Cultivated peanut (Arachis hypogaea) is an allotetraploid with closely related subgenomes of a total size of ~2.7 Gb. This makes the assembly of chromosomal pseudomolecules very challenging. As a foundation to understanding the genome of cultivated peanut, we report the genome sequences of its diploid ancestors (Arachis duranensis and Arachis ipaensis). We show that these genomes are similar to cultivated peanut's A and B subgenomes and use them to identify candidate disease resistance genes, to guide tetraploid transcript assemblies and to detect genetic exchange between cultivated peanut's subgenomes. On the basis of remarkably high DNA identity of the A. ipaensis genome and the B subgenome of cultivated peanut and biogeographic evidence, we conclude that A. ipaensis may be a direct descendant of the same population that contributed the B subgenome to cultivated peanut. A r t i c l e s npg © 2016 Nature America, Inc. All rights reserved.Nature GeNetics VOLUME 48 | NUMBER 4 | APRIL 2016 4 3 9 subgenomes of A. hypogaea. Progeny are vigorous, phenotypically normal and fertile and showed lower segregation distortion 16,17 than has been observed for some populations derived from A. hypogaea intraspecific crosses [18][19][20][21] . Therefore, as a first step to characterizing the genome of cultivated peanut, we sequenced and analyzed the genomes of the two diploid ancestors of cultivated peanut. RESULTS Sequencing and assembly of the diploid A and B genomesConsidering that A. duranensis V14167 and A. ipaensis K30076 are likely good representatives of the ancestral species of A. hypogaea, we sequenced their genomes. After filtering, the data generated from the seven paired-end libraries corresponded to an estimated 154× and 163× base-pair coverage for A. duranensis and A. ipaensis, respectively (Supplementary Tables 1-6). The total assembly sizes were 1,211 and 1,512 Mb for A. duranensis and A. ipaensis, respectively, of which 1,081 and 1,371 Mb were represented in scaffolds of 10 kb or greater in size (Supplementary Table 7). Ultradense genetic maps were generated through genotyping by sequencing (GBS) of two diploid recombinant inbred line (RIL) populations (Supplementary Data Set 1). SNPs within scaffolds were used to validate the assemblies and confirmed their high quality; 190 of 1,297 initial scaffolds of A. duranensis and 49 of 353 initial scaffolds of A. ipaensis were identified as chimeric, on the basis of the presence of diagnostic population-wide switches in genotype calls occurring at the point of misjoin. Chimeric scaffolds were split, and their components were remapped. Thus, approximate chromosomal placements were obtained for 1,692 and 459 genetically verified scaffolds, respectively. Conventional molecular marker maps (Supplementary Data Set 2) and syntenic inferences were then used to refine the ordering of scaffolds within the initial genetic bins. Generally, agreement was good for maps in euchromatic arms and poorer in pericentromeric regions (although one map 22 showed large inversions in two lin...

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Unresolved questions about evolution of the large and diverse legume family include the timing of polyploidy (whole-genome duplication; WGDs) relative to the origin of the major lineages within the Fabaceae and to the origin of symbiotic nitrogen fixation. Previous work has established that a WGD affects most lineages in the Papilionoideae and occurred sometime after the divergence of the papilionoid and mimosoid clades, but the exact timing has been unknown. The history of WGD has also not been established for legume lineages outside the Papilionoideae. We investigated the presence and timing of WGDs in the legumes by querying thousands of phylogenetic trees constructed from transcriptome and genome data from 20 diverse legumes and 17 outgroup species. The timing of duplications in the gene trees indicates that the papilionoid WGD occurred in the common ancestor of all papilionoids. The earliest diverging lineages of the Papilionoideae include both nodulating taxa, such as the genistoids (e.g., lupin), dalbergioids (e.g., peanut), phaseoloids (e.g., beans), and galegoids (=Hologalegina, e.g., clovers), and clades with nonnodulating taxa including Xanthocercis and Cladrastis (evaluated in this study). We also found evidence for several independent WGDs near the base of other major legume lineages, including the Mimosoideae-Cassiinae-Caesalpinieae (MCC), Detarieae, and Cercideae clades. Nodulation is found in the MCC and papilionoid clades, both of which experienced ancestral WGDs. However, there are numerous nonnodulating lineages in both clades, making it unclear whether the phylogenetic distribution of nodulation is due to independent gains or a single origin followed by multiple losses.

PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control.

Background: Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated.