Análisis de encuestas de graficas de barras

As of March 1, 2012, there were only 252,832

Arachis ESTs in NCBI’s GenBank, 178,490

from A. hypogaea (Feng et al.2012). Extensive

sequencing of expressed genes has occurred over the last 4 years as costs for next-generation sequencing have dropped precipitously. Much of this sequence has been deposited in public

databases along with previously generated

microarray data (Table6.3). Analyses of differ-

ential gene expression and pathways across developmental series and in response to pests, diseases, and abiotic stress have been the primary objectives across these studies. Several microar-

ray experiments preceded high-throughput

sequencing and were informative for the study of drought stress response. Expressed sequence from all primary organs of cultivated peanut is available and can be used to construct gene networks that will inform the study of traits that

impact productivity (Clevenger et al.2016). For

example, an oil metabolic network of peanut and three other oilseed crops (soybean, rapeseed, and sesame) has been constructed using available

EST data and is accessible at http://ocri-

genomics.org/ocsESTdb (Ke et al. 2015). Knowledge of gene expression, association of expressed genes with gene models in the newly released diploid Arachis genomes (Bertioli et al.

2016), and putative positions with respect to

QTL are contributing to hypothesis development and testing of gene function.

Testing of gene function often includes quantitative analysis of expression patterns across time, developmental stages, treatments, etc. Such analyses require a frame of reference. i.e., genes whose expression is relatively uniform regardless of tissue or treatment. Four studies to identify suitable reference genes for peanut have

been conducted, testing 8–14 candidates across a

variety of tissues (Table 6.4). Both alcohol

dehydrogenase III and alpha tubulin showed high stability and moderate abundance in more than one study.

In addition to extensive transcriptome data, proteome data are being produced to further evaluate gene function, study physiological responses, and develop proteome maps. While a couple of studies have been focused on the peanut leaf proteome, with or without stress

(Kottapalli et al.2009; Katam et al.2010), most

have targeted peanut gynophores (Li et al. 2013;

Sun et al. 2013; Xia et al. 2015, Zhao et al.

2015b), organs that are relatively unique among crop plants but whose response to gravity is critical for reproductive success. In addition to being transcribed, most genes must be translated to perform a function, and the coordination of transcription and translation can be different for different genes depending on the regulation of expression and turnover rates. Analyzing the proteome independently and in concert with the transcriptome can provide new insight into gene

action. For example, Zhao et al. (2015b) only

identified transcripts for 38 out of 69 key peg

proteins while transcripts were not identified for

some abundant proteins and vice versa.

6.4

Bioinformatics Resources

Bioinformatics resources for peanut at the time of writing include the genome sequences of the diploid progenitors of cultivated peanut, A. dura- nensis and A. ipaensis, as well as extensive tran- scriptome sequences for wild diploid accessions

and for cultivated peanut (Bertioli et al.2016). The

Table 6.3 Publicly available gene expression data sets

Data type NCBI accession Publication Target

tissues

Treatment/ Experimental materialsa

Objective

RNAseq PRJNA312741 Qingdao Agric Univ, unpubl. Leaf Salinity stress

RNAseq PRJNA298453 Shandong Acad Agric Sci, unpubl. Gynophore Light and dark

RNAseq PRJNA291488 Bertioli et al. (2016) 22 Developmental

stages

Also accessible at peanutbase. org

RNAseq PRJNA290039 Zhao et al. (2015b) Gynophore Developmental

stages

Integrated with proteome analysis

RNAseq PRJNA286040 Peng et al. (2016) Leaf Two genotypes SSR discovery

RNAseq PRJNA284674 Guimaraes et al. (2015) Root A. stenosperma/

nematode

Pest pressure time course

RNAseq PRJNA251584 Fujian Agric For Univ, unpubl. 8 Biotic and abiotic

stress

miRNAseq PRJNA251517 Zhao et al. (2015a) Root Organ

RNAseq PRJNA248910 Chopra et al. (2014) Leaf, pod,

root A. hypogaea A. ipaensis A. duranensis Assembler comparisons

RNAseq PRJNA243319 Li et al. (2014) Leaf, root,

stem

Water deficit and ABA

Differential gene expression

RNAseq PRJNA233534 Geng et al. (2014) Leaf, stem,

pod, root

One genotype Tissue-specific

gene discovery

RNAseq SRA053198 Chen et al. (2013) Pod Above and below

ground

Early embryo abortion

RNAseq PRJNA185732 Yin et al. (2013) Seed Two genotypes Oil metabolism

RNAseq PRJNA181974 Xia et al. (2013), Zhong et al.(2016) Gynophore Developmental

stages/light–dark Differential gene expression SSR discovery Small RNAseq

PRJNA146213 Zhai et al. (2011) Flower,

nodule

Organs Legume

conservation

Microarray PRJNA143823 Chen et al. (2012) Leaves,

pods

Five varieties Expression in

response to selection

Microarray PRJNA138261 Guo et al. (2011) Seeds Two cultivars,

Aspergillus infected Differential response to Aspergillus exposure Microarray PRJNA123433, PRJNA123473, PRJNA119623

Payton, unpubl. Leaf Two genotypes,

Heat stress

Comparison of acclimated and unacclimated response

Microarray PRJNA106585 Payton et al. (2009) Leaf, stem,

peg, pod, root

Organs Tissue-speci_fic

gene expression

RNAseq PRJNA78245 Guimaraes et al. (2012) Leaf A. stenosperma/

leaf spot

Infection time course

GenBank (A. duranensis: GCA_000817695.1, A. ipaensis: GCA_000816755.1). Efforts are under- way to sequence the tetraploid peanut genome, along with extensive high-density genotyping of diverse tetraploid accessions and RIL (Recombi- nant Inbred Lines) populations.

The primary repository for peanut genetic and

genomic data for the IPGI is PeanutBase (http://

peanutbase.org), which holds genome sequences, gene model sequences and annotations, genetic maps, mapped traits (QTL), expression data, germplasm information, and a variety of tools for browsing, searching, querying, and interrelating

these data sets. These tools are briefly described

below.