Bioinformatic Tools in Crop Improvement - Crop Improvement: New Approaches and Modern Techniques - page 59

Gramene
A database for plant comparative genomics providing gene information and some
genetic linkage maps for 29 monocotyledon (grass) species, using some of the more
important and more commonly used genetic markers detailed above (Liang et al.
2008 ; Ware 2007 ).
GeneticDiversityandPopulationGeneticsAnalysis
Comprehensive discussion of the genetic and statistical analyses employed in popu-
lation genetics is beyond the scope of this review, but I refer you to the following
books and reviews (Clark and Gorley 2001 ; Conte et al. 2008 ; Wall et al. 2008 ;
Barnholtz-Sloan and Tiwari 2009 ; Pu et al. 2009). Population gene family data are
usually produced by computational procedures including a first step that conducts
an all-against-all sequence similarity analysis or matrix, and then a second step in
building clusters of inter- and intra- population analysis parameters, by methods
such as Markov Clustering (MCL), multi dimensional scaling (MDS), and principle
component analysis (PPO); using programs like PRIMER and Arlequin (Table 2.1 ).
Advanced software statistics (GenAlEx—Table 2.2 ) can yield indices and informa-
tion that are useful for further statistical analysis and phylogenetic studies using
Analysis of Co-Variance (ANCOVA), Analysis of Similarity and Analysis of Vari-
ance (ANOVA). Listed in Table 2.2 are a number of important web-based sites for
population genetics analysis and computation; and for ease their purpose and URL
are also presented there.
RNA Variation Resources
ESTandcDNA
Expressed Sequence Tag (ESTs) are determined by partial sequencing of randomly
picked gene transcripts that have originated from isolated RNA and converted to
cDNA (Adams et al. 1993 ). Since cDNA and EST collections can be easily gener-
ated regardless of chromosome and gene complexity, this method has been applied
not only to model plants, but also to a number of crop species with large genomes;
due mainly to polyploidy and/or to the number of repetitive sequences. Because
EST data collected from cDNA libraries of an organism consists of redundant se-
quences from the same gene locus or RNA target, it is often necessary to perform
EST grouping by metabolic and/or functional units. Then these groups are further
consolidated into alignment sequences for each transcript before further analysis
(Ewing et al. 1998 ; Huang and Madan 1999 ; Masoudi-Nejad et al. 2006 ). The