, that scenario is rapidly changing owingto the invention and commercial introductionof several revolutionary approaches to DNAsequencing, the so-called next-generation se-quencing technologies. Although these instru-ments only began to become commerciallyavailable in 2004, they already are having a ma-jor impact on our ability to explore and an-swer genome-wide biological questions; morethan 100 next-generation sequencing–relatedmanuscripts have appeared to date in the peer-reviewed literature. These technologies are notonly changing our genome sequencing ap-proaches and the associated timelines and costs,but also accelerating and altering a wide va-riety of types of biological inquiry that havehistorically used a sequencing-based readout,or effecting a transition to this type of read-out, as detailed in this review. Furthermore,next-generation platforms are helping to openentirely new areas of biological inquiry, includ-ing the investigation of ancient genomes, thecharacterization of ecological diversity, and theidentification of unknown etiologic agents.

NEXT-GENERATION DNASEQUENCING

Three platforms for massively parallel DNAsequencing read production are in reasonablywidespread use at present: the Roche/454FLX (30) (http://www.454.com/enabling-technology/the-system.asp), the Illumina/Solexa Genome Analyzer (7) (http://www.illumina.com/pages.ilmn?ID=203), and theApplied Biosystems SOLiDTM System (http://marketing.appliedbiosystems.com/images/Product/Solid Knowledge/flash/102207/solid.html). Recently, another two massivelyparallel systems were announced: theHelicos HeliscopeTM (www.helicosbio.com)and Pacific Biosciences SMRT (www.pacificbiosciences.com) instruments. The

388 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Helicos system only recently became com-mercially available, and the Pacific Biosciencesinstrument will likely launch commerciallyin early 2010. Each platform embodies acomplex interplay of enzymology, chemistry,high-resolution optics, hardware, and softwareengineering. These instruments allow highlystreamlined sample preparation steps prior toDNA sequencing, which provides a significanttime savings and a minimal requirementfor associated equipment in comparison tothe highly automated, multistep pipelinesnecessary for clone-based high-throughputsequencing. By different approaches outlinedbelow, each technology seeks to amplify singlestrands of a fragment library and performsequencing reactions on the amplified strands.The fragment libraries are obtained by anneal-ing platform-specific linkers to blunt-endedfragments generated directly from a genome orDNA source of interest. Because the presenceof adapter sequences means that the moleculesthen can be selectively amplified by PCR, nobacterial cloning step is required to amplify thegenomic fragment in a bacterial intermediate asis done in traditional sequencing approaches.Importantly, both the Helicos and PacificBiosystems instruments mentioned above areso-called “single molecule” sequencers anddo not require any amplification of DNAfragments prior to sequencing.

Another contrast between these instrumentsand capillary platforms is the run time requiredto generate data. Next-generation sequencersrequire longer run times of between 8 h and10 days, depending upon the platform and readtype (single end or paired ends). The longerrun times result mainly from the need to im-age sequencing reactions that are occurring ina massively parallel fashion, rather than a peri-odic charge-coupled device (CCD) snapshot of96 fixed capillaries. The yield of sequence readsand total bases per instrument run is signifi-cantly higher than the 96 reads of up to 750 bpeach produced by a capillary sequencer run, andcan vary from several hundred thousand reads(Roche/454) to tens of millions of reads (Il-lumina and Applied Biosystems SOLiD). The

Charge-coupleddevice (CCD): acapacitor array used inoptical scanners tocapture images

Emulsion PCR(ePCR): method forDNA amplificationthat uses a water in oilemulsion to isolatesingle DNA moleculesin aqueousmicroreactors

combination of streamlined sample preparationand long run times means that a single oper-ator can readily keep several next-generationsequencing instruments at full capacity. Thefollowing sections aim to introduce the readerto the primary features of each of the three mostwidely used next-generation platforms and todiscuss strengths and weaknesses.

Roche/454 FLX Pyrosequencer

This next-generation sequencer was the firstto achieve commercial introduction (in 2004)and uses an alternative sequencing technologyknown as pyrosequencing. In pyrosequencing,each incorporation of a nucleotide by DNApolymerase results in the release of pyrophos-phate, which initiates a series of downstreamreactions that ultimately produce light by thefirefly enzyme luciferase. The amount of lightproduced is proportional to the number of nu-cleotides incorporated (up to the point of de-tector saturation). In the Roche/454 approach(Figure 1), the library fragments are mixedwith a population of agarose beads whose sur-faces carry oligonucleotides complementary tothe 454-specific adapter sequences on the frag-ment library, so each bead is associated with asingle fragment. Each of these fragment:beadcomplexes is isolated into individual oil:watermicelles that also contain PCR reactants, andthermal cycling (emulsion PCR) of the micellesproduces approximately one million copies ofeach DNA fragment on the surface of eachbead. These amplified single molecules are thensequenced en masse. First the beads are ar-rayed into a picotiter plate (PTP; a fused silicacapillary structure) that holds a single bead ineach of several hundred thousand single wells,which provides a fixed location at which each se-quencing reaction can be monitored. Enzyme-containing beads that catalyze the downstreampyrosequencing reaction steps are then addedto the PTP and the mixture is centrifuged tosurround the agarose beads. On instrument,the PTP acts as a flow cell into which eachpure nucleotide solution is introduced in a step-wise fashion, with an imaging step after each

www.annualreviews.org • Next-Generation DNA Sequencing Methods 389

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Anneal sstDNA to an excess ofDNA capture beads

Emulsify beads and PCRreagents in water-in-oilmicroreactors

Clonal amplification occursinside microreactors

Break microreactors andenrich for DNA-positivebeads

Amplified sstDNA library beads Quality filtered bases

a

b

c

DNA library preparation

Emulsion PCR

Sequencing

A

A

A B

B

B

4.5 hours

8 hours

7.5 hours

Ligation

Selection

(isolate AB

fragments

only)

•Genome fragmented by nebulization

•No cloning; no colony picking

•sstDNA library created with adaptors

•A/B fragments selected using avidin-biotin purification

gDNA sstDNA library

sstDNA library Bead-amplified sstDNA library

•Well diameter: average of 44 μm

•400,000 reads obtained in parallel

•A single cloned amplified sstDNA bead is deposited per well

390 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

nucleotide incorporation step. The PTP isseated opposite a CCD camera that records thelight emitted at each bead. The first four nu-cleotides (TCGA) on the adapter fragment ad-jacent to the sequencing primer added in libraryconstruction correspond to the sequential flowof nucleotides into the flow cell. This strategyallows the 454 base-calling software to calibratethe light emitted by a single nucleotide incor-poration. However, the calibrated base callingcannot properly interpret long stretches (>6)of the same nucleotide (homopolymer run), sothese areas are prone to base insertion and dele-tion errors during base calling. By contrast,because each incorporation step is nucleotidespecific, substitution errors are rarely encoun-tered in Roche/454 sequence reads.

The FLX instrument currently provides 100flows of each nucleotide during an 8-h run,which produces an average read length of 250nucleotides (an average of 2.5 bases per flow areincorporated). These raw reads are processedby the 454 analysis software and then screenedby various quality filters to remove poor-qualitysequences, mixed sequences (more than one ini-tial DNA fragment per bead), and sequenceswithout the initiating TCGA sequence. Theresulting reads yield 100 Mb of quality data onaverage. Downstream of read processing, an as-sembly algorithm (Newbler) can assemble FLXreads. Although shorter than reads derived fromcapillary sequencers, FLX reads are of sufficientlength to assemble small genomes such as bacte-rial and viral genomes to high quality and con-tiguity. As mentioned, the lack of a bacterialcloning step in the Roche/454 process meansthat sequences not typically sampled in a WGSapproach owing to cloning bias will be morelikely represented in a FLX data set, which con-

Bridge amplification:allows the generationof in situ copies of aspecific DNAmolecule on anoligo-decorated solidsupport

tributes to more comprehensive genome cover-age.

Illumina Genome Analyzer

The single molecule amplification step forthe Illumina Genome Analyzer starts with anIllumina-specific adapter library, takes place onthe oligo-derivatized surface of a flow cell, andis performed by an automated device called aCluster Station. The flow cell is an 8-channelsealed glass microfabricated device that allowsbridge amplification of fragments on its surface,and uses DNA polymerase to produce multipleDNA copies, or clusters, that each represent thesingle molecule that initiated the cluster ampli-fication. A separate library can be added to eachof the eight channels, or the same library canbe used in all eight, or combinations thereof.Each cluster contains approximately one mil-lion copies of the original fragment, which issufficient for reporting incorporated bases atthe required signal intensity for detection dur-ing sequencing.

The Illumina system utilizes a sequencing-by-synthesis approach in which all four nu-cleotides are added simultaneously to the flowcell channels, along with DNA polymerase,for incorporation into the oligo-primed clus-ter fragments (see Figure 2 for details). Specif-ically, the nucleotides carry a base-unique flu-orescent label and the 3′-OH group is chem-ically blocked such that each incorporation isa unique event. An imaging step follows eachbase incorporation step, during which each flowcell lane is imaged in three 100-tile segmentsby the instrument optics at a cluster densityper tile of 30,000. After each imaging step,the 3′ blocking group is chemically removed

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Figure 1The method used by the Roche/454 sequencer to amplify single-stranded DNA copies from a fragment library on agarose beads. Amixture of DNA fragments with agarose beads containing complementary oligonucleotides to the adapters at the fragment ends aremixed in an approximately 1:1 ratio. The mixture is encapsulated by vigorous vortexing into aqueous micelles that contain PCRreactants surrounded by oil, and pipetted into a 96-well microtiter plate for PCR amplification. The resulting beads are decorated withapproximately 1 million copies of the original single-stranded fragment, which provides sufficient signal strength during thepyrosequencing reaction that follows to detect and record nucleotide incorporation events. sstDNA, single-stranded template DNA.

www.annualreviews.org • Next-Generation DNA Sequencing Methods 391

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

to prepare each strand for the next incorpora-tion by DNA polymerase. This series of stepscontinues for a specific number of cycles, as de-termined by user-defined instrument settings,which permits discrete read lengths of 25–35

bases. A base-calling algorithm assigns se-quences and associated quality values to eachread and a quality checking pipeline evaluatesthe Illumina data from each run, removingpoor-quality sequences.

Adapter

DNA fragment

Dense lawnof primers

Adapter

Attached

DNA

Adapters

Prepare genomic DNA sample

Randomly fragment genomic DNAand ligate adapters to both ends ofthe fragments.

Attach DNA to surface

Bind single-stranded fragmentsrandomly to the inside surfaceof the flow cell channels.

Bridge amplification

Add unlabeled nucleotides

and enzyme to initiate solid-

phase bridge amplification.

Denature the doublestranded molecules

Nucleotides

a

Figure 2The Illumina sequencing-by-synthesis approach. Cluster strands created by bridge amplification are primed and all four fluorescentlylabeled, 3′-OH blocked nucleotides are added to the flow cell with DNA polymerase. The cluster strands are extended by onenucleotide. Following the incorporation step, the unused nucleotides and DNA polymerase molecules are washed away, a scan buffer isadded to the flow cell, and the optics system scans each lane of the flow cell by imaging units called tiles. Once imaging is completed,chemicals that effect cleavage of the fluorescent labels and the 3′-OH blocking groups are added to the flow cell, which prepares thecluster strands for another round of fluorescent nucleotide incorporation.

392 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Applied Biosystems SOLiDTM

SequencerThe SOLiD platform uses an adapter-ligatedfragment library similar to those of the othernext-generation platforms, and uses an emul-sion PCR approach with small magnetic beadsto amplify the fragments for sequencing. Un-like the other platforms, SOLiD uses DNA lig-ase and a unique approach to sequence the am-plified fragments, as illustrated in Figure 3a.Two flow cells are processed per instrumentrun, each of which can be divided to containdifferent libraries in up to four quadrants. Readlengths for SOLiD are user defined between25–35 bp, and each sequencing run yields be-tween 2–4 Gb of DNA sequence data. Once

the reads are base called, have quality values,and low-quality sequences have been removed,the reads are aligned to a reference genome toenable a second tier of quality evaluation calledtwo-base encoding. The principle of two-baseencoding is shown in Figure 3b, which illus-trates how this approach works to differenti-ate true single base variants from base-callingerrors.

Two key differences that speak to the utilityof next-generation sequence reads are (a) thelength of a sequence read from all current next-generation platforms is much shorter than thatfrom a capillary sequencer and (b) each next-generation read type has a unique error modeldifferent from that already established for

b

Laser

First chemistry cycle:

determine first base

To initiate the first

sequencing cycle, add

all four labeled reversible

terminators, primers, and

DNA polymerase enzyme

to the flow cell.

Image of first chemistry cycle

After laser excitation, capture the image

of emitted fluorescence from each

cluster on the flow cell. Record the

identity of the first base for each cluster.

Sequence read over multiple chemistry cycles

Repeat cycles of sequencing to determine the sequenceof bases in a given fragment a single base at a time.

Before initiating the

next chemistry cycle

The blocked 3' terminus

and the fluorophore

from each incorporated

base are removed.

GCTGA...

Figure 2(Continued )

www.annualreviews.org • Next-Generation DNA Sequencing Methods 393

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

A C G T

1st b

ase

2nd base

A

C

G

T

3'TAnnnzzz5'

3'TCnnnzzz5'

3'TGnnnzzz5'

3'TTnnnzzz5'

Cleavage site

Di base probesSOLiD™ substrate

3'TA

AT

Universal seq primer (n)

3'P1 adapter Template sequence

P OH

Universal seq primer (n–1)

Ligase

Phosphatase

+

1. Prime and ligate

2. Image

4. Cleave off fluor

5. Repeat steps 1–4 to extend sequence

3'

Universal seq primer (n–1)1. Melt off extended

sequence2. Primer reset3'

AA AC G

G GG

C C

C

T AA

A GG

CC

T T TT

6. Primer reset

7. Repeat steps 1–5 with new primer

8. Repeat Reset with , n–2, n–3, n–4 primers

TA

AT

AT3'

TA

AT3'

Excite Fluorescence

Cleavage agent

P

HO

TAAA AG AC AAATTT TC TG TT AC

TGCGGC 3'

3. Cap unextended strands

3'

PO4

1 2 3 4 5 6 7 ... (n cycles)Ligation cycle

3'

3'1 μmbead

1 μmbead

1 μmbead

–1

Universal seq primer (n–1)

Universal seq primer (n)

Universal seq primer (n–2)

Universal seq primer (n–3)

Universal seq primer (n–4)

3'

3'

3'

3'

3'

1

2

3

4

5

Primer round 1

Template

Primer round 2 1 base shift

Glass slide

3'5' Template sequence

1 μmbead P1 adapter

Bridge probe

Bridge probe

Bridge probe

Read position

Indicates positions of interrogation

35 34 3332313029282726252423222120191817161514131211109876543210

Ligation cycle

Pri

me

r ro

un

d

1 2 3 4 5 6 7

a

394 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Color space sequence

Possible dinucleotides

Decoded sequence

Base space sequence

TA

GC

CG

AC

CA

GT

TG

AA

CC

GG

TT

AT TG GG

A T G G

AT

GA

TC

AG

CT

GA

A

Possible dinucleotides encoded by each color

Decoding

A

A

C

C

G

G

T

T

1st

ba

se

2nd base

ATCGGCTA

ACCAGTTG

AACCGGT T

GATCAGCT

Data collection and image analysis

Collect color image

Collect color image

Identify

beads

Identify

beads

Identify

bead color

Identify

bead color

Primer round 1,

ligation cycle 1

Primer round 2,

ligation cycle 1

Primer round 3,

ligation cycle 1

Color space for this sequence

Glass slide

R

R

G

BG

RG

B BY

RR G

BY

Primer round 4,

ligation cycle 1GR B

BY

A AT G G

With 2 base encoding each

base is defined twice

Double interrogation

Template sequence

Base zero

b

Figure 3(a) The ligase-mediated sequencing approach of the Applied Biosystems SOLiD sequencer. In a manner similar to Roche/454 emulsionPCR amplification, DNA fragments for SOLiD sequencing are amplified on the surfaces of 1-μm magnetic beads to provide sufficientsignal during the sequencing reactions, and are then deposited onto a flow cell slide. Ligase-mediated sequencing begins by annealing aprimer to the shared adapter sequences on each amplified fragment, and then DNA ligase is provided along with specific fluorescent-labeled 8mers, whose 4th and 5th bases are encoded by the attached fluorescent group. Each ligation step is followed by fluorescencedetection, after which a regeneration step removes bases from the ligated 8mer (including the fluorescent group) and concomitantlyprepares the extended primer for another round of ligation. (b) Principles of two-base encoding. Because each fluorescent group on aligated 8mer identifies a two-base combination, the resulting sequence reads can be screened for base-calling errors versus truepolymorphisms versus single base deletions by aligning the individual reads to a known high-quality reference sequence.

www.annualreviews.org • Next-Generation DNA Sequencing Methods 395

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Chromatinimmunoprecipitation(ChIP): chemicalcrosslinking of DNAand proteins, andimmunoprecipitationusing a specificantibody to determineDNA:proteinassociations in vivo

Quantitativepolymerase chainreaction (qPCR):rapidly measures thequantity of DNA,cDNA, or RNApresent in a samplethrough cycle-by-cyclemeasurement ofincorporatedfluorescent dyes

capillary sequence reads. Both differences affecthow the reads are utilized in bioinformatic anal-yses, depending upon the application. For ex-ample, in strain-to-reference comparisons (re-sequencing), the typical definition of repeatcontent must be revised in the context of theshorter read length. In addition, a much higherread coverage or sampling depth is required forcomprehensive resequencing with short readsto adequately cover the reference sequence atthe depth and low gap size needed.

Some applications are more suitable for cer-tain platforms than others, as detailed below.Furthermore, read length and error profileissues entail platform- and application-specificbioinformatics-based considerations. More-over, it is important to recognize the signifi-cant impacts that implementation of these plat-forms in a production sequencing environmenthas on informatics and bioinformatics infras-tructures. The massively parallel scale of se-quencing implies a similarly massive scale ofcomputational analyses that include image anal-ysis, signal processing, background subtraction,base calling, and quality assessment to producethe final sequence reads for each run. In everycase, these analyses place significant demandson the information technology (IT), computa-tional, data storage, and laboratory informationmanagement system (LIMS) infrastructures ex-tant in a sequencing center, thereby addingto the overhead required for high-throughputdata production. This aspect of next-generationsequencing is at present complicated by thedearth of current sequence analysis tools suitedto shorter sequence read data; existing dataanalysis pipelines and algorithms must be mod-ified to accommodate these shorter reads. Inmany cases, and certainly for new applica-tions of next-generation sequencing, entirelynew algorithms and data visualization inter-faces are being devised and tested to meet thisnew demand. Therefore, the next-generationplatforms are effecting a complete paradigmshift, not only in the organization of large-scaledata production, but also in the downstreambioinformatics, IT, and LIMS support required

for high data utility and correct interpretation.This paradigm shift promises to radically alterthe path of biological inquiry, as the followingreview of recent endeavors to implement next-generation sequencing platforms and accompa-nying bioinformatics-based analyses serves tosubstantiate.

ELUCIDATING DNA-PROTEININTERACTIONS THROUGHCHROMATINIMMUNOPRECIPITATIONSEQUENCING

The association between DNA and proteins is afundamental biological interaction that plays akey part in regulating gene expression and con-trolling the availability of DNA for transcrip-tion, replication, and other processes. Theseinteractions can be studied in a focused mannerusing a technique called chromatin immuno-precipitation (ChIP) (43). ChIP entails a seriesof steps: (a) DNA and associated proteins arechemically cross-linked; (b) nuclei are isolated,lysed, and the DNA is fragmented; (c) an an-tibody specific for the DNA binding protein(transcription factor, histone, etc.) of interest isused to selectively immunoprecipitate the as-sociated protein:DNA complexes; and (d ) thechemical crosslinks between DNA and proteinare reversed and the DNA is claimed for down-stream analysis. In early applications, typicalanalyses examined the specific gene of interestby qPCR (quantitative PCR) or Southern blot-ting to determine if corresponding sequenceswere contained in the captured fragment pop-ulation. Recently, genome-wide ChIP-basedstudies of DNA-protein interactions becamepossible in sequenced genomes by using ge-nomic DNA microarrays to assay the releasedfragments. This so-called ChIP-chip approachwas first reported by Ren and coworkers (38).Although utilized for a number of importantstudies, it has several drawbacks, including a lowsignal-to-noise ratio and a need for replicates tobuild statistical power to support putative bind-ing sites.

396 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Many of these drawbacks were addressed byshifting the readout of ChIP-derived DNA se-quences onto next-generation sequencing plat-forms. The precedent-setting paper for thisparadigm was published by Johnson and col-leagues (21), who used the model organismCaenorhabditis elegans and the Roche platformto elucidate nucleosome positioning on ge-nomic DNA. This study established that se-quencing the micrococcal nuclease–derived di-gestion products of genomic DNA carefullyisolated from mixed stage hermaphrodite pop-ulations of C. elegans was sufficient to gener-ate a genome-wide, highly precise positionalprofile of chromatin. This capability enablesstudies of specific physiological conditions andtheir genome-wide impact on nucleosome posi-tioning, among other applications. Subsequentstudies have utilized a ChIP-based approachand the Illumina platform to provide insightsinto transcription factor binding sites in the hu-man genome such as neuron-restrictive silencerfactor (NRSF) (20) and signal transducer andactivator of transcription 1 (STAT1) (39). In alandmark study, Mikkelsen and coworkers (32)explored the connection between chromatinpackaging of DNA and differential gene ex-pression using mouse embryonic stem cells andlineage-committed mouse cells (neural progen-itor cells and embryonic fibroblasts), provid-ing a next-generation sequencing-based frame-work for using genome-wide chromatin profil-ing to characterize cell populations. This groupdemonstrated that trimethylation of lysine 4and lysine 27 determines genes that are ei-ther expressed, poised for expression, or sta-bly repressed, effectively reflecting cell stateand lineage potential. Also, lysine 4 and lysine9 trimethylation mark imprinting control re-gions, whereas lysine 9 and lysine 20 trimethy-lation identifies satellite, telomeric, and ac-tive long-terminal repeats. These early studiesdemonstrated that the ability to map genome-wide changes in transcription factor binding orchromatin packaging under different environ-mental conditions offers a profound opportu-nity to couple evidence of altered DNA:proteininteractions to expression level changes of

Serial analysis ofgene expression(SAGE): measuresthe quantitativeexpression of genes inan mRNA populationsample by generatingSAGE tags that arethen sequenced

specific genes in the context of specific envi-ronmental stimuli, thereby enhancing our un-derstanding of gene expression–based cellularresponses.

GENE EXPRESSION:SEQUENCING THETRANSCRIPTOME

Historically, mRNA expression has beengauged by microarray or qPCR-based ap-proaches; the latter is most efficient and cost-effective for a genome-wide survey of gene ex-pression levels. Even the exquisite sensitivityof qPCR, however, is not absolute, nor is itstraightforward or reliable to evaluate novel al-ternative splicing isoforms using either technol-ogy. In the past, serial analysis of gene expres-sion (SAGE) (50) and variants have provided adigital readout of gene expression levels usingDNA sequencing. These approaches are pow-erful in their ability to report the expression ofgenes at levels below the sensitivity of microar-rays, but have been limited in their applicationby the cost of DNA sequencing.

By contrast, the rapid and inexpensive se-quencing capacity offered by next-generationsequencing instruments meshes perfectly withSAGE tagging or conventional cDNA sequenc-ing approaches, as evidenced by several studiesthat used Roche/454 technology (6, 11, 46, 53).Undoubtedly, the shorter read lengths offeredby the Illumina and Applied Biosystems instru-ments will be utilized with these approaches inthe future, offering the advantage of sequenc-ing individual SAGE tags rather than requir-ing concatenation of the tags prior to sequenc-ing. Indeed, one might imagine combining thedata obtained from isolating and sequencingChIP-derived DNA bound by a transcriptionfactor of interest to the corresponding coiso-lated and sequenced mRNA population fromthe same cells. Such experiments will be en-tirely feasible with next-generation technolo-gies, especially given the low input amount ofeach type of biomolecule required for a suitablelibrary and the high sensitivity afforded by thesequencing method.

www.annualreviews.org • Next-Generation DNA Sequencing Methods 397

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

Noncoding RNA(ncRNA): any nativeRNA that istranscribed but nottranslated into aprotein

microRNA (miRNA):21–23-base RNAmolecules thatparticipate in RNA-induced silencing ofgene expression

DISCOVERING NONCODINGRNAs

One of the most exciting areas of biologicalresearch in recent years has been the discov-ery and functional analysis of noncoding RNA(ncRNA) systems in different organisms. Firstdescribed in plants, ncRNAs are providing newinsights into gene regulation in animal systemsas well, as recognized by the awarding of theNobel Prize in Medicine and Physiology to An-drew Fire and Craig Mello in 2006. Perhaps themost profound impact of next-generation se-quencing technology has been on the discoveryof novel ncRNAs belonging to distinct classesin an extraordinarily diverse set of species (3, 8,9, 18, 22, 29, 41, 55). In fact, this approach hasbeen responsible for the discovery of ncRNAclasses in organisms not previously known topossess them (41). These discoveries are beingcoupled with an ever-expanding comprehen-sion of the functions embodied by these uniqueRNA species, including gene regulation by avariety of mechanisms. In this regard, studyingthe roles of specific microRNAs (miRNAs) incancer is helping to uncover certain aspects ofthe disease (10, 44).

Noncoding RNA discovery is best accom-plished by sequencing because the evolution-ary diversity of ncRNA gene sequences makesit difficult to predict their presence in a genomewith high certainty by computational methodsalone. The unique structures of the processedncRNAs pose difficulties for converting theminto next-generation sequencing libraries (29),but remarkable progress has already been madein characterizing these molecules. With thesebarriers dissolving, the high capacity and lowcost of next-generation platforms ensure thatdiscovery of ncRNAs will continue at a rapidpace and that sequence variants with impor-tant functional impacts will also be determined.Because the readout from next-generation se-quencers is quantitative, ncRNA characteri-zation will include detecting expression levelchanges that correlate with changes in envi-ronmental factors, with disease onset and pro-gression, and perhaps with complex disease on-

set or severity, for example. Importantly, thediscovery and characterization of ncRNAs willenhance the annotation of sequenced genomessuch that, especially in model organisms andhumans, the impact of mutations will becomemore broadly interpretable across the genome.

ANCIENT GENOMESRESURRECTED

Attempts to characterize fossil-derived DNAshave been limited by the degraded state of thesample, which in the past permitted only mi-tochondrial DNA sequencing and typically in-volved PCR amplification of specific mitochon-drial genome regions (1, 15, 23, 24, 35, 40).The advent of next-generation sequencing hasfor the first time made it possible to directlysample the nuclear genomes of ancient remainsfrom the cave bear (34), mammoth (37), andthe Neanderthal (17, 33). Several non-trivialtechnical complications arise in these inquiries,most notably the need to identify contaminat-ing DNA from modern humans in the caseof Neanderthal remains. Although even next-generation sequencing of these sample remainsis quite inefficient, owing largely to bacterialDNA that is coisolated with the genomic prepa-ration and the degraded nature of the ancientgenome, important characterizations are beingmade. So far, one million bases of the Nean-derthal genome have been sequenced, startingfrom DNA obtained from a single fossil bone(17).

METAGENOMICS EMERGES

Characterizing the biodiversity found on Earthis of particular interest as climate changesreshape our planet. DNA- or RNA-based ap-proaches for this purpose are becoming in-creasingly powerful as the growing numberof sequenced genomes enables us to interpretpartial sequences obtained by direct samplingof specific environmental niches. Such investi-gations are referred to as metagenomics, andare typically aimed at answering the question:who’s there? Conventionally, this question is

398 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

addressed by isolating DNA from an environ-mental sample, amplifying the collective of 16Sribosomal RNA (rRNA) genes with degeneratePCR primer sets, subcloning the PCR prod-ucts that result, and classifying the taxa presentaccording to a database of assigned 16S rRNAsequences. As an alternative, DNA (or RNA) isisolated, subcloned, and then sequenced to pro-duce a fragment pool representative of the ex-isting population. These sequences can then betranslated in silico into protein fragments andcompared with the existing database of anno-tated genome sequences to identify communitymembers. In both approaches, deep sequencingof the population of subclones is necessary toobtain the full spectrum of taxa present, and islimited by potential cloning bias that can resultfrom the use of bacterial cloning. By samplingRNA sequences from a metagenomic isolate,one can attempt to reconstruct metabolic path-ways that are active in a given environment (45,47). Several early metagenomic studies utilizedDNA sequence sampling by capillary sequenc-ing to investigate an acidophilic biofilm (49), anacid mine site (12) and the Sargasso Sea (52).Although these studies defined metagenomicsas a scientific pursuit, they were limited in thebreadth of diversity that could be sampled ow-ing to the expense of the conventional sequenc-ing process. By contrast, the rapid, inexpensive,and massive data production enabled by next-generation platforms has caused a recent ex-plosion in metagenomic studies. These studiesinclude previously sampled environments suchas the ocean (2, 19, 42) and an acid mine site(13), but soil (14, 27) and coral reefs (54) alsowere studied by Roche/454 pyrosequencing.

Another metagenomic environment thatis being characterized by next-generation se-quencing is the human microbiome; the humanbody contains several highly specific environ-ments that are inhabited by various microbial,fungal, viral, and eukaryotic symbiont com-munities, the inhabitants of which may varyaccording to the health status of the individual.These environments include the skin, the oraland nasal cavities, the gastrointestinal tract, andthe vagina, among others. Particularly well-

Metagenomics: thegenomics-based studyof genetic materialrecovered directlyfrom environmentallyderived sampleswithout laboratoryculture

Epigenomics: seeksto define the influenceof changes to geneexpression that areindependent of genesequence

studied is the lower intestine of humans, firstcharacterized by 16S rRNA classification (4, 5,28) and more recently by 454 pyrosequencingin adult humans (16, 36, 48) and in infants(25, 36). Supporting these characterizationefforts will be a large-scale project to sequencehundreds of isolated microbial genomes thatare known symbionts of humans as references(http://www.genome.gov/25521743). Theearly successes in human microbiome charac-terization and the apparent interplay betweenthe human host and its microbial census haveresulted in the inclusion of a Human Micro-biome Initative in the NIH Roadmap (http://nihroadmap.nih.gov/hmp/). The associatedfunding opportunities, with the advantagesoffered by next-generation sequencing instru-mentation, should initiate a revolution in ourunderstanding of how the human microbiomeinfluences our health status.

FUTURE POSSIBILITIES

As this review has described, the advent andwidespread availability of next-generation se-quencing instruments has ushered in an erain which DNA sequencing will become amore universal readout for an increasingly widevariety of front-end assays. However, moreapplications of next-generation sequencing,beyond those covered here, are yet to come.For example, genome resequencing will likelybe used to characterize strains or isolates rela-tive to high-quality reference genomes such asC. elegans, Drosophila, and human. Studies of thistype will identify and catalog genomic variationon a wide scale, from single nucleotide poly-morphisms (SNPs) to copy number variationsin large sequence blocks (>1000 bases). Ulti-mately, resequencing studies will help to bettercharacterize, for example, the range of normalvariation in complex genomes such as the hu-man genome, and aid in our ability to compre-hensively view the range of genome variation inclinical isolates of pathogenic microbes, viruses,etc.

Epigenomic variation, as an extension ofgenome resequencing applications, also will be

www.annualreviews.org • Next-Generation DNA Sequencing Methods 399

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

investigated using next-generation sequencingapproaches that enable the ascertainment ofgenome-wide patterns of methylation and howthese patterns change through the course of anorganism’s development, in the context of dis-ease, and under various other influences.

Perhaps the most exciting possibility en-gendered by the ability to use DNA sequenc-

ing to rapidly read out experimental results isthe enhanced potential to combine the resultsof different experiments—correlative analysesof genome-wide methylation, histone bindingpatterns, and gene expression, for example—owing to the similar data type produced. Thepower in these correlative analyses is the powerto begin unlocking the secrets of the cell.

DISCLOSURE STATEMENT

The author serves as a Director of the Applera Corporation.

LITERATURE CITED

1. Adcock GJ, Dennis ES, Easteal S, Huttley GA, Jermiin LS, et al. 2001. Mitochondrial DNA sequencesin ancient Australians: implications for modern human origins. Proc. Natl. Acad. Sci. USA 98:537–42

2. Angly FE, Felts B, Breitbart M, Salamon P, Edwards RA, et al. 2006. The marine viromes of four oceanicregions. PLoS Biol. 4:e368

3. Axtell MJ, Snyder JA, Bartel DP. 2007. Common functions for diverse small RNAs of land plants. PlantCell 19:1750–69

4. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, et al. 2004. The gut microbiota as an environmentalfactor that regulates fat storage. Proc. Natl. Acad. Sci. USA 101:15718–23

5. Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. 2005. Host-bacterial mutualism in thehuman intestine. Science 307:1915–20

6. Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T, et al. 2006. Analysis of the prostate cancercell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genomics 7:246

7. Bentley DR. 2006. Whole-genome resequencing. Curr. Opin. Genet. Dev. 16:545–528. Berezikov E, Thuemmler F, van Laake LW, Kondova I, Bontrop R, et al. 2006. Diversity of microRNAs

in human and chimpanzee brain. Nat. Genet. 38:1375–779. Brennecke J, Aravin AA, Stark A, Dus M, Kellis M, et al. 2007. Discrete small RNA-generating loci as

master regulators of transposon activity in Drosophila. Cell 128:1089–10310. Calin GA, Liu CG, Ferracin M, Hyslop T, Spizzo R, et al. 2007. Ultraconserved regions encoding ncRNAs

are altered in human leukemias and carcinomas. Cancer Cell 12:215–2911. Cheung F, Haas BJ, Goldberg SM, May GD, Xiao Y, Town CD. 2006. Sequencing Medicago truncatula

expressed sequenced tags using 454 Life Sciences technology. BMC Genomics 7:27212. Edwards KJ, Bond PL, Gihring TM, Banfield JF. 2000. An archaeal iron-oxidizing extreme acidophile

important in acid mine drainage. Science 287:1796–9913. Edwards RA, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, et al. 2006. Using pyrosequencing

to shed light on deep mine microbial ecology. BMC Genomics 7:5714. Fierer N, Breitbart M, Nulton J, Salamon P, Lozupone C, et al. 2007. Metagenomic and small-subunit

rRNA analyses of the genetic diversity of bacteria, archaea, fungi, and viruses in soil. Appl. Environ.Microbiol. 73:7059–66

15. Gilbert MT, Tomsho LP, Rendulic S, Packard M, Drautz DI, et al. 2007. Whole-genome shotgunsequencing of mitochondria from ancient hair shafts. Science 317:1927–30

16. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, et al. 2006. Metagenomic analysis of the humandistal gut microbiome. Science 312:1355–59

17. Green RE, Krause J, Ptak SE, Briggs AW, Ronan MT, et al. 2006. Analysis of one million base pairs ofNeanderthal DNA. Nature 444:330–36

18. Houwing S, Kamminga LM, Berezikov E, Cronembold D, Girard A, et al. 2007. A role for Piwi andpiRNAs in germ cell maintenance and transposon silencing in zebrafish. Cell 129:69–82

400 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

19. Huber JA, Mark Welch DB, Morrison HG, Huse SM, Neal PR, et al. 2007. Microbial population structuresin the deep marine biosphere. Science 318:97–100

20. Johnson DS, Mortazavi A, Myers RM, Wold B. 2007. Genome-wide mapping of in vivo protein-DNAinteractions. Science 316:1497–502

21. Johnson SM, Tan FJ, McCullough HL, Riordan DP, Fire AZ. 2006. Flexibility and constraint in thenucleosome core landscape of Caenorhabditis elegans chromatin. Genome Res. 16:1505–16

22. Kasschau KD, Fahlgren N, Chapman EJ, Sullivan CM, Cumbie JS, et al. 2007. Genome-wide profilingand analysis of Arabidopsis siRNAs. PLoS Biol. 5:e57

23. Krause J, Dear PH, Pollack JL, Slatkin M, Spriggs H, et al. 2006. Multiplex amplification of the mammothmitochondrial genome and the evolution of Elephantidae. Nature 439:724–27

24. Krings M, Geisert H, Schmitz RW, Krainitzki H, Paabo S. 1999. DNA sequence of the mitochondrialhypervariable region II from the Neandertal type specimen. Proc. Natl. Acad. Sci. USA 96:5581–85

25. Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H, et al. 2007. Comparative metagenomics revealedcommonly enriched gene sets in human gut microbiomes. DNA Res. 14:169–81

26. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, et al. 2001. Initial sequencing and analysis ofthe human genome. Nature 409:860–921

27. Leininger S, Urich T, Schloter M, Schwark L, Qi J, et al. 2006. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442:806–9

28. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. 2005. Obesity alters gutmicrobial ecology. Proc. Natl. Acad. Sci. USA 102:11070–75

29. Lu C, Meyers BC, Green PJ. 2007. Construction of small RNA cDNA libraries for deep sequencing.Methods 43:110–17

30. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, et al. 2005. Genome sequencing in microfab-ricated high-density picolitre reactors. Nature 437:376–80

31. McPherson JD, Marra M, Hillier L, Waterston RH, Chinwalla A, et al. 2001. A physical map of the humangenome. Nature 409:934–41

32. Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, et al. 2007. Genome-wide maps of chromatin statein pluripotent and lineage-committed cells. Nature 448:553–60

33. Noonan JP, Coop G, Kudaravalli S, Smith D, Krause J, et al. 2006. Sequencing and analysis of Neanderthalgenomic DNA. Science 314:1113–18

34. Noonan JP, Hofreiter M, Smith D, Priest JR, Rohland N, et al. 2005. Genomic sequencing of Pleistocenecave bears. Science 309:597–99

35. Ovchinnikov IV, Gotherstrom A, Romanova GP, Kharitonov VM, Liden K, Goodwin W. 2000. Molecularanalysis of Neanderthal DNA from the northern Caucasus. Nature 404:490–93

36. Palmer C, Bik EM, Digiulio DB, Relman DA, Brown PO. 2007. Development of the human infantintestinal microbiota. PLoS Biol. 5:e177

37. Poinar HN, Schwarz C, Qi J, Shapiro B, Macphee RD, et al. 2006. Metagenomics to paleogenomics:large-scale sequencing of mammoth DNA. Science 311:392–94

38. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, et al. 2000. Genome-wide location and functionof DNA binding proteins. Science 290:2306–9

39. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, et al. 2007. Genome-wide profiles of STAT1DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods4:651–57

40. Rogaev EI, Moliaka YK, Malyarchuk BA, Kondrashov FA, Derenko MV, et al. 2006. Complete mito-chondrial genome and phylogeny of Pleistocene mammoth Mammuthus primigenius. PLoS Biol. 4:e73

41. Ruby JG, Jan C, Player C, Axtell MJ, Lee W, et al. 2006. Large-scale sequencing reveals 21U-RNAs andadditional microRNAs and endogenous siRNAs in C. elegans. Cell 127:1193–207

42. Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, et al. 2006. Microbial diversity in the deepsea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103:12115–20

43. Solomon MJ, Larsen PL, Varshavsky A. 1988. Mapping protein-DNA interactions in vivo with formalde-hyde: evidence that histone H4 is retained on a highly transcribed gene. Cell 53:937–47

44. Stahlhut Espinosa CE, Slack FJ. 2006. The role of microRNAs in cancer. Yale J. Biol. Med. 79:131–40

www.annualreviews.org • Next-Generation DNA Sequencing Methods 401

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

ANRV353-GG09-20 ARI 25 July 2008 14:57

45. Strous M, Pelletier E, Mangenot S, Rattei T, Lehner A, et al. 2006. Deciphering the evolution andmetabolism of an anammox bacterium from a community genome. Nature 440:790–94

46. Torres TT, Metta M, Ottenwalder B, Schlotterer C. 2007. Gene expression profiling by massively parallelsequencing. Genome Res. 18:172–77

47. Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, et al. 2005. Comparative metagenomicsof microbial communities. Science 308:554–57

48. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. 2006. An obesity-associatedgut microbiome with increased capacity for energy harvest. Nature 444:1027–31

49. Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, et al. 2004. Community structure andmetabolism through reconstruction of microbial genomes from the environment. Nature 428:37–43

50. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. 1995. Serial analysis of gene expression. Science270:484–87

51. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, et al. 2001. The sequence of the human genome.Science 291:1304–51

52. Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, et al. 2004. Environmental genomeshotgun sequencing of the Sargasso Sea. Science 304:66–74

53. Weber AP, Weber KL, Carr K, Wilkerson C, Ohlrogge JB. 2007. Sampling the Arabidopsis transcriptomewith massively parallel pyrosequencing. Plant Physiol. 144:32–42

54. Wegley L, Edwards R, Rodriguez-Brito B, Liu H, Rohwer F. 2007. Metagenomic analysis of the microbialcommunity associated with the coral Porites astreoides. Environ. Microbiol. 9:2707–19

55. Zhao T, Li G, Mi S, Li S, Hannon GJ, et al. 2007. A complex system of small RNAs in the unicellulargreen alga Chlamydomonas reinhardtii. Genes Dev. 21:1190–203

402 Mardis

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

AR353-GG09-FM ARI 25 July 2008 5:19

Annual Review ofGenomics andHuman Genetics

Volume 9, 2008Contents

Human Telomere Structure and BiologyHarold Riethman � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Infectious Disease in the Genomic EraXiaonan Yang, Hongliang Yang, Gangqiao Zhou, and Guo-Ping Zhao � � � � � � � � � � � � � � � � � � �21

ENU Mutagenesis, a Way Forward to Understand Gene FunctionAbraham Acevedo-Arozena, Sara Wells, Paul Potter, Michelle Kelly,

Roger D. Cox, and Steve D.M. Brown � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Clinical Utility of Contemporary Molecular CytogeneticsBassem A. Bejjani and Lisa G. Shaffer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �71

The Role of Aminoacyl-tRNA Synthetases in Genetic DiseasesAnthony Antonellis and Eric D. Green � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �87

A Bird’s-Eye View of Sex Chromosome Dosage CompensationArthur P. Arnold, Yuichiro Itoh, and Esther Melamed � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 109

Linkage Disequilibrium and Association MappingB. S. Weir � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 129

Positive Selection in the Human Genome: From Genome Scansto Biological SignificanceJoanna L. Kelley and Willie J. Swanson � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 143

The Current Landscape for Direct-to-Consumer Genetic Testing:Legal, Ethical, and Policy IssuesStuart Hogarth, Gail Javitt, and David Melzer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 161

Transcriptional Control of SkeletogenesisGerard Karsenty � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 183

A Mechanistic View of Genomic ImprintingKy Sha � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 197

Phylogenetic Inference Using Whole GenomesBruce Rannala and Ziheng Yang � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 217

v

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

AR353-GG09-FM ARI 25 July 2008 5:19

Transgenerational Epigenetic EffectsNeil A. Youngson and Emma Whitelaw � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 233

Evolution of Dim-Light and Color Vision PigmentsShozo Yokoyama � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 259

Genetic Basis of Thoracic Aortic Aneurysms and Dissections: Focuson Smooth Muscle Cell Contractile DysfunctionDianna M. Milewicz, Dong-Chuan Guo, Van Tran-Fadulu, Andrea L. Lafont,

Christina L. Papke, Sakiko Inamoto, and Hariyadarshi Pannu � � � � � � � � � � � � � � � � � � � � � � � � � � � 283

Cohesin and Human DiseaseJinglan Liu and Ian D. Krantz � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 303

Genetic Predisposition to Breast Cancer: Past, Present, and FutureClare Turnbull and Nazneen Rahman � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 321

From Linkage Maps to Quantitative Trait Loci: The Historyand Science of the Utah Genetic Reference ProjectStephen M. Prescott, Jean Marc Lalouel, and Mark Leppert � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 347

Disorders of Lysosome-Related Organelle Biogenesis: Clinicaland Molecular GeneticsMarjan Huizing, Amanda Helip-Wooley, Wendy Westbroek,

Meral Gunay-Aygun, and William A. Gahl � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 359

Next-Generation DNA Sequencing MethodsElaine R. Mardis � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 387

African Genetic Diversity: Implications for Human DemographicHistory, Modern Human Origins, and Complex Disease MappingMichael C. Campbell and Sarah A. Tishkoff � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 403

Indexes

Cumulative Index of Contributing Authors, Volumes 1–9 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 435

Cumulative Index of Chapter Titles, Volumes 1–9 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 438

Errata

An online log of corrections to Annual Review of Genomics and Human Genetics articlesmay be found at http://genom.annualreviews.org/

vi Contents

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.

AnnuAl Reviewsit’s about time. Your time. it’s time well spent.

AnnuAl Reviews | Connect with Our expertsTel: 800.523.8635 (us/can) | Tel: 650.493.4400 | Fax: 650.424.0910 | Email: service@annualreviews.org

New From Annual Reviews:

Annual Review of Statistics and Its ApplicationVolume 1 • Online January 2014 • http://statistics.annualreviews.org

Editor: Stephen E. Fienberg, Carnegie Mellon UniversityAssociate Editors: Nancy Reid, University of Toronto

Stephen M. Stigler, University of ChicagoThe Annual Review of Statistics and Its Application aims to inform statisticians and quantitative methodologists, as well as all scientists and users of statistics about major methodological advances and the computational tools that allow for their implementation. It will include developments in the field of statistics, including theoretical statistical underpinnings of new methodology, as well as developments in specific application domains such as biostatistics and bioinformatics, economics, machine learning, psychology, sociology, and aspects of the physical sciences.

Complimentary online access to the first volume will be available until January 2015. table of contents:•What Is Statistics? Stephen E. Fienberg•A Systematic Statistical Approach to Evaluating Evidence

from Observational Studies, David Madigan, Paul E. Stang, Jesse A. Berlin, Martijn Schuemie, J. Marc Overhage, Marc A. Suchard, Bill Dumouchel, Abraham G. Hartzema, Patrick B. Ryan

•The Role of Statistics in the Discovery of a Higgs Boson, David A. van Dyk

•Brain Imaging Analysis, F. DuBois Bowman•Statistics and Climate, Peter Guttorp•Climate Simulators and Climate Projections,

Jonathan Rougier, Michael Goldstein•Probabilistic Forecasting, Tilmann Gneiting,

Matthias Katzfuss•Bayesian Computational Tools, Christian P. Robert•Bayesian Computation Via Markov Chain Monte Carlo,

Radu V. Craiu, Jeffrey S. Rosenthal•Build, Compute, Critique, Repeat: Data Analysis with Latent

Variable Models, David M. Blei•Structured Regularizers for High-Dimensional Problems:

Statistical and Computational Issues, Martin J. Wainwright

•High-Dimensional Statistics with a View Toward Applications in Biology, Peter Bühlmann, Markus Kalisch, Lukas Meier

•Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data, Kenneth Lange, Jeanette C. Papp, Janet S. Sinsheimer, Eric M. Sobel

•Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond, Elena A. Erosheva, Ross L. Matsueda, Donatello Telesca

•Event History Analysis, Niels Keiding•StatisticalEvaluationofForensicDNAProfileEvidence,

Christopher D. Steele, David J. Balding•Using League Table Rankings in Public Policy Formation:

Statistical Issues, Harvey Goldstein•Statistical Ecology, Ruth King•Estimating the Number of Species in Microbial Diversity

Studies, John Bunge, Amy Willis, Fiona Walsh•Dynamic Treatment Regimes, Bibhas Chakraborty,

Susan A. Murphy•Statistics and Related Topics in Single-Molecule Biophysics,

Hong Qian, S.C. Kou•Statistics and Quantitative Risk Management for Banking

and Insurance, Paul Embrechts, Marius Hofert

Access this and all other Annual Reviews journals via your institution at www.annualreviews.org.

Ann

u. R

ev. G

enom

. Hum

an G

enet

. 200

8.9:

387-

402.

Dow

nloa

ded

from

ww

w.a

nnua

lrev

iew

s.or

g A

cces

s pr

ovid

ed b

y U

nive

rsity

of

Pais

Vas

co-V

izca

ya U

nive

rsity

on

01/2

9/15

. For

per

sona

l use

onl

y.