Sugarcane - Bioenergy Crop Plants - page 539

from 26 diverse cDNA libraries which were clustered into 43,000 transcripts or SAS (Sugarcane
Assembled Sequences).
The information generated in EST projects has been used in comparative mapping of the family
of grasses , using common markers that hybridize with sugarcane, rice, corn, hexaploid wheat, bar-
ley and sorghum. However, the molecular information developed to date for sugarcane is minimal
when compared to the information that is necessary to identify and characterize the loci that encode
the important agronomic characteristics. Table 21.2 lists the genetic maps available for sugarcane.
To date, approximately 400 genetic markers have been developed (Cordeiro et al. 2002; Pinto et al.
2006; Oliveira et al. 2009) and used in the construction of the first functional sugarcane genetic map
(Oliveira et al. 2008).
All of the EST collections were clustered by the Center for Genomic Research (TIGR) as
the Sugarcane Gene Index 2.1, and more recently by the Computational Biology and Functional
Genomics Laboratory at the Dana-Farber Cancer Institute as the Sugarcane Gene Index 2.2. In
the SUCEST-FUN database (http://sucest-fun.org) a tool is available where clusters generated by
the SUCEST collection and the Sugarcane Gene Index collection can be cross-referenced. The
SUCEST-FUN has been developed in the concept of the mediator approach that incorporates
concepts from Data Warehouse and Federation approaches. It is a flexible data integration that
assembles heterogeneous distributed data sources, experimental data, resources, the application of
scientific algorithms and computational analysis. Bioinformatics and the management of scientific
data are critical to the support of life sciences discoveries. Nowadays, an explosion of available
biological data and research has risen up, most of it compound and stored in dozens of smaller data-
bases. Scientists are not currently able to easily identify and integrate autonomous data sources and
exploit this information because of the variety of semantics, interfaces, and data formats used by the
underlying data sources. The SUCEST-FUN Database is, therefore, being developed to give access
to gene expression studies and make available tools that will allow a Systems Biology approach in
sugarcane and the identification of regulatory networks.
The first transcriptomics tools developed made use of existing cDNA clones to produce mac-
roarrays. Macroarrays have been used to define gene expression patterns in immature and mature
leaf, immature and mature internodes (Carson and Botha 2002; Carson et al. 2002), sugarcane
responses to cold (Nogueira et al. 2003), tissue profiling of transposable element transcripts
(Araujo et al. 2005), stem development (Watt et al. 2005), methyl jasmonate responses (De Rosa
Jr et al. 2005), the response of sugarcane leaves to ethanol application (Camargo et al. 2007),
sink-source activity alterations (McCormick et al. 2006, 2008) and ABA/MeJA-activation of sug-
arcane transcription factors (Schlogl et al. 2008). Data mining of the SUCEST database led to the
identification of 276 sequences homologous to TEs in 21 different families of which 54% corre-
spond to classical transposons and 46% to retrotransposons (Rossi et al. 2001). Retrotransposons
mobilize themselves through an RNA intermediate and thus are now considered one of the major
forces driving genome expansion in plants (Piegu et al. 2006) while transposons usually move
using either a cut/paste or a copy/paste mechanism. Expression profiling of 162 clones (Araujo et
al. 2005) showed that callus was the tissue with most expressed TE families. Although it has been
proposed several times that tissue culture somaclonal variation could be a result of TE activity,
this was the first report that demonstrated that callus is indeed a tissue where different TEs are
expressed at the same time, not necessarily in the same cell. One largely unanticipated result was
the revelation that within a family there are lineages with varying copy number in the genome
(Rossi et al. 2004; Saccaro-Junior et al. 2007). Adding to that, some of the transposon lineages
were associated with previously described “domesticated” versions of a transposase (Bundock
and Hooykaas 2005; Cowan et al. 2005). Regardless of the mechanism by which a transposable
element moves, the field of genetic mobile elements is now flourishing with hypotheses of their
impact on genome structure, gene regulation and even function leaving their once considered
“junk DNA” status as a secondary role (Casacuberta and Santiago 2003; Kashkush et al. 2003;
Bundock and Hooykaas 2005).