See discussions, stats, and author profiles for this publication at: https://www.researchgate.

net/publication/260197220

An Introduction to Functional Genomics and Systems Biology

Article  in  Advances in Wound Care · November 2013

DOI: 10.1089/wound.2012.0379 · Source: PubMed

CITATIONS READS
32 27,862

2 authors:

Evelien M Bunnik Karine G Le Roch

University of Texas Health Science Center at San Antonio University of California, Riverside
100 PUBLICATIONS   2,013 CITATIONS    197 PUBLICATIONS   9,938 CITATIONS   

SEE PROFILE SEE PROFILE

Some of the authors of this publication are also working on these related projects:

RAPs- mediated post-transcriptional control in Apicomplexan parasites View project

NIH: International Cooperative Biodiversity Groups (ICBG) Drug Discovery View project

All content following this page was uploaded by Evelien M Bunnik on 20 February 2014.

The user has requested enhancement of the downloaded file.

 TECHNOLOGY ADVANCES

An Introduction to Functional Genomics

and Systems Biology

Evelien M. Bunnik and Karine G. Le Roch*

Department of Cell Biology and Neuroscience, University of California, Riverside, California.

Objective: Over the past decade, the development of high-throughput technologies

for DNA and protein analysis has revolutionized the ways in which cells can be
studied. Within a relatively short time frame, research has changed from studying
individual genes and proteins to analyzing entire genomes and proteomes.
Approach: In this article, we summarize the technologies and concepts that form
the basis of this functional genomics approach.
Results: Microarray and next-generation sequencing technologies have allowed
researchers to investigate many different aspects of the cell, including DNA mu-
tations, histone modifications, DNA methylation, chromatin structure, transcrip-
Karine G. Le Roch, PhD tion, and translation on a genome-wide level. In addition, mass spectrometry
technologies have undergone significant development and currently enable us to
Submitted for publication November 8, 2012.
Accepted in revised form December 12, 2012.
globally profile protein levels, protein–protein interactions, post-translational
*Correspondence: Institute for Integrative protein modifications, and metabolites.
Genome Biology, Center for Disease Vector Re- Innovation and Conclusion: The integration of information from the various pro-
search, Department of Cell Biology and Neu-
roscience, University of California–Riverside, 900
cesses that occur within a cell provides a more complete picture of how genes give
University Ave., Genomics Building Room 2121B, rise to biological functions, and will ultimately help us to understand the biology of
Riverside, CA 92521 (e-mail: karine.leroch@ucr.edu). organisms, in both health and disease.

Abbreviations
and Acronyms
INTRODUCTION ability of complete genome sequences.
The field of functional genomics Since the landmark publication of the
AT = adenine + thymine
attempts to describe the functions first draft of the human genome in
cDNA = complementary and interactions of genes and pro- 2001,1,2 the genomes of hundreds of
deoxyribonucleic acid
teins by making use of genome-wide organisms from all branches of the tree
ChIP-Seq = chromatin immuno- approaches, in contrast to the gene- of life have been sequenced. This has
precipitation coupled to next-
generation sequencing
by-gene approach of classical molec- lead to improved annotations of genes
ular biology techniques. It combines and their products, and has enabled
cRNA = complementary ribo-
nucleic acid
data derived from the various pro- genome-wide studies aimed at under-
cesses related to DNA sequence, gene standing interactions and molecular
ddNTP = dideoxy nucleotide
triphosphate
expression, and protein function, such processes in the cell.
as coding and noncoding transcription,
DNA = deoxyribonucleic acid
protein translation, protein–DNA,
FAIRE = formaldehyde-assisted protein–RNA, and protein–protein CLINICAL PROBLEMS
isolation of regulatory elements ADDRESSED
interactions. Together, these data
GeLC-MS = gel electrophoresis are used to model interactive and This article will give a brief over-
coupled to liquid chromatogra-
phy and mass spectrometry
dynamic networks that regulate view of high-throughput omics tech-
gene expression, cell differentiation, nologies and their applications, and
(continued) and cell cycle progression. how these powerful tools have ex-
Studying cells at a systems level has panded the possibilities for studying
been facilitated by recent technological the complex biology of cells, organs,
advancements, as well as the avail- and full organisms.

490 j ADVANCES IN WOUND CARE, VOLUME 2, NUMBER 9

Copyright ª 2013 by Mary Ann Liebert, Inc. DOI: 10.1089/wound.2012.0379
 FUNCTIONAL GENOMICS AND SYSTEMS BIOLOGY 491

MATERIALS AND METHODS Over the years, sequencing pipe-

DNA microarrays lines have greatly improved in Abbreviations
throughput and costs for instru- and Acronyms (continued)
DNA microarrays consist of thou-
sands of microscopic DNA spots ments and reagents, along with im- MAINE = micrococcal nucle-
provements in computational power, ase-assisted isolation
(probes) that are bound to a solid sur-
of nucleosomes
face, such as glass or a silicon chip data storage, and bioinformatics
tools that facilitate the analysis of mRNA = messenger ribonucleic
(Affymetrix) or microscopic beads (Il-
acid
lumina). Labeled single-stranded DNA the growing quantities of sequence
reads. Together, these advancements MS = mass spectrometry
or antisense RNA fragments from a
sample of interest are hybridized to have caused a dramatic drop in se- MudPIT = multidimensional
quencing costs, down to about $0.09 protein identification
the DNA microarray under high-
technology
stringency conditions. Each probe is (U.S.) per megabase in early 2012.7
Several new companies, such as He- NGS = next-generation
identified by its location on the DNA
sequencing
microarray, and the amount of hy- licos Biosciences, Pacific Biosciences,
and Oxford Nanopore Technologies, NMR = nuclear magnetic
bridization detected for a specific probe
resonance
is proportional to the level of nucleic are currently developing novel, so-
called third generation sequencing RNA = ribonucleic acid
acids from the corresponding genomic
location in the original sample. techniques that do not require am- RNA-Seq = high-throughput ri-
plification of template DNA, but are bonucleic acid sequencing
Next-generation sequencing able to read the sequence of single SAGE = serial analysis of gene
technologies DNA molecules.8,9 These technolo- expression
Three main next-generation se- gies could significantly advance the SDS-PAGE = sodium dodecyl
quencing (NGS) platforms are widely sequencing field by greatly reducing sulfate–polyacrylamide gel
used: the Roche 454 platform (Roche the cost for reagents and improving electrophoresis
Life Sciences),3 the Applied Biosys- the throughput, while simultaneously TAP = tandem affinity
tems SOLiD platform (Applied Bio- eliminating any bias introduced dur- purification
systems),4 and the Illumina (formerly ing the template amplification step of
known as Solexa) Genome Analyzer the NGS protocol.
and HiSeq platforms (Illumina).5
For these three NGS platforms, tem- Mass spectrometry
plate DNA is fragmented, bound to A mass spectrometer consists of
adaptors, amplified by polymerase three components: an ion source to
chain reaction, and subsequently im- convert a gas-phase sample into ions,
mobilized on beads or on an array a mass analyzer to separate the ions
where clusters consisting of identical by means of an electromagnetic field,
DNA fragments are formed. These and a detector. The development of
clusters are read by sequential cycles ionization techniques that enable
of nucleotide incorporation, washing, the transfer of proteins and peptides
and detection, where the number into the gas phase without substan-
of cycles eventually determines the tial degradation has been crucial
read length (Fig. 1). A fourth DNA se- for the application of mass spec-
quencing technology has been recently trometry (MS) in large-scale pro-
developed by Ion Torrent. The Ion teomic studies. The most commonly
Torrent technology takes advantage of used ionization techniques are
the hydrogen ion that is released as a matrix-assisted laser desorption ion-
byproduct of the incorporation of a ization and electrospray ionization.
nucleotide into a DNA strand by poly- These ionization techniques can be
merase. The sequencer directly senses combined with various types of mass
the ions produced by template-directed analyzers that separate ions based
DNA polymerase synthesis on a mas- on the mass-to-charge ratio by ei-
sive parallel semiconductor-sensing ther trapping ions in an electrical
device that directly transforms this field (trapping mass spectrometers)
chemical signal to digital information.6 or by accelerating ions through an
492 BUNNIK AND LE ROCH

Figure 1. Comparison between Sanger sequencing and next-generation sequencing (NGS) technologies. Sanger sequencing is limited to determining the order
of one fragment of DNA per reaction, up to a maximum length of *700 bases. NGS platforms can sequence millions of DNA fragments in parallel in one reaction,
yielding enormous amounts of data. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound

electrical field and measuring the time-of-flight. A Sanger dideoxy sequencing method. Sanger se-
comparison of instrument configurations that quencing requires each sequence read to be am-
are most commonly used in proteomics is provided plified and read individually (Fig. 1). Despite
elsewhere.10 The most advanced mass spec- considerable improvements in automation and
trometer available to date is the Orbitrap, which throughput, Sanger sequencing remains relatively
has a high resolution, a high mass accuracy, and expensive and labor intensive. For whole-genome
a large dynamic range that make it suitable for sequencing, it is dependent upon bacterial cloning,
a wide range of proteomics and metabolomics which is time-consuming and can introduce biases
applications. as a result of, for example, difficulties in cloning
The most common strategy for proteomic studies AT-rich fragments or genes that are toxic to bac-
is a bottom-up approach, in which a protein sample teria. Since 2005, several NGS technologies have
is first enzymatically digested into smaller pep- become commercially available, which have
tides, followed by separation of the peptides by transformed the field of whole-genome sequencing.
charge, hydrophobicity, or a combination of these The amount of data generated in parallel from
characteristics, and then injected into the mass small amounts of DNA is enormous, and currently
spectrometer. Individual peptide spectra are used reaches up to 6 billion short reads or 600 gigabase
to indirectly identify complete proteins that were per instrument run. This has greatly facilitated the
present in the original sample. sequencing of the complete genome of organisms to
identify DNA mutations, ranging from single-
nucleotide polymorphisms to large gene deletion or
RESULTS duplication events. In addition, these technologies
Genomics have enabled a range of novel applications, in-
For almost 30 years, sequencing of DNA has cluding genome-wide analysis of epigenetic mech-
largely been dependent on the first-generation anisms, such as DNA methylation, location of
 FUNCTIONAL GENOMICS AND SYSTEMS BIOLOGY 493

histone modifications, transcription factors bind- perimental setup and data analysis yields highly
ing events, and nucleosome positioning, as well as accurate positioning of modified histones. Novel
profiling of gene expression (see Transcriptomics experimental procedures are continuously being
section). applied to achieve even a higher resolution, recently
Many of these applications are based on map- yielding single-base pair resolution for both tran-
ping short reads of DNA obtained from a particular scription factor binding sites12 and nucleosome
sample to a reference genome and analyzing the positioning.13
distribution of these reads (Fig. 2).11 For example, NGS has greatly improved our ability to study
for determining the locations of a particular histone the various genetic and epigenetic mechanisms,
modification, chromatin is sheared into mono- with unprecedented detail and specificity. This
nucleosomal fragments. Chromatin fragments con- information has provided us with enormous insight
taining the histone modification of interest are into gene regulation and cell cycle control, as well
immunoprecipitated and the corresponding DNA as the roll of mutations and epigenetic mechanisms
fragments are sequenced (ChIP-Seq). Since only the in pathogenesis.
5¢ end of DNA fragments are sequenced, the se-
quence reads obtained in this experiment will map Transcriptomics
to the outer side of the nucleosome. However, the Regulation of gene expression is fundamentally
midpoint of the histone can be determined by ex- important for cell development and differentiation.
trapolation of the distribution of sequence reads Profiling the abundance of transcripts in different
from either side of the nucleosome. This type of ex- cell types and under various conditions increases

Figure 2. Applications of NGS. The types of experiments that can be performed using NGS are many fold and are certainly not limited to the applications listed
here. Applications include sequencing the complete genome or exome (all coding regions of the genome) to identify single-nucleotide polymorphisms (SNP-Seq)
or other DNA mutations, profiling the genome-wide locations of methylated cytosines (Bisulfite-Seq), investigating various aspects of chromatin structure and
regulation of gene expression by determining nucleosome positioning (MAINE-Seq and FAIRE-Seq), histone modifications or transcription factor binding (ChIP-
Seq), and determining mRNA levels to study gene expression and its regulation (RNA-Seq). To see this illustration in color, the reader is referred to the web
version of this article at www.liebertpub.com/wound
494 BUNNIK AND LE ROCH

our knowledge about gene function and regulatory other tool used for gene expression analysis in the
pathways. In the past, RNA transcripts have been past. An additional advantage of RNA-Seq over
analyzed using Northern blotting or reverse tran- DNA microarrays is that it is not dependent on the
scription polymerase chain reaction, which are re- availability of a microarray for the species of in-
stricted to limited numbers of known transcripts. terest and can therefore be implemented for all
Serial analysis of gene expression (SAGE) was de- organisms.
veloped in 1995, and consists of sequencing small
Proteomics
tags that correspond to the 3¢ fragments of mes-
Proteins are one of the functional units of the
senger RNA (mRNA).14 This allows for a highly
cell, and it is therefore essential to understand how
quantitative analysis by simply counting the
proteins function to completely understand bio-
number of tags that map to a particular gene. De-
logical processes. Since transcript levels do not
spite several improvements to the original protocol,
necessarily correlate with protein levels, quanti-
SAGE is no longer widely used as it is very labor
tation of proteins is required to unequivocally de-
intensive and relatively low-throughput compared
termine their abundance. In addition, many
to newly developed NGS applications.
proteins are post-translationally modified, adding
Around the same time, the first DNA micro-
an extra level of complexity to their structure and
arrays were developed for measuring the expres-
function.
sion levels of large numbers of genes.15,16
Analysis of the protein content of cells can be
Transcripts isolated from a sample of interest are
performed by two-dimensional gel electrophoresis,
converted into cDNA or cRNA, are labeled, and are
where proteins are separated first by size, and
subsequently hybridized to the DNA microarray.
then by charge, followed by MS. Another relatively
The amount of hybridization detected for a specific
straightforward proteomics approach is geLC-MS,
probe is proportional to the transcript level of the
where proteins are first separated by one-
corresponding gene. Comparing transcript levels
dimensional gel electrophoresis (SDS-PAGE).
between various cell types or conditions can be
Each gel lane is then divided into equally sized
used to identify genes that are involved in cell dif-
sections, and the proteins from each section are
ferentiation or in responses to certain environ-
digested, separated by liquid chromatography, and
mental changes. Cluster analysis is often employed
analyzed by MS. More recently, a high-throughput
to characterize genes that have similar expression
technology has been developed that is more suit-
profiles and are therefore likely to have similar
able for identifying large numbers of proteins from
biological functions. DNA microarrays are still in
complex mixtures. Using the multidimensional
use today and continue to provide valuable biolog-
protein identification technology (MudPIT), pro-
ical information, although it is to be expected that
teins are digested into peptides that are then
gene expression profiling will shift more and more
separated by means of two-dimensional chroma-
toward the use of NGS tools.
tography, based on both charge and hydrophobic-
NGS technologies have opened the door for a
ity, and are subsequently analyzed by MS.17 The
broad range of genome-wide analyses related to
signals of each peptide obtained using MS can then
gene expression and transcript profiles, which are
be compared to a database of previously sequenced
collectively known as RNA-Seq (Fig. 2). Sequence
proteins or to a database of predicted proteins based
reads derived from an RNA sample of interest are
on the genome sequence to identify the protein from
mapped to a reference genome, where the number
which the peptide was derived. MudPIT allows for a
of reads that map to a certain gene corresponds to
highly sensitive detection of proteins and has over
the expression level of that gene. Besides profiling
the last decade been applied to a broad range of cells
gene expression levels, RNA-Seq can be used to
and organisms. It has successfully been used to
analyze transcript boundaries and intron/exon
profile organelle and membrane proteins, identify
junctions and to discover novel transcripts and
post-translational modifications, dissect protein
novel alternative splice variants. In addition, it can
complexes, and analyze protein expression.
be applied to, for example, profiling of noncoding
RNA, nascent transcripts, and ribosome-associated Interactomics
mRNA, and has the potential to immensely in- Determining the abundance and localization of a
crease our understanding of the different roles of protein is not sufficient to understand its function.
RNA and of the various levels of regulation of Many molecular processes in the cell are performed
gene expression. RNA-Seq provides a combination by complexes of proteins that are organized
of high-throughput, large sequencing depth, and by protein–protein interactions. Such functional
genome-wide coverage, which is not offered by any interactions are found in signal transduction,
 FUNCTIONAL GENOMICS AND SYSTEMS BIOLOGY 495

transcriptional regulation, metabolic pathways, protein partners. However, the rate of false posi-
and many other biological functions. Deciphering tives is relatively high, and interactions found by
these interactions is crucial to understanding the the two-hybrid technique should always be vali-
interactive pathways and networks that form the dated using other tools.
basis of many cellular processes. While the two-hybrid system is limited to
Protein–protein interactions can be studied us- screening the interaction between two proteins at a
ing a variety of methods. The two-hybrid system time, affinity purification methods may be more
has been used for the first time in 19897 and has suitable to study the organization of proteins into
since then been modified to allow proteome-scale complexes. This technique entails fusing a tag to a
screening.18,19 In the two-hybrid method, one pro- protein of interest, which is subsequently used to
tein of interest is fused to a DNA binding domain, isolate this protein together with all proteins that
while another protein of interest is fused to an ac- are bound. The bound proteins are then analyzed
tivation domain. Both fusion proteins are then ex- by MS. In 2002, this technique was first performed
pressed in the same cell, which could in theory be in yeast and revealed thousands of protein–protein
any living cell, although yeast and bacterial cells interactions, many of which had not been described
are most widely used. If the proteins interact, a before.20,21 Since then, the tandem affinity purifi-
reporter gene is transcriptionally activated, which cation (TAP) strategy has become increasingly
will change the phenotype of the cell and allow for popular. TAP involves two rounds of affinity puri-
an easy readout. In addition to the original two- fication that provide a high specificity, but may on
hybrid system, which requires proteins to be pres- the other hand result in the loss of transient or very
ent in the nucleus, the cytotrap yeast two-hybrid weak protein–protein interactions. In combination
tool has been developed for detection of protein– with other tools, such as protein microarray and
protein interactions in the cytoplasm. The two- phage display, these technologies have vastly in-
hybrid technique is relatively straightforward and creased our understanding of interactive protein
can be used as a first screen to identify interacting networks.

Figure 3. Schematic overview of network analysis. Integration of information from different aspects of the cell, such as genome, transcriptome, proteome,
interactome, and metabolome, will increase our understanding of how these components are interconnected and how these interactions determine biological
functions. To see this illustration in color, the reader is referred to the web version of this article at www.liebertpub.com/wound
496 BUNNIK AND LE ROCH

Metabolomics
KEY FINDINGS
Metabolites are small molecules,
 NGS technologies enable a range of applications for studying various
such as amino acids, sugars, and fatty
cellular processes related to DNA, chromatin structure, transcription, and
acids, that are chemically transformed
translation on a genome-wide level.
by enzymes during metabolism and that
play critical roles in various biological  Advances in MS allow large-scale studies into proteins, protein–protein
processes. Metabolite levels correlate interactions, post-translational protein modifications, and metabolites.
more directly with a cellular phenotype  Integration of genome-wide data by network analyses will improve our
than genes or proteins, and therefore understanding of cellular biology.
provide an accurate functional readout
of the state of a cell. Researchers have
long been interested in profiling metabolites on a will allow differentiation between relatively static
global level, but only recently technologies have housekeeping genes and the dynamic processes
emerged that enable these types of studies. The involved in response to stress or other external and
tools most widely used for global metabolomics internal signals.22 This will ultimately lead to
approaches are nuclear magnetic resonance (NMR) building improved models of biologically relevant
and liquid chromatography coupled to MS. The interactions between all components of a cell.
main advantages of NMR are its high reproduc-
ibility and ease of sample preparation. However,
INNOVATION
the sensitivity of MS-based techniques is higher
compared to NMR, and allows the detection of most Novel technologies developed over the past de-
metabolites present in a cell. Information from cade allow a systems biology approach to studying
metabolomics studies will increase our under- the complex processes that shape cells, organs, and
standing of complex cellular metabolism, charac- organisms. Instead of focusing on single genes or
terize new metabolic pathways, and identify new proteins, NGS platforms and MS applications pro-
targets for therapeutic intervention in, for exam- vide the opportunity to study genes, transcripts,
ple, cancer. proteins, and their interactions on a genome-wide
level. Ultimately, the integration of this information
will result in an improved understanding of how
DISCUSSION genes give rise to biological functions.
With a plethora of information emerging from
various omics studies, the main challenge in sys- ACKNOWLEDGMENTS
tems biology is to integrate these data into a single AND FUNDING SOURCES
network and to find out how genes, transcripts, E.M.B. is supported by the Human Frontier
proteins, and metabolites interact to regulate the Science Program (grant LT00507/2011-L) and
biological processes that determine cell function K.G.R. is supported by the National Institutes of
and cell cycle progression (Fig. 3). The availability Health (grant R01 AI85077-01A1).
of large amounts of data has led to the development
of more robust computational methods for network
analysis. These tools can, for example, be used to AUTHOR DISCLOSURE AND GHOSTWRITING
predict protein function by means of guilt-by- No competing financial interests exist. The con-
association analysis. This type of analysis is based tent of this article was expressly written by the
on the principle that the function of a protein is authors listed. No ghostwriters were used to write
likely to resemble the function of proteins with this article.
which it interacts or is coexpressed. In addition,
multiple tools are available that support pathway
analyses to determine whether certain pathways ABOUT THE AUTHORS
or gene ontologies are over-represented in certain Dr. Evelien Bunnik is a post-doctoral fellow
biological processes. and Dr. Karine Le Roch is an associate professor
To obtain accurate and complete cell models, at the University of California, Riverside, CA. They
network analysis should not only be based on ex- use functional genomics approaches, such as pro-
periments performed in model organisms under teomics and high-throughput sequencing technol-
standard laboratory conditions. In contrast, using ogies to elucidate critical regulatory networks
information obtained from multiple species, cell driving the malaria parasite life cycle progression
types, or under various environmental conditions and to identify novel drug targets.
 FUNCTIONAL GENOMICS AND SYSTEMS BIOLOGY 497

REFERENCES
1. Lander ES, Linton LM, Birren B, Nusbaum C, 2. Venter JC, Adams MD, Myers EW, Li PW, Mural Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM,
Zody MC, Baldwin J, Devon K, Dewar K, Doyle RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH,
M, FitzHugh W, Funke R, Gage D, Harris K, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Ji-
Heaford A, Howland J, Kann L, Lehoczky J, Le- Huson DH, Wortman JR, Zhang Q, Kodira CD, rage KB, Kim JB, Knight JR, Lanza JR, Leamon JH,
Vine R, McEwan P, McKernan K, Meldrim J, Zheng XH, Chen L, Skupski M, Subramanian G, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H,
Mesirov JP, Miranda C, Morris W, Naylor J, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Makhijani VB, McDade KE, McKenna MP, Myers
Raymond C, Rosetti M, Santos R, Sheridan A, Broder S, Clark AG, Nadeau J, McKusick VA, EW, Nickerson E, Nobile JR, Plant R, Puc BP,
Sougnez C, Stange-Thomann N, Stojanovic N, Zinder N, Levine AJ, Roberts RJ, Simon M, Ronan MT, Roth GT, Sarkis GJ, Simons JF,
Subramanian A, Wyman D, Rogers J, Sulston J, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Simpson JW, Srinivasan M, Tartaro KR, Tomasz
Ainscough R, Beck S, Bentley D, Burton J, Clee C, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, A, Vogt KA, Volkmer GA, Wang SH, Wang Y,
Carter N, Coulson A, Deadman R, Deloukas P, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Re- Weiner MP, Yu P, Begley RF, and Rothberg JM:
Dunham A, Dunham I, Durbin R, French L, Graf- inert K, Remington K, Abu-Threideh J, Beasley E, Genome sequencing in microfabricated high-den-
ham D, Gregory S, Hubbard T, Humphray S, Hunt Biddick K, Bonazzi V, Brandon R, Cargill M, sity picolitre reactors. Nature 2005; 437: 376.
A, Jones M, Lloyd C, McMurray A, Matthews L, Chandramouliswaran I, Charlab R, Chaturvedi K,
4. Shendure J, Porreca GJ, Reppas NB, Lin X,
Mercer S, Milne S, Mullikin JC, Mungall A, Deng Z, Di Francesco V, Dunn P, Eilbeck K,
McCutcheon JP, Rosenbaum AM, Wang MD,
Plumb R, Ross M, Shownkeen R, Sims S, Wa- Evangelista C, Gabrielian AE, Gan W, Ge W, Gong
Zhang K, Mitra RD, and Church GM: Accurate
terston RH, Wilson RK, Hillier LW, McPherson F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke
multiplex polony sequencing of an evolved bac-
JD, Marra MA, Mardis ER, Fulton LA, Chinwalla Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin
terial genome. Science 2005; 309: 1728.
AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, X, Lu F, Merkulov GV, Milshina N, Moore HM,
Delehaunty KD, Miner TL, Delehaunty A, Kramer Naik AK, Narayan VA, Neelam B, Nusskern D, 5. Bentley DR, Balasubramanian S, Swerdlow HP,
JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ,
Clifton SW, Hawkins T, Branscomb E, Predki P, Wang Z, Wang A, Wang X, Wang J, Wei M, Barnes CL, Bignell HR, Boutell JM, Bryant J,
Richardson P, Wenning S, Slezak T, Doggett N, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Carter RJ, Keira Cheetham R, Cox AJ, Ellis DJ,
Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Flatbush MR, Gormley NA, Humphray SJ, Irving
E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter LJ, Karbelashvili MS, Kirk SM, Li H, Liu X, Mai-
Bouck JB, Sodergren EJ, Worley KC, Rives CM, S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, singer KS, Murray LJ, Obradovic B, Ost T, Par-
Gorrell JH, Metzker ML, Naylor SL, Kucherlapati An H, Awe A, Baldwin D, Baden H, Barnstead M, kinson ML, Pratt MR, Rasolonjatovo IM, Reed MT,
RS, Nelson DL, Weinstock GM, Sakaki Y, Fu- Barrow I, Beeson K, Busam D, Carver A, Center A, Rigatti R, Rodighiero C, Ross MT, Sabot A, Sankar
jiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Cheng ML, Curry L, Danaher S, Davenport L, De- SV, Scally A, Schroth GP, Smith ME, Smith VP,
Kawagoe C, Watanabe H, Totoki Y, Taylor T, silets R, Dietz S, Dodson K, Doup L, Ferriera S, Spiridou A, Torrance PE, Tzonev SS, Vermaas EH,
Weissenbach J, Heilig R, Saurin W, Artiguenave Garg N, Gluecksmann A, Hart B, Haynes J, Haynes Walter K, Wu X, Zhang L, Alam MD, Anastasi C,
F, Brottier P, Bruls T, Pelletier E, Robert C, C, Heiner C, Hladun S, Hostin D, Houck J, How- Aniebo IC, Bailey DM, Bancarz IR, Banerjee S,
Wincker P, Smith DR, Doucette-Stamm L, Ru- land T, Ibegwam C, Johnson J, Kalush F, Kline L, Barbour SG, Baybayan PA, Benoit VA, Benson KF,
benfield M, Weinstock K, Lee HM, Dubois J, Koduru S, Love A, Mann F, May D, McCawley S, Bevis C, Black PJ, Boodhun A, Brennan JS,
Rosenthal A, Platzer M, Nyakatura G, Taudien S, McIntosh T, McMullen I, Moy M, Moy L, Murphy Bridgham JA, Brown RC, Brown AA, Buermann
Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi DH, Bundu AA, Burrows JC, Carter NP, Castillo N,
Hood L, Rowen L, Madan A, Qin S, Davis RW, H, Reardon M, Rodriguez R, Rogers YH, Romblad Chiara E Catenazzi M, Chang S, Neil Cooley R,
Federspiel NA, Abola AP, Proctor MJ, Myers RM, D, Ruhfel B, Scott R, Sitter C, Smallwood M, Crake NR, Dada OO, Diakoumakos KD, Dom-
Schmutz J, Dickson M, Grimwood J, Cox DR, Stewart E, Strong R, Suh E, Thomas R, Tint NN, inguez-Fernandez B, Earnshaw DJ, Egbujor UC,
Olson MV, Kaul R, Raymond C, Shimizu N, Ka- Tse S, Vech C, Wang G, Wetter J, Williams S, Elmore DW, Etchin SS, Ewan MR, Fedurco M,
wasaki K, Minoshima S, Evans GA, Athanasiou Williams M, Windsor S, Winn-Deen E, Wolfe K, Fraser LJ, Fuentes Fajardo KV, Scott Furey W,
M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Zaveri J, Zaveri K, Abril JF, Guigó R, Campbell George D, Gietzen KJ, Goddard CP, Golda GS,
Lehrach H, Reinhardt R, McCombie WR, de la MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Granieri PA, Green DE, Gustafson DL, Hansen NF,
Bastide M, Dedhia N, Blöcker H, Hornischer K, Lazareva B, Hatton T, Narechania A, Diemer K, Harnish K, Haudenschild CD, Heyer NI, Hims MM,
Nordsiek G, Agarwala R, Aravind L, Bailey JA, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Ho JT, Horgan AM, Hoschler K, Hurwitz S, Ivanov
Bateman A, Batzoglou S, Birney E, Bork P, Brown Lippert R, Schwartz R, Walenz B, Yooseph S, Al- DV, Johnson MQ, James T, Huw Jones TA, Kang
DG, Burge CB, Cerutti L, Chen HC, Church D, len D, Basu A, Baxendale J, Blick L, Caminha M, GD, Kerelska TH, Kersey AD, Khrebtukova I,
Clamp M, Copley RR, Doerks T, Eddy SR, Eichler Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Kindwall AP, Kingsbury Z, Kokko-Gonzales PI,
EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely Kumar A, Laurent MA, Lawley CT, Lee SE, Lee X,
Hayashizaki Y, Haussler D, Hermjakob H, Hokamp D, Esparham S, Fosler C, Gire H, Glanowski S, Liao AK, Loch JA, Lok M, Luo S, Mammen RM,
K, Jang W, Johnson LS, Jones TA, Kasif S, Ka- Glasser K, Glodek A, Gorokhov M, Graham K, Martin JW, McCauley PG, McNitt P, Mehta P,
spryzk A, Kennedy S, Kent WJ, Kitts P, Koonin Gropman B, Harris M, Heil J, Henderson S, Ho- Moon KW, Mullens JW, Newington T, Ning Z, Ling
EV, Korf I, Kulp D, Lancet D, Lowe TM, McLy- over J, Jennings D, Jordan C, Jordan J, Kasha J, Ng B, Novo SM, O’Neill MJ, Osborne MA, Os-
saght A, Mikkelsen T, Moran JV, Mulder N, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, nowski A, Ostadan O, Paraschos LL, Pickering L,
Pollara VJ, Ponting CP, Schuler G, Schultz J, Lopez J, Ma D, Majoros W, McDaniel J, Murphy Pike AC, Pike AC, Chris Pinkard D, Pliskin DP,
Slater G, Smit AF, Stupka E, Szustakowski J, S, Newman M, Nguyen T, Nguyen N, Nodell M, Podhasky J, Quijano VJ, Raczy C, Rae VH, Rawlings
Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis Pan S, Peck J, Peterson M, Rowe W, Sanders R, SR, Chiva Rodriguez A, Roe PM, Rogers J, Rogert
J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Scott J, Simpson M, Smith T, Sprague A, Stock- Bacigalupo MC, Romanov N, Romieu A, Roth RK,
Yang SP, Yeh RF, Collins F, Guyer MS, Peterson well T, Turner R, Venter E, Wang M, Wen M, Wu Rourke NJ, Ruediger ST, Rusman E, Sanches-Kui-
J, Felsenfeld A, Wetterstrand KA, Patrinos A, D, Wu M, Xia A, Zandieh A, and Zhu X: The per RM, Schenker MR, Seoane JM, Shaw RJ,
Morgan MJ, de Jong P, Catanese JJ, Osoegawa sequence of the human genome. Science 2001; Shiver MK, Short SW, Sizto NL, Sluis JP, Smith
K, Shizuya H, Choi S, Chen YJ; International 291: 1304. MA, Ernest Sohna Sohna J, Spence EJ, Stevens K,
Human Genome Sequencing Consortium: Initial Sutton N, Szajkowski L, Tregidgo CL, Turcatti G,
sequencing and analysis of the human genome. 3. Margulies M, Egholm M, Altman WE, Attiya S, Vandevondele S, Verhovsky Y, Virk SM, Wakelin S,
Nature 2001; 409: 860. Bader JS, Bemben LA, Berka J, Braverman MS, Walcott GC, Wang J, Worsley GJ, Yan J, Yau L,
 498 BUNNIK AND LE ROCH

Zuerlein M, Rogers J, Mullikin JC, Hurles ME, Trulson M, Vieceli J, Wegener J, Wu D, Yang A, 18. Fields S and Song O: A novel genetic system to
McCooke NJ, West JS, Oaks FL, Lundberg PL, Zaccarin D, Zhao P, Zhong F, Korlach J, and detect protein-protein interactions. Nature 1989;
Klenerman D, Durbin R, and Smith AJ: Accurate Turner S: Real-time DNA sequencing from single 340: 245.
whole human genome sequencing using reversible polymerase molecules. Science 2009; 323: 133.
terminator chemistry. Nature 2008; 456: 53. 19. Phizicky E, Bastiaens PI, Zhu H, Snyder M, and
10. Yates JR, Ruse CI, and Nakorchevsky A: Pro- Fields S: Protein analysis on a proteomic scale.
6. Rothberg JM, Hinz W, Rearick TM, Schultz J, teomics by mass spectrometry: approaches, ad- Nature 2003; 422: 208.
Mileski W, Davey M, Leamon JH, Johnson K, vances, and applications. Annu Rev Biomed Eng
Milgrew MJ, Edwards M, Hoon J, Simons JF, 2009; 11: 49. 20. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L,
Marran D, Myers JW, Davidson JF, Branting A, Adams SL, Millar A, Taylor P, Bennett K, Boutilier
11. Wold B and Myers RM: Sequence census meth-
Nobile JR, Puc BP, Light D, Clark TA, Huber M, K, Yang L, Wolting C, Donaldson I, Schandorff S,
ods for functional genomics. Nat Methods 2008;
Branciforte JT, Stoner IB, Cawley SE, Lyons M, Fu Shewnarane J, Vo M, Taggart J, Goudreault M,
5: 19.
Y, Homer N, Sedova M, Miao X, Reed B, Sabina J, Muskat B, Alfarano C, Dewar D, Lin Z, Micha-
Feierstein E, Schorn M, Alanjary M, Dimalanta E, 12. Rhee HS, and Pugh BF: Comprehensive genome- lickova K, Willems AR, Sassi H, Nielsen PA,
Dressman D, Kasinskas R, Sokolsky T, Fidanza JA, wide protein-DNA interactions detected at single- Rasmussen KJ, Andersen JR, Johansen LE, Han-
Namsaraev E, McKernan KJ, Williams A, Roth GT, nucleotide resolution. Cell 2011; 147: 1408. sen LH, Jespersen H, Podtelejnikov A, Nielsen E,
and Bustillo J: An integrated semiconductor de- Crawford J, Poulsen V, Sørensen BD, Matthiesen
13. Brogaard K, Xi L, Wang JP, and Widom J: A map
vice enabling non-optical genome sequencing. J, Hendrickson RC, Gleeson F, Pawson T, Moran
of nucleosome positions in yeast at base-pair
Nature 2011; 475: 348. MF, Durocher D, Mann M, Hogue CW, Figeys D,
resolution. Nature 2012; 486: 496.
and Tyers M: Systematic identification of protein
7. Wetterstrand KA: DNA Sequencing Costs: Data
14. Velculescu VE, Zhang L, Vogelstein B, and Kinzler complexes in Saccharomyces cerevisiae by mass
from the NHGRI Genome Sequencing Program (GSP).
KW: Serial analysis of gene expression. Science spectrometry. Nature 2002; 415: 180.
Available at: www.genome.gov/sequencingcosts
1995; 270: 484.
(accessed June 1, 2012). 21. Gavin AC, Bösche M, Krause R, Grandi P, Mar-
15. Schena M, Shalon D, Davis RW, and Brown PO: zioch M, Bauer A, Schultz J, Rick JM, Michon AM,
8. Braslavsky I, Hebert B, Kartalov E, and Quake SR:
Quantitative monitoring of gene expression pat- Cruciat CM, Remor M, Höfert C, Schelder M,
Sequence information can be obtained from sin-
terns with a complementary DNA microarray. Brajenovic M, Ruffner H, Merino A, Klein K, Hudak
gle DNA molecules. Proc Natl Acad Sci USA 2003;
Science 1995; 270: 467. M, Dickson D, Rudi T, Gnau V, Bauch A, Bastuck
100: 3960.
16. Schena M, Shalon D, Heller R, Chai A, Brown S, Huhse B, Leutwein C, Heurtier MA, Copley RR,
9. Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, Edelmann A, Querfurth E, Rybin V, Drewes G,
PO, and Davis RW: Parallel human genome
Peluso P, Rank D, Baybayan P, Bettman B, Bibillo Raida M, Bouwmeester T, Bork P, Seraphin B,
analysis: microarray-based expression monitoring
A, Bjornson K, Chaudhuri B, Christians F, Cicero R, Kuster B, Neubauer G, and Superti-Furga G:
of 1000 genes. Proc Natl Acad Sci USA 1996; 93:
Clark S, Dalal R, Dewinter A, Dixon J, Foquet M, Functional organization of the yeast proteome by
10614.
Gaertner A, Hardenbol P, Heiner C, Hester K, systematic analysis of protein complexes. Nature
Holden D, Kearns G, Kong X, Kuse R, Lacroix Y, 17. Washburn MP, Wolters D, and Yates JR 3rd.: 2002; 415: 141.
Lin S, Lundquist P, Ma C, Marks P, Maxham M, Large-scale analysis of the yeast proteome by
Murphy D, Park I, Pham T, Phillips M, Roy J, Sebra multidimensional protein identification technol- 22. Ideker T and Krogan NJ: Differential network bi-
R, Shen G, Sorenson J, Tomaney A, Travers K, ogy. Nat Biotechnol 2001; 19: 242. ology. Mol Syst Biol 2012; 8: 565.

View publication stats