Agriculture Reference
In-Depth Information
Table 2.2
Summary of leguminous crops genome sequence information.
S. No.
Scientific name
Estimated
genome size
Sequencing strategy
Reference
1
Cajanus cajan
(L.) Millsp.
833.07 Mb
Scaffold/bacterial artificial chromosome (BAC)
Varshney
et al.,
2012
2
Cicer arietinum
L
.
740 Mb
Draft assembly, whole genome shotgun sequence
Varshney
et al.,
2013
3
Glycine max
(L.) Merr.
1115 Mb
Whole-genome shotgun approach integrated with
physical and high-density genetic maps
Schmutz
et al.,
2010
4
G. soja
Sieb. & Zucc.
~1100 Mbp
Whole-genome shotgun sequencing
Kim
et al.,
2010
5
Lotus japonicus
L.
472 Mb
Clone-by-clone sequencing and shotgun sequencing of
selected regions of the genome
Sato
et al.,
2008
6
Lupinus angustifolius
L.
1.153 Gb
Draft assembly from a whole-genome shotgun
sequencing
Yang
et al.,
2013
7
Medicago truncatula
Godr. & Gnem.
~454 to 526 Mbp
Euchromatin based on a recently completed
BAC-by-BAC sequencing approach
Young
et al.,
2011
8
Phaseolus vulgaris
(L.)
~650 Mb
V1.0, the first chromosome-scale version includes BAC
and fosmid end sequence whole-genome shotgun
(WGS) method
9
Vigna unguiculata
L.
(Walp.)
620 Mb
160 Mb completed and project is underway
10
Arachis hypogaea
L.
2890 Mb
In progress
analyses. Interestingly, in leguminous crops genome-
wide studies are important to identify and establish
network(s) involved in stress response pathways, which
could eventually be manipulated to minimize crop
losses due to abiotic stresses (Urano
et al.,
2010).
The analysis of gene transcripts is probably the most
developed field. Indeed, there are several platforms
available for gene transcript analysis. The quantitative
real-time polymerase chain reaction (qRT-PCR) tech-
nique is considered highly accurate but permits analysis
of only a limited number of genes. In contrast, gene
microarray technology allows the analysis of thousands
of genes at a time. RNA-Seq is oriented to the
performance of unbiased analysis of RNA transcripts,
which generates gigabyte-sized readouts with all the
RNA transcripts of a given cell, organism or tissue
(Brady
et al.,
2007; Steibel
et al.,
2009). This increasing
amount of data generated has led researchers to create
databases of experiments in environmental stresses
(Dinneny
et al.,
2008).
Mantri
et al.
(2007) studied transcriptional profiling in
the leaf, root and/or flower tissues in tolerant and sus-
ceptible genotypes in chickpea under salinity, drought
and cold, and reported over two-fold differential expres-
sion in hundreds of transcripts. The differentially
expressed genes coded for several functional and
regulatory proteins, which indicates that multiple genes
regulate the abiotic stress response mechanisms. A very
limited number of molecular markers and candidate
genes are available for undertaking molecular breeding
in chickpea to tackle salinity stresses. The study con-
ducted by Varshney
et al.
(2009) reports on the
generation and analysis of a comprehensive resource of
drought- and salinity-responsive expressed sequence
tags (ESTs) and gene-based markers. A total of 20,162
(18,435 high quality) drought- and salinity-responsive
ESTs were generated from 10 different root tissue cDNA
libraries of chickpea. Hierarchical clustering of 105
selected contigs provided clues about stress-responsive
candidate genes, and their expression profiles showed
predominance in specific stress-challenged libraries.
Such a set of chickpea ESTs serves as a resource of high-
quality transcripts for gene discovery and development
of functional markers associated with abiotic stress tol-
erance that will facilitate chickpea breeding. Mapping of
gene-based markers in chickpea will also add more ref-
erence points to align genomes of chickpea and other
legume species (Varshney
et al.,
2009).
A study by Molina
et al.
(2011) reported characteriza-
tion of salt-stress responses of chickpeas at the
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