Jim Leebens-Mack

Douglas E. Soltis

Pamela S. Soltis

Plant Reproductive Genomics at the Plant and Animal Genome Conference

A major challenge in the post-genomic era is the development of experimental
approaches to monitor the properties of proteins on a proteome-wide level. It would
be particularly useful to systematically assay protein subcellular localization, post-translational
modifications and protein&#x2013;protein interactions, both at steady state and
in response to environmental stimuli. Development of new reagents and methods
will enhance our ability to do so efficiently and systematically. Here we describe
the construction of two collections of budding yeast strains that facilitate proteome-wide
measurements of protein properties. These collections consist of strains with
an epitope tag integrated at the C-terminus of essentially every open reading frame
(ORF), one with the tandem affinity purification (TAP) tag, and one with the green
fluorescent protein (GFP) tag. We show that in both of these collections we have
accurately tagged a high proportion of all ORFs (approximately 75&#x25; of the proteome)
by confirming expression of the fusion proteins. Furthermore, we demonstrate
the use of the TAP collection in performing high-throughput immunoprecipitation
experiments. Building on these collections and the methods described in this paper,
we hope that the yeast community will expand both the quantity and type of proteome
level data available.

Russell Howson

Won-Ki Huh

Sina Ghaemmaghami

James V. Falvo

Kiowa Bower

Archana Belle

Noah Dephoure

Dennis D. Wykoff

Jonathan S. Weissman

Erin K. O&#x27;Shea

Construction, Verification and Experimental Use of Two Epitope-Tagged
                                 Collections of Budding Yeast Strains

The genome of tomato (Solanum lycopersicum) is being sequenced by an international
consortium of 10 countries (Korea, China, the United Kingdom, India, The
Netherlands, France, Japan, Spain, Italy and the United States) as part of a larger initiative
called the &#x2018;International Solanaceae Genome Project (SOL): Systems Approach
to Diversity and Adaptation&#x2019;. The goal of this grassroots initiative, launched in
November 2003, is to establish a network of information, resources and scientists
to ultimately tackle two of the most significant questions in plant biology and agriculture:
(1) How can a common set of genes/proteins give rise to a wide range of
morphologically and ecologically distinct organisms that occupy our planet&#x3F; (2) How
can a deeper understanding of the genetic basis of plant diversity be harnessed to
better meet the needs of society in an environmentally friendly and sustainable manner&#x3F;
The Solanaceae and closely related species such as coffee, which are included
in the scope of the SOL project, are ideally suited to address both of these questions.
The first step of the SOL project is to use an ordered BAC approach to generate a
high quality sequence for the euchromatic portions of the tomato as a reference for
the Solanaceae. Due to the high level of macro and micro-synteny in the Solanaceae
the BAC-by-BAC tomato sequence will form the framework for shotgun sequencing
of other species. The starting point for sequencing the genome is BACs anchored
to the genetic map by overgo hybridization and AFLP technology. The overgos are
derived from approximately 1500 markers from the tomato high density F2-2000
genetic map (http://sgn.cornell.edu/). These seed BACs will be used as anchors from
which to radiate the tiling path using BAC end sequence data. Annotation will be
performed according to SOL project guidelines. All the information generated under
the SOL umbrella will be made available in a comprehensive website. The information
will be interlinked with the ultimate goal that the comparative biology of the
Solanaceae&#x2014;and beyond&#x2014;achieves a context that will facilitate a systems biology
approach.

Lukas A. Mueller

Steven D. Tanksley

Jim J. Giovannoni

Joyce van Eck

Stephen Stack

Doil Choi

Byung Dong Kim

Mingsheng Chen

Zhukuan Cheng

Chuanyou Li

Hongqing Ling

Yongbiao Xue

Graham Seymour

Gerard Bishop

Glenn Bryan

Rameshwar Sharma

Jiten Khurana

Akhilesh Tyagi

Debasis Chattopadhyay

Nagendra K. Singh

Willem Stiekema

P. Lindhout

Taco Jesse

Rene Klein Lankhorst

Mondher Bouzayen

Daisuke Shibata

Satoshi Tabata

Antonio Granell

Miguel A. Botella

Giovanni Giuliano

Luigi Frusciante

Mathilde Causse

Dani Zamir

The Tomato Sequencing Project, the First Cornerstone of the International Solanaceae Project &#x28;SOL&#x29;

Recent advances, such as the availability of extensive genome survey sequence (GSS)
data and draft physical maps, are radically transforming the means by which we
can dissect Brassica genome structure and systematically relate it to the Arabidopsis
model. Hitherto, our view of the co-linearities between these closely related genomes
had been largely inferred from comparative RFLP data, necessitating substantial
interpolation and expert interpretation. Sequencing of the Brassica rapa genome
by the Multinational Brassica Genome Project will, however, enable an entirely
computational approach to this problem. Meanwhile we have been developing
databases and bioinformatics tools to support our work in Brassica comparative
genomics, including a recently completed draft physical map of B. rapa integrated
with anchor probes derived from the Arabidopsis genome sequence. We are also
exploring new ways to display the emerging Brassica&#x2013;Arabidopsis sequence homology
data. We have mapped all publicly available Brassica sequences in silico to the
Arabidopsis TIGR v5 genome sequence and published this in the ATIDB database
that uses Generic Genome Browser (GBrowse). This in silico approach potentially
identifies all paralogous sequences and so we colour-code the significance of the
mappings and offer an integrated, real-time multiple alignment tool to partition them
into paralogous groups. The MySQL database driving GBrowse can also be directly
interrogated, using the powerful API offered by the Perl Bio&#x2237;DB&#x2237;GFF methods,
facilitating a wide range of data-mining possibilities.

Paul Beckett

Ian Bancroft

Martin Trick

Computational Tools for Brassica&#x2013;Arabidopsis Comparative Genomics

A complete genome sequence provides unlimited information in the sequenced organism
as well as in related taxa. According to the guidance of the Multinational Brassica
Genome Project (MBGP), the Korea Brassica Genome Project (KBGP) is sequencing
chromosome 1 (cytogenetically oriented chromosome #1) of Brassica rapa. We
have selected 48 seed BACs on chromosome 1 using EST genetic markers and FISH
analyses. Among them, 30 BAC clones have been sequenced and 18 are on the way.
Comparative genome analyses of the EST sequences and sequenced BAC clones from
Brassica chromosome 1 revealed their homeologous partner regions on the Arabidopsis
genome and a syntenic comparative map between Brassica chromosome 1 and
Arabidopsis chromosomes. In silico chromosome walking and clone validation have
been successfully applied to extending sequence contigs based on the comparative
map and BAC end sequences. In addition, we have defined the (peri)centromeric
heterochromatin blocks with centromeric tandem repeats, rDNA and centromeric
retrotransposons. In-depth sequence analyses of five homeologous BAC clones and
an Arabidopsis chromosomal region reveal overall co-linearity, with 82&#x25; sequence
similarity. The data indicate that the Brassica genome has undergone triplication and
subsequent gene losses after the divergence of Arabidopsis and Brassica. Based on in-depth
comparative genome analyses, we propose a comparative genomics approach
for conquering the Brassica genome. In 2005 we intend to construct an integrated
physical map, including sequence information from 500 BAC clones and integration
of fingerprinting data and end sequence data of more than 100 000 BAC clones.
The sequences have been submitted to GenBank with accession numbers: 10 204
BAC ends of the KBrH library (CW978640&#x2013;CW988843); KBrH138P04, AC155338;
KBrH117N09, AC155337; KBrH097M21, AC155348; KBrH093K03, AC155347;
KBrH081N08, AC155346; KBrH080L24, AC155345; KBrH077A05, AC155343;
KBrH020D15, AC155340; KBrH015H17, AC155339; KBrH001H24, AC155335;
KBrH080A08, AC155344; KBrH004D11, AC155341; KBrH117M18, AC146875;
KBrH052O08, AC155342.

Tae-Jin Yang

Jung-Sun Kim

Ki-Byung Lim

Soo-Jin Kwon

Jin-A Kim

Mina Jin

Jee Young Park

Myung-Ho Lim

Ho-Il Kim

Seog Hyung Kim

Yong Pyo Lim

Beom-Seok Park

The Korea Brassica Genome Project: a Glimpse of the Brassica Genome Based on Comparative Genome Analysis With Arabidopsis

J. S. Almeida

D. J. McKillen

Y. A. Chen

P. S. Gross

R. W. Chapman

G. Warr

Design and Calibration of Microarrays as Universal Transcriptomic Environmental Biosensors

Gene expression microarray studies have led to interesting experimental designand statistical analysis challenges. The comparison of expression profiles acrosspopulations is one of the most common objectives of microarray experiments. Inthis manuscript we review some issues regarding design and statistical analysis fortwo-colour microarray platforms using mixed linear models, with special attentiondirected towards the different hierarchical levels of replication and the consequenteffect on the use of appropriate error terms for comparing experimental groups.We examine the traditional analysis of variance (ANOVA) models proposed formicroarray data and their extensions to hierarchically replicated experiments. Inaddition, we discuss a mixed model methodology for power and efficiency calculationsof different microarray experimental designs.

Guilherme J. M. Rosa

Juan P. Steibel

Robert J. Tempelman

Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models

With microarray technology becoming more prevalent in recent years, it is nowcommon for several laboratories to employ the same microarray technology to identifydifferentially expressed genes that are related to the same phenomenon in the samespecies. Although experimental specifics may be similar, each laboratory will typicallyproduce a slightly different list of statistically significant genes, which calls intoquestion the validity of each gene list (i.e. which list is best). A statistically-basedmeta-analytic approach to microarray analysis systematically combines results fromthe different laboratories to provide a single estimate of the degree of differentialexpression for each gene. This approach provides a more precise view of genesthat are of significant interest, while simultaneously allowing for differences betweenlaboratories. The widely-used Affymetrix oligonucleotide array and its software areof particular interest because the results are naturally suited to a meta-analysis.A simulation model based on the Affymetrix platform is developed to examine theadaptive nature of the meta-analytic approach and to illustrate the utility of suchan approach in combining microarray results across laboratories.

John R. Stevens

R. W. Doerge

Meta-Analysis Combines Affymetrix Microarray Results Across Laboratories

Recent years have seen a huge increase in the amount of biomedical information
that is available in electronic format. Consequently, for biomedical researchers
wishing to relate their experimental results to relevant data lurking somewhere within
this expanding universe of on-line information, the ability to access and navigate
biomedical information sources in an efficient manner has become increasingly
important. Natural language and text processing techniques can facilitate this task
by making the information contained in textual resources such as MEDLINE
more readily accessible and amenable to computational processing. Names of
biological entities such as genes and proteins provide critical links between different
biomedical information sources and researchers' experimental data. Therefore,
automatic identification and classification of these terms in text is an essential
capability of any natural language processing system aimed at managing the wealth
of biomedical information that is available electronically. To support term recognition
in the biomedical domain, we have developed Termino, a large-scale terminological
resource for text processing applications, which has two main components: first, a
database into which very large numbers of terms can be loaded from resources such
as UMLS, and stored together with various kinds of relevant information; second,
a finite state recognizer, for fast and efficient identification and mark-up of terms
within text. Since many biomedical applications require this functionality, we have
made Termino available to the community as a web service, which allows for its
integration into larger applications as a remotely located component, accessed through
a standardized interface over the web.

Henk Harkema

Ian Roberts

Rob Gaizauskas

Mark Hepple

A Web Service for Biomedical Term Look-Up

About this journal

International Journal of Genomics publishes papers in all areas of genome-scale analysis, including bioinformatics, clinical and disease genomics, epigenomics, evolutionary and functional genomics, genome engineering, and synthetic genomics.

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International Journal of Genomics - Table of contents 2005

International Journal of Genomics | Hindawi

title

International Journal of Genomics publishes papers in all areas of genome-scale analysis, including bioinformatics, clinical and disease genomics, epigenomics, evolutionary and functional genomics, genome engineering, and synthetic genomics.

International Journal of Genomics