Research Interests
The Barbazuk lab
uses computational,
comparative and functional genomics to study genome architecture,
function and
evolution. In order to address these questions, genome sequences and
well-established catalogues of genes and their positions within the
genome are
required. As more and more genome data becomes available, the methods
used to
catalogue genes become more robust, and ultimately the questions of
genome
organization, gene interaction and regulation can be addressed. Our lab
works
with crop (particularly maize and tomato), model (Arabidopsis) and
non-model
genomes where we apply comparative analysis and computational methods
to
investigate gene structure, gene content, gene/genome organization and
regulation.
We are currently
examining:
Next
generation sequencing of transcriptome and genome
sequence data: We
are actively using
next generation sequence data from multiple platforms to examine and
characterize genomes and transcriptomes. We currently have projects
that apply next generation
sequence
technologies to investigate gene content, gene structure, expression,
gene
gain/loss, genome architecture and sequence diversity.
Gene Annotation and gene structure: Plant
genome
sequence is the
knowledge infrastructure for the next generation of plant molecular
genetics
and crop improvement, and will provide the foundation for crop
improvement. The
products of ongoing and future plant sequencing projects are
collections of
large contiguous nucleotide segments for which there is no a priori
knowledge
of content or function. Therefore, high throughput
computational tools
that can accurately identify genes within genomic sequence are
absolutely
necessary for annotating and understanding the maize genome.
In collaboration
with Dr. Michael Brent at Washington University,
are improving gene prediction in
maize and
tomato by identifying a comprehensive "training set" of complete and
annotated gene models; and, using these to optimize TWINSCAN. TWINSCAN
is a
next-generation gene discovery tool developed by Michael
Brent.
Originally designed for human gene prediction, it improves gene
detection by
integrating traditional probability models like those underlying
GENSCAN and
FGENESH with information from the alignments between two genomes.
Alternative
Splicing: Alternative
splicing (AS) creates multiple mRNA transcripts from a single gene.
While AS is known to
contribute to gene
regulation and proteome diversity in animals, the study of its
importance in
plants is in its early stages. However,
recently available plant genome and transcript sequence data sets are
enabling
a global analysis of AS in many plant species. Results of genome
analysis have revealed differences
between animals and
plants in the frequency of alternative splicing. The proportion of
plant genes that have one
or more alternative transcript isoforms is approximately 20%,
indicating that
AS in plants is not rare, although this rate is ~1/3 of that observed
in
human. The majority
of plant AS events
have not been functionally characterized, but evidence suggests that AS
participates in important plant functions including stress response,
and may
impact domestication and trait selection. The increasing availability
of plant genome sequence data
is enabling
larger comparative analyses that will identify functionally important
plant AS events
based on their evolutionary conservation;
determine the influence of genome duplication on the evolution of AS;
and
discover plant specific cis
elements
that regulate AS.
Representative Publications
Yan Fu, Oliver
Bannach, Hao Chen,
Jan-Hendrik Teune, Axel Schmitz, Gerhard Steger, Liming Xiong, and W. Brad Barbazuk .
Alternative Splicing of
Anciently Exonized 5S
rRNA Regulates Plant Transcription Factor TFIIIA. Genome
Research 2009 19:913-21.
W.
Brad Barbazuk, Yan Fu, Karen
M. McGinnis.
Genome-wide analyses of alternative splicing in plants: Opportunities
and
Challenges. Genome Research 2008 18:1381-92.
Subramanian S, Fu
Y, Sunkar R, Barbazuk BW, Zhu JK,
Yu O. Novel and nodulation-regulated microRNAs
in soybean roots. BMC Genomics.
2008
9:160
W.
Brad Barbazuk, Scott J.
Emrich*, Hsin D. Chen, Li
Li, and Patrick S. Schnable. SNP discovery in maize via 454
transcriptome
sequencing.Plant
J. 2007 51:910-18
Scott J. Emrich, W.
Brad Barbazuk, Li Li and Patrick S. Schnable. Gene discovery
and annotation using LCM-454
transcriptome sequencing. Genome Research 2007
17:69-73.
Epub 2006 Nov 9.
Barbazuk,
W. B,
Bedell, J. A and Pablo D.
Rabinowicz
Reduced
representation sequencing: a success in
maize and a promise for other plant genomes.BioEssays
2005;
27:839-848