Abstract

In past 5 years, the promise that came with genome sequencing has revolutionized the functional genomics research field at unprecedented manner. It would soon know what all known genes do, particularly genes involved in genetic improvement of animal health and increase food animal production. With the availability of full bovine genomic sequence, yet we still have a lot of daunting tasks on ‘genotype-to-phenotype problem’ particularly about the phenotypic variations and trying to predict what genes are likely to be involved, and improved integrated interactive database. This article outlined and discussed about the current status of bovine functional genomics, recent development in bovine genome databases particularly in annotation of bovine genome, bovine quantitative trait loci database and its potential impact to unveil the from genotype-to-phenotype problem.

INTRODUCTION

The functional status of bovine genome is determined by the physiological response to hormones, nutrients, infections and other important developmental stages specific physiological stimuli to make the proteins. This genomic response of relevant cells to such stimuli underlies an animal’s overall physiology and significantly influences economically important traits (phenotypes) of interest in dairy cattle, e.g. growth and development, beef and milk production, disease resistance, feed intake and reproductive efficiency. Bovine functional genomics is the recently developed science that is providing scientists the ability to study large numbers of genes that control important economic traits and physiological functions. So now, scientists can see what genes are expressed or not expressed as a result of a treatment or physiological condition. In recent technological development, gene expression levels have made it possible to conceive of phenotypic traits in term of many of the sets of genes being linked to specific pathways or functions [1].

In cattle, traditional breeding and selection programs are primarily based on marker assisted selection (MAS) and gene assisted selection (GAS) [2], which is of great interest to cattle breeding industry through its use in the improvement of the desired traits that an animal breeder wishes to develop, like the meat properties of bullocks or the milk yield of cows. Most of the traits of biological and economical interest are continuous and are often given a quantitative value, which served important goals for cattle breeding programs. The predictions of these quantitative values of economically important traits are made on the basis of genotypic database [single nucleotide polymorphism (SNP) markers] and phenotypic database [the estimated breeding value (EBV) of quantitative trait]. The EBV is the genetic merit of an animal’s breeding value, calculated based on the prediction of the traits. Therefore, the selection on quantitative traits of economic importance, provide an estimate the collective effects of all genes that affect the phenotypic trait, without knowing where the genes that control the trait are located in the genome or what their individual effects are. This quantitative genetic approach to selection has been effective for many traits. It is essentially this quantitative trait loci (QTL) approach, in which regions in the genome that contain genes that affect the trait and/or using molecular markers that are linked to the quantitative trait nucleotides (QTN) were identified [3]. In most cases, however, the actual location, identity and functional role of the QTN remain unknown. In cattle, examples where the causative gene for the QTN has been identified are limited, e.g. diacylglycerol O-acyltransferase 1 (DGAT1) [4] and ATP binding cassette sub family G member 2 (ABCG2) [5] for milk production, myostatin for beef production [6] and haplotypes of MHC DQ genes involved in immune response against susceptibility to bovine mastitis [7]. Although, markers that are linked to QTN can be used to enhance genetic progress through MAS and GAS, there are still limitations to such selection. In addition, the ability to identify QTN is limited for traits that are difficult or expensive to record. However, the advent of bovine functional genomics tools has enhanced the discovery of genes that control phenotypic traits of importance and its implementation toward enhanced livestock selection programs, management programs and the integration of selection and management programs [8].

CURRENT STATUS OF BOVINE FUNCTIONAL GENOMICS

The current version of cattle genome was sequenced to 7.1× coverage, which contains a minimum of 22 000 genes, with a core set of 14 345 orthologs shared among 7 mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes [9]. The bovine assembly process consisted of multiple phases including both BAC generated sequence and whole genome shotgun (WGS) reads, as well as in combination with the individual overlapping WGS reads. Now, over 90% bovine genome assembly was placed on chromosomes using the available map information, with the estimated genome size is 2.87 Gb, which represents a high degree of completeness, with 95% of the available expression sequence tags (EST) sequences found in assembled contigs. The assembly contigs and scaffolds align linearly to the finished bacterial artificial chromosomes (BACs), and the accuracy of the assembly has been confirmed by >99.2% were correctly positioned genotyping and genetic mapping of 17 482 SNPs within the latest Btau_4.0 assembly [10].

In past years, a great deal of progress has been made in the development of functional genomics tools, especially in context to whole genome sequencing. The recently 7.1× genome assembly of a bovine genome (Hereford) produced by the Baylor College of Medicine Human Genome Sequencing Centre (BCM-HGSC) has now allowed years of work in cattle QTL mapping to be associated with genes and other genomic features [10]. Since the release of first draft bovine genome sequencing [11] advanced developmental tools of next-generation genome sequencing (NGS) platforms [12] has recently introduced in functional genomics researches [13, 14]. In last 5 years, these NGS platforms have shown great impact in mammalian functional genomics researches because of its potential applications in whole genome sequencing [15], whole genome variation [16–18], epigenetic [19–21], transcriptome sequencing (RNA seq) [22–25], resequencing and de novo sequencing [26], chromatin immunoprecipitation sequencing (ChIP-Seq) and with DNA microarray (ChIP-chip) [27–29] and other customized applications. It is expected that the potential impact of NGS technologies will take its pace soon in bovine functional genomics in coming years [30].

Presently, second high-throughout (HT)-NGS platform [31–33] and third HT-NGS platform [34–37] and fourth HT-NGS (http://www.iontorrent.com/: Ion Torrent semiconductor sequencing technology) platform are available in market. The availability of these emerging HT-NGS technologies together with advances in gene expression technologies and bioinformatics, have revolutionized the functional genomics research field at unprecedented manner and providing a fresh impetus for the investigation of complex biological problems, such as from genotypes to phenotype and back (forward and reverse genetic: Figure 1).

Figure 1:

Central dogma of forward and reverse genetics in bovine functional genomics.

Figure 1:

Central dogma of forward and reverse genetics in bovine functional genomics.

Traditionally, the application of bovine genomics to biology is usually evolving from the discovery of genetic determinants (structural genomics) to biological signature (functional genomics), which involves typical pattern of gene expression, or even a typical profile of proteins or metabolites characterizing a specific physiological or pathological state. Now the challenge for biological research is to integrate the structural and functional genomics [38, 39], as well as to associate genotype data from the different ‘-omic’ sciences to phenotypic data [40]. This process is facilitated by rapid developments in computational biology and bioinformatics fields [41], which followed the rapid expansion in bovine functional genomic research. In past decade, substantial progress has been made in bovine functional genome, particularly on genomics selection based on QTN of cattle economic traits to gain knowledge and help to develop future improvements in health, reproduction and production [42], yet we still do not know the functional annotation of a large number of the bovine genes, and cannot predict with any accuracy what the effect will be of modifying the activity of an uncharacterized gene, even when it has been assigned to a functional annotation class. Equally daunting task is starting with a phenotypic variant and trying to predict what genes are likely to be involved. The problem is complicated by the fact that most phenotypes of human or animal genomes are complex i.e. polygenic in nature, which are in addition to environmental factors, contributes to expression of the phenotype.

BOVINE GENOME DATABASE

Principally, the selection of bovine genome database (BGD) is mainly dealt with physical maps, genetic maps, QTL maps, SNP maps, EST maps, BovMap comparison, Human/Bovine Comparison, literature mining and genome annotation editors (Table 1). The principle goal of BGD is to support the Bovine Genome Sequencing and Analysis Consortium (BGSAC) by providing the annotation analytic tools to implement and integrate the components of bovine genome the annotation system [43]. In a recent development, the latest version of BGD [44] has been upgraded with additional features of genome browsers (GBrowse), the Apollo annotation editor, a QTL viewer, BLAST databases and gene pages. The updated features of GBrowsers, available for both scaffold and chromosome coordinate systems, display the bovine Official Gene Set version 2 (OGSv2) representing 23 633 gene models, 3164 of which were manually annotated. In addition to gene models and associated functional annotations, BGD also included RefSeq [45] and Ensembl gene models [46], non-coding RNA, repeats, pseudo-genes, protein homolog alignments, complementary DNA (cDNA) alignments, DNA repeats, SNPs, microsatellite markers QTL intervals and the Bovine QTL viewer for the identification of candidate genes underlying QTL. The introduced Apollo annotation editor was especially designed to illustrate the use of BGD for gene annotation and for mining the bovine genome. The components of Apollo annotation editor were composed of BLAST site, multiple genome browsers, an annotation portal and the Apollo Annotation Editor configured to connect directly to Chado database [43]. The key role of this gene ontology terms is to provide a straightforward link from gene coordinates to phenotype (Figure 1).

Table 1:

Bovine genome database web resources

Name Description Location 
Bovine physical maps Representing BAC fingerprint map and internet contig explorer http://www.bcgsc.ca/lab/mapping/bovine 
BAC library contig project http://www.livestockgenomics.csiro.au/WebFPC/bovinemap/ 
COMRAD: comparative radiation hybrid mapping at Roslin Institute http://www.projects.roslin.ac.uk/comrad/introduction.html 
University of Illinois/Texas A&M (ILTX) Radiation Hybrid Map http://cagst.animal.uiuc.edu/RHmap2004/index.html 
Bovine genetic maps BOVMAP (INRA, France)—marker lists, maps, breed polymorphisms, chromosome homology, QTL lists, homologous genes and literature associated with all Data http://locus.jouy.inra.fr/cgi-bin/bovmap/intro.pl/ 
Cattle genome database, CSIRO division of tropical agriculture—CGD map and markers, searchable QTL, comparisons of mapped loci to human, mouse, rat and pigs http://www.cgd.csiro.au/ 
MARC database—MARC map and markers http://www.marc.usda.gov/genome/genome.html 
University of Illinois Reference/Resource Families (IRRF) Map http://cagst.animal.uiuc.edu/genemap/irrf.html 
BovMap comparison ARKdb, Roslin institute—maps and markers for genetic, physical and cytogenetic maps, including cross-species comparisons http://www.thearkdb.org/browser?species=cow 
Comparative location database (compLDB), University of Sydney—comparative map viewer (CMAP)—all publically available maps and an integrated map http://medvet.angis.org.au/cmap/ 
NCBI map viewer—MARC and ILTX RH maps displayed side-by-side. http://www.ncbi.nlm.nih.gov/genome/guide/cow/ 
Bovine QTL servers Bovine QTL viewer developed at Texas A&M http://bovineqtl.tamu.edu 
Dairy cattle QTL map database, University of Sidney http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/ 
Bovine EST projects Cattle EST gene family database: Texas A&M—EST assemblies grouped with homologous human protein families and associated gene ontologies http://racerx00.tamu.edu 
Michigan State University, center for animal functional genomics and national bovine functional genomics consortium—Precomputed UniProt hits to ESTs, Blast server, EST clusters, clone lists for microarrays, primer lists for real time PCR http://gowhite.ans.msu.edu/public_php/showPage.php 
University of Illinois cattle EST sequencing project—EST clusters, precomputed UniGene homologs (using BLASTN), GO annotation for EST assemblies (based on BLASTN to human UniGene) http://titan.biotec.uiuc.edu/cattle/cattle_project.htm 
TIGR gene indices www.tigr.org/tigrscripts/tgi/T_index.cgi?species=cattle 
Bovine SNP project Interactive bovine In-Silico SNP database, CSIRO livestock industries http://www.livestockgenomics.csiro.au/ibiss/ 
Human/Bovine comparison Virtual comparative map (VCMap) http://bioneos.com/VCMap/ 
University of Illinois cattle radiation hybrid-human sequence comparative map, BAC ends track on UCSC genome browser http://cagst.animal.uiuc.edu/RHmap2004/index.html 
University of Illinois cattle comparative genomics—downloadable COMPASS results http://cagst.animal.uiuc.edu/COMPASS/index.html 
Comparative Genomics Library (CGL) http://www.yandell-lab.org/cgI/ 
Interactive bovine In-Silico SNP database, CSIRO livestock industries http://www.livestockgenomics.csiro.au/ibiss/ 
BGD literature mining Online Mendelian Inheritance Animals (OMIA) http://www.angis.org.au/Databases/BIRX/omia/ 
Mendelian inheritance in cattle http://dga.jouy.inra.fr/lgbc/mic2000/ 
Crittendon genomics research references http://www.genome.iastate.edu/db/map_ref/refsrch.html 
Bovine genome annotation Apollo annotation editor http://bovinegenome.org/?q=annotator_login 
Ensembl genome browser http://pre.ensembl.org/Bos_taurus/ 
UCSC genome browser http://www.genome.ucsc.edu/ 
NCBI map viewer http://www.ncbi.nlm.nih.gov/genome/guide/cow/ 
The gene index (TGI) project http://biocomp.dfci.harvard.edu/tgi/ 
Name Description Location 
Bovine physical maps Representing BAC fingerprint map and internet contig explorer http://www.bcgsc.ca/lab/mapping/bovine 
BAC library contig project http://www.livestockgenomics.csiro.au/WebFPC/bovinemap/ 
COMRAD: comparative radiation hybrid mapping at Roslin Institute http://www.projects.roslin.ac.uk/comrad/introduction.html 
University of Illinois/Texas A&M (ILTX) Radiation Hybrid Map http://cagst.animal.uiuc.edu/RHmap2004/index.html 
Bovine genetic maps BOVMAP (INRA, France)—marker lists, maps, breed polymorphisms, chromosome homology, QTL lists, homologous genes and literature associated with all Data http://locus.jouy.inra.fr/cgi-bin/bovmap/intro.pl/ 
Cattle genome database, CSIRO division of tropical agriculture—CGD map and markers, searchable QTL, comparisons of mapped loci to human, mouse, rat and pigs http://www.cgd.csiro.au/ 
MARC database—MARC map and markers http://www.marc.usda.gov/genome/genome.html 
University of Illinois Reference/Resource Families (IRRF) Map http://cagst.animal.uiuc.edu/genemap/irrf.html 
BovMap comparison ARKdb, Roslin institute—maps and markers for genetic, physical and cytogenetic maps, including cross-species comparisons http://www.thearkdb.org/browser?species=cow 
Comparative location database (compLDB), University of Sydney—comparative map viewer (CMAP)—all publically available maps and an integrated map http://medvet.angis.org.au/cmap/ 
NCBI map viewer—MARC and ILTX RH maps displayed side-by-side. http://www.ncbi.nlm.nih.gov/genome/guide/cow/ 
Bovine QTL servers Bovine QTL viewer developed at Texas A&M http://bovineqtl.tamu.edu 
Dairy cattle QTL map database, University of Sidney http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/ 
Bovine EST projects Cattle EST gene family database: Texas A&M—EST assemblies grouped with homologous human protein families and associated gene ontologies http://racerx00.tamu.edu 
Michigan State University, center for animal functional genomics and national bovine functional genomics consortium—Precomputed UniProt hits to ESTs, Blast server, EST clusters, clone lists for microarrays, primer lists for real time PCR http://gowhite.ans.msu.edu/public_php/showPage.php 
University of Illinois cattle EST sequencing project—EST clusters, precomputed UniGene homologs (using BLASTN), GO annotation for EST assemblies (based on BLASTN to human UniGene) http://titan.biotec.uiuc.edu/cattle/cattle_project.htm 
TIGR gene indices www.tigr.org/tigrscripts/tgi/T_index.cgi?species=cattle 
Bovine SNP project Interactive bovine In-Silico SNP database, CSIRO livestock industries http://www.livestockgenomics.csiro.au/ibiss/ 
Human/Bovine comparison Virtual comparative map (VCMap) http://bioneos.com/VCMap/ 
University of Illinois cattle radiation hybrid-human sequence comparative map, BAC ends track on UCSC genome browser http://cagst.animal.uiuc.edu/RHmap2004/index.html 
University of Illinois cattle comparative genomics—downloadable COMPASS results http://cagst.animal.uiuc.edu/COMPASS/index.html 
Comparative Genomics Library (CGL) http://www.yandell-lab.org/cgI/ 
Interactive bovine In-Silico SNP database, CSIRO livestock industries http://www.livestockgenomics.csiro.au/ibiss/ 
BGD literature mining Online Mendelian Inheritance Animals (OMIA) http://www.angis.org.au/Databases/BIRX/omia/ 
Mendelian inheritance in cattle http://dga.jouy.inra.fr/lgbc/mic2000/ 
Crittendon genomics research references http://www.genome.iastate.edu/db/map_ref/refsrch.html 
Bovine genome annotation Apollo annotation editor http://bovinegenome.org/?q=annotator_login 
Ensembl genome browser http://pre.ensembl.org/Bos_taurus/ 
UCSC genome browser http://www.genome.ucsc.edu/ 
NCBI map viewer http://www.ncbi.nlm.nih.gov/genome/guide/cow/ 
The gene index (TGI) project http://biocomp.dfci.harvard.edu/tgi/ 

THE BOVINE QTL DATABASE

From a computer science perspective, the QTLdb makes use of relational database structure and ontology management methods, with tools that have been developed to create an interactive data repository to help curators and users via worldwide web. However, from a biology perspective, it is not only a powerful QTL data comparison, data mining and collaboration aid, but it also provides key links among existing genome databases in terms of genetic information flow between genotypes and phenotypes, which is useful for meta-analysis and network analysis in terms of functional genomics to system biology. The bovine QTL provides a way to associate segments of genome locations with quantitative traits, which represent the majority of economically important phenotypes of cattle. Since the first bovine genome scan experiment [47], large numbers of detected bovine QTLs were available and allow researchers to narrow down genomic regions and identify the genetic factors that contribute to trait variations [48]. However, there is a bottleneck between mapped QTL and gene discovery [49], which hampered to use this information in the genetic improvement of livestock. The current bovine QTL database is comprised of 4682 QTLs for 376 phenotypic traits based the 274 publications. The bovine QTL database includes GBrowse and map alignment of different types of structural genome features include SNP, microarray elements (Affymetrix expression data), RH markers, microsatellites, BAC/FPC, etc. and most recently the high density Illumina SNP chip data [50]. For the comparative views of bovine QTLs with other species, Virtual Comparative Map (VCMap) is available [51] to the bovine QTLdb in terms of assisting comparative genome information mining for promising QTL regions, using information from well-studied species such as mouse, rat or humans. In future initiatives, the bovine QTLdb will include new types of data, e.g. copy number variations, segmentation duplications, eQTL, genome wide SNP associations, etc. as they become available, in order to strengthen the links from genotypes to phenotypes and back (Figure 1).

CONCLUDING REMARKS

With the completion of bovine genome-sequencing project, increasingly sophisticated functional genomics tools are being developed with the long-term goal of understanding how the coordinated activity of functional genes gives rise to a complex phenotype and back. The combination of both forward and reverse genetics with recently developed bovine genome database is beginning to revolutionize the way, in which gene functions are studied in cattle. Furthermore, the emerging high-throughput sequencing technologies should also provide a means to analyse bovine gene functions—the phenome—on a genomic scale.

Key Points

  • Bovine genome database is an integrated database for the bovine genome, which support the efforts of the bovine genome sequencing and analysis consortium that integrates bovine genomics data with structural and functional annotations of genes and the genome.

  • Genome assembly refers to the process of taking a large number of short DNA sequences, all of which were generated by a shotgun sequencing project, and putting them back together to create a representation of the original chromosomes, from which the DNA originated.

  • Genome annotation is the process of attaching biological information to sequence identifying elements on the genome (gene prediction) and attaching biological information to these elements.

  • GBrowsers is a graphical interface for display of information from a biological database for genomic data. It enable researchers to visualize and browse entire genomes with annotated data including gene prediction and structure, proteins, expression, regulation, variation, comparative analysis, etc.

  • Phenome is the set of all phenotypes expressed by a cell tissue organ organism or species. It represents the sum total of its phenotypic traits.

References

1
Nachtomy
O
Shavit
A
Yakhini
Z
Gene expression and the concept of the phenotype
Stud Hist Philos Biol Biomed Sci
 , 
2007
, vol. 
38
 (pg. 
238
-
54
)
2
Dekkers
JCM
Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons
Anim Sci
 , 
2004
, vol. 
82
 (pg. 
E313
-
28
)
3
Georges
M
Andersson
L
Positional identification of structural and regulatory quantitative trait nucleotides in domestic animal species
Cold Spring Harb Symp Quant Biol
 , 
2003
, vol. 
68
 (pg. 
179
-
87
)
4
Grisart
B
Coppieters
W
Farnir
F
, et al.  . 
Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition
Genome Res
 , 
2001
, vol. 
12
 (pg. 
222
-
31
)
5
Olsen
HG
Lien
S
Gautier
M
, et al.  . 
Mapping of a milk production QTL to a 420 kb region on bovine chromosome 6
Genetics
 , 
2005
, vol. 
169
 (pg. 
275
-
83
)
6
Grobet
L
Martin
LJ
Poncelet
D
, et al.  . 
A deletion in the bovine myostatin gene causes the double-muscled phenotype in cattle
Nat Genet
 , 
1997
, vol. 
17
 (pg. 
71
-
4
)
7
Park
YH
Joo
YS
Park
JY
, et al.  . 
Characterization of lymphocyte subpopulations and major histocompatibility complex haplotypes of mastitis-resistant and susceptible cows
J Vet Sci
 , 
2004
, vol. 
5
 (pg. 
29
-
39
)
8
Green
RD
Qureshi
MA
Long
JA
, et al.  . 
Identifying the future needs for long-term USDA efforts in agricultural animal genomics
Int J Biol Sci
 , 
2007
, vol. 
3
 (pg. 
185
-
91
)
9
Bovine Genome Sequencing and Analysis Consortium
Elsik
CG
Tellam
RL
Worley
KC
, et al.  . 
The genome sequence of taurine cattle: a window to ruminant biology and evolution
Science
 , 
2009
, vol. 
324
 (pg. 
522
-
8
)
10
Liu
Y
Qin
X
Henry Song
X
, et al.  . 
Bos taurus genome assembly
BMC Genomics
 , 
2009
, vol. 
10
 pg. 
180
 
11
Gibbs
R
Weinstock
G
Rohrer
G
, et al.  . 
Bovine genomic sequencing initiative Cattle-izing the human genome
Bovine Sequencing White Paper
 , 
2002
(pg. 
p. 1
-
12
12
Margulies
M
Egholm
M
Altman
WE
, et al.  . 
Genome sequencing in microfabricated high-density picolitre reactors
Nature
 , 
2005
, vol. 
437
 (pg. 
376
-
80
)
13
Morozova
O
Marra
MA
Applications of next-generation sequencing technologies in functional genomics
Genomics
 , 
2008
, vol. 
92
 (pg. 
255
-
64
)
14
Werner
T
Next generation sequencing in functional genomics
Brief Bioinform
 , 
2010
, vol. 
11
 (pg. 
499
-
511
)
15
Wheeler
DA
Srinivasan
M
Egholm
M
, et al.  . 
The complete genome of an individual by massively parallel DNA sequencing
Nature
 , 
2008
, vol. 
452
 (pg. 
872
-
6
)
16
Xi
R
Kim
TM
Park
PJ
Detecting structural variations in the human genome using next generation sequencing
Brief Funct Genomics
 , 
2010
, vol. 
9
 (pg. 
405
-
15
)
17
Henn
BM
Gravel
S
Moreno-Estrada
A
, et al.  . 
Fine-scale population structure and the era of next-generation sequencing
Hum Mol Genet
 , 
2010
, vol. 
19
 (pg. 
R221
-
6
)
18
Bowne
SJ
Sullivan
LS
Koboldt
DC
, et al.  . 
Identification of disease-causing mutations in autosomal dominant retinitis pigmentosa (adRP) using next-generation DNA sequencing
Invest Ophthalmol Vis Sci
 , 
2011
, vol. 
52
 (pg. 
494
-
503
)
19
Bormann
G
Chung
CA
Boyd
VL
, et al.  . 
Whole methylome analysis by ultra-deep sequencing using two-base encoding
PLoS One
 , 
2010
, vol. 
5
 pg. 
e9320
 
20
Fouse
SD
Nagarajan
RP
Costello
JF
Genome-scale DNA methylation analysis
Epigenomics
 , 
2010
, vol. 
2
 (pg. 
105
-
17
)
21
Bhaijee
F
Pepper
DJ
Pitman
KT
, et al.  . 
New developments in the molecular pathogenesis of head and neck tumors: a review of tumor-specific fusion oncogenes in mucoepidermoid carcinoma, adenoid cystic carcinoma and NUT midline carcinoma
Ann Diagn Pathol
 , 
2011
, vol. 
15
 (pg. 
69
-
77
)
22
Costa
V
Angelini
C
De Feis
I
, et al.  . 
Uncovering the complexity of transcriptomes with RNA-Seq
J Biomed Biotechnol
 , 
2010
, vol. 
2010
 pg. 
853916
 
23
Buermans
HP
Ariyurek
Y
van Ommen
G
, et al.  . 
New methods for next generation sequencing based microRNA expression profiling
BMC Genomics
 , 
2010
, vol. 
11
 pg. 
716
 
24
Nagalakshmi
U
Waern
K
Snyder
M
RNA-Seq: a method for comprehensive transcriptome analysis
Curr Protoc Mol Biol
 , 
2010
, vol. 
11
 (pg. 
1
-
13
)
25
Marguerat
S
Bähler
J
RNA-seq: from technology to biology
Cell Mol Life Sci
 , 
2010
, vol. 
67
 (pg. 
569
-
79
)
26
Li
R
Fan
W
Tian
G
, et al.  . 
The sequence and de novo assembly of the giant panda genome
Nature
 , 
2010
, vol. 
463
 (pg. 
311
-
7
)
27
Robertson
G
Hirst
M
Bainbridge
M
, et al.  . 
Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing
Nat Methods
 , 
2007
, vol. 
4
 (pg. 
651
-
7
)
28
Euskirchen
GM
Rozowsky
JS
Wei
CL
, et al.  . 
Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies
Genome Res
 , 
2007
, vol. 
17
 (pg. 
898
-
909
)
29
Popp
C
Dean
W
Feng
S
, et al.  . 
Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency
Nature
 , 
2010
, vol. 
463
 (pg. 
1101
-
5
)
30
Michelizzi
VN
Dodson
MV
Pan
Z
, et al.  . 
Water buffalo genome science comes of age
Int J Biol Sci
 , 
2010
, vol. 
6
 (pg. 
333
-
49
)
31
Mardis
ER
Next-generation DNA sequencing methods
Annu Rev Genomics Hum Genet
 , 
2008
, vol. 
9
 (pg. 
387
-
402
)
32
Mardis
ER
New strategies and emerging technologies for massively parallel sequencing: applications in medical research
Genome Med
 , 
2009
, vol. 
1
 pg. 
40
 
33
Metzker
ML
Sequencing technologies- the next generation
Nat Rev Genet
 , 
2010
, vol. 
1
 (pg. 
31
-
46
)
34
Greenleaf
WJ
Block
SM
Single-molecule, motion-based DNA sequencing using RNA polymerase
Science
 , 
2006
, vol. 
313
 pg. 
801
 
35
Harris
TD
Buzby
PR
Babcock
H
, et al.  . 
Single-molecule DNA sequencing of a viral genome
Science
 , 
2008
, vol. 
320
 (pg. 
106
-
9
)
36
Eid
J
Fehr
A
Gray
J
, et al.  . 
Real-time DNA sequencing from single polymerase molecules
Science
 , 
2009
, vol. 
323
 (pg. 
133
-
8
)
37
Rusk
N
Cheap third-generation sequencing
Nat Methods
 , 
2009
, vol. 
6
 (pg. 
244
-
5
)
38
Tuggle
CK
Dekkers
JCM
Reecy
JM
Integration of structural and functional genomics
Anim Genet
 , 
2006
, vol. 
37
 (pg. 
1
-
6
)
39
Wimmers
K
Murani
E
Ponsuksili
S
Functional genomics and genetical genomics approaches towards elucidating networks of genes affecting meat performance in pigs
Brief Functl Genomics
 , 
2010
, vol. 
9
 (pg. 
251
-
8
)
40
Hocquette
JF
Lehnerta
S
Barendse
J
, et al.  . 
Recent advances in cattle functional genomics and their application to beef quality
Animal
 , 
2007
, vol. 
1
 (pg. 
159
-
73
)
41
Horner
DS
Pavesi
G
Castrignanò
T
, et al.  . 
Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing
Brief Bioinform
 , 
2010
, vol. 
1
 (pg. 
181
-
97
)
42
Weller
JI
Ron
M
Invited review: quantitative trait nucleotide determination in the era of genomic selection
J Dairy Sci
 , 
2011
, vol. 
94
 (pg. 
1082
-
90
)
43
Reese
JT
Childers
CP
Sundaram
JP
, et al.  . 
Bovine Genome Database: supporting community annotation and analysis of the Bos taurus genome
BMC Genomics
 , 
2010
, vol. 
11
 pg. 
645
 
44
Childers
CP
Reese
JT
Sundaram
JP
, et al.  . 
Bovine Genome Database: integrated tools for genome annotation and discovery
Nucleic Acids Res
 , 
2011
, vol. 
l39
 (pg. 
D830
-
4
)
45
Pruit
KD
Tatusova
T
Klimke
W
, et al.  . 
NCBI Reference Sequences: current status, policy and new initiatives
Nucleic Acids Res
 , 
2009
, vol. 
37
 (pg. 
D32
-
6
)
46
Curwen
V
Eyras
E
Andrews
DT
, et al.  . 
The Ensembl automatic gene annotation system
Genome Res
 , 
2004
, vol. 
14
 (pg. 
942
-
50
)
47
Georges
M
Nielsen
D
Mackinnon
M
, et al.  . 
Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing
Genetics
 , 
1995
, vol. 
139
 (pg. 
907
-
20
)
48
Rothschild
MF
Hu
Z
Jiang
Z
Advances in QTL mapping in pigs
Int J Biol Sci
 , 
2007
, vol. 
3
 (pg. 
192
-
7
)
49
Womack
J.E
Advances in livestock genomics: opening the barn door
Genome Res
 , 
2005
, vol. 
12
 (pg. 
1699
-
705
)
50
Hu
Z
Fritz
ER
Reecy
JM
AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond
Nucleic Acids Res
 , 
2007
, vol. 
35
 (pg. 
D604
-
9
)
51
Kwitek
A
Davis
S
Shimoyama
M
, et al.  . 
Virtual Comparative Map (VCMap)
 
Proceedings of Plant & Animal Genomes XVIII Conference, January 9–13, 2010, Town & Country Convention Center, San Diego, CA