A genomic resource for the sedentary semi-endoparasitic reniform nematode, Rotylenchulus reniformis Linford & Oliveira.

The reniform nematode (Rotylenchulus reniformis) is a sedentary semi-endoparasitic species that is pathogenic on many row crops, fruits, and vegetables. Here, the authors present a draft genome assembly of R. reniformis using small- and large-insert libraries sequenced on the Illumina GAIIx and MiSeq platforms.The reniform nematode (Rotylenchulus reniformis) is a sedentary semi-endoparasitic species that is pathogenic on many row crops, fruits, and vegetables. Here, the authors present a draft genome assembly of R. reniformis using small- and large-insert libraries sequenced on the Illumina GAIIx and MiSeq platforms.

Insights into the Evolution of the New World Diploid Cottons (Gossypium, Subgenus Houzingenia) Based on Genome Sequencing.

We employed phylogenomic methods to study molecular evolutionary processes and phylogeny in the geographically widely dispersed New World diploid cottons (Gossypium, subg. Houzingenia). Whole genome resequencing data (average of 33× genomic coverage) were generated to reassess the phylogenetic history of the subgenus and provide a temporal framework for its diversification. Phylogenetic analyses indicate that the subgenus likely originated following transoceanic dispersal from Africa about 6.6 Ma, but that nearly all of the biodiversity evolved following rapid diversification in the mid-Pleistocene (0.5-2.0 Ma), with multiple long-distance dispersals required to account for range expansion to Arizona, the Galapagos Islands, and Peru. Comparative analyses of cpDNAversus nuclear data indicate that this history was accompanied by several clear cases of interspecific introgression. Repetitive DNAs contribute roughly half of the total 880 Mb genome, but most transposable element families are relatively old and stable among species. In the genic fraction, pairwise synonymous mutation rates average 1% per Myr, with nonsynonymous changes being about seven times less frequent. Over 1.1 million indels were detected and phylogenetically polarized, revealing a 2-fold bias toward deletions over small insertions. We suggest that this genome down-sizing bias counteracts genome size growth by TE amplification and insertions, and helps explain the relatively small genomes that are restricted to this subgenus. Compared with the rate of nucleotide substitution, the rate of indel occurrence is much lower averaging about 17 nucleotide substitutions per indel event.

Five life stage-specific transcriptome assemblies for the reniform nematode, Rotylenchulus reniformis Linford & Oliveira.

The reniform nematode (Rotylenchulus reniformis Linford and Oliveira) is a semi-endoparasitic nematode that is a pathogen of numerous major crops such as cotton and soybean. Here, the authors present transcriptome assemblies of the egg, second-stage juvenile (J2), J3, vermiform adult, and sedentary female life stages of this important plant pathogen.The reniform nematode (Rotylenchulus reniformis Linford and Oliveira) is a semi-endoparasitic nematode that is a pathogen of numerous major crops such as cotton and soybean. Here, the authors present transcriptome assemblies of the egg, second-stage juvenile (J2), J3, vermiform adult, and sedentary female life stages of this important plant pathogen.

Comparative Genomics of an Unusual Biogeographic Disjunction in the Cotton Tribe (Gossypieae) Yields Insights into Genome Downsizing.

Long-distance insular dispersal is associated with divergence and speciation because of founder effects and strong genetic drift. The cotton tribe (Gossypieae) has experienced multiple transoceanic dispersals, generating an aggregate geographic range that encompasses much of the tropics and subtropics worldwide. Two genera in the Gossypieae, Kokia and Gossypioides, exhibit a remarkable geographic disjunction, being restricted to the Hawaiian Islands and Madagascar/East Africa, respectively. We assembled and use de novo genome sequences to address questions regarding the divergence of these two genera from each other and from their sister-group, Gossypium. In addition, we explore processes underlying the genome downsizing that characterizes Kokia and Gossypioides relative to other genera in the tribe. Using 13,000 gene orthologs and synonymous substitution rates, we show that the two disjuncts last shared a common ancestor ∼5 Ma, or half as long ago as their divergence from Gossypium. We report relative stasis in the transposable element fraction. In comparison to Gossypium, there is loss of ∼30% of the gene content in the two disjunct genera and a history of genome-wide accumulation of deletions. In both genera, there is a genome-wide bias toward deletions over insertions, and the number of gene losses exceeds the number of gains by ∼2- to 4-fold. The genomic analyses presented here elucidate genomic consequences of the demographic and biogeographic history of these closest relatives of Gossypium, and enhance their value as phylogenetic outgroups.

Independent Domestication of Two Old World Cotton Species.

Domesticated cotton species provide raw material for the majority of the world's textile industry. Two independent domestication events have been identified in allopolyploid cotton, one in Upland cotton (Gossypium hirsutum L.) and the other to Egyptian cotton (Gossypium barbadense L.). However, two diploid cotton species, Gossypium arboreum L. and Gossypium herbaceum L., have been cultivated for several millennia, but their status as independent domesticates has long been in question. Using genome resequencing data, we estimated the global abundance of various repetitive DNAs. We demonstrate that, despite negligible divergence in genome size, the two domesticated diploid cotton species contain different, but compensatory, repeat content and have thus experienced cryptic alterations in repeat abundance despite equivalence in genome size. Evidence of independent origin is bolstered by estimates of divergence times based on molecular evolutionary analysis of f7,000 orthologous genes, for which synonymous substitution rates suggest that G. arboreum and G. herbaceum last shared a common ancestor approximately 0.4-2.5 Ma. These data are incompatible with a shared domestication history during the emergence of agriculture and lead to the conclusion that G. arboreum and G. herbaceum were each domesticated independently.

Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres.

Polyploidy often confers emergent properties, such as the higher fibre productivity and quality of tetraploid cottons than diploid cottons bred for the same environments. Here we show that an abrupt five- to sixfold ploidy increase approximately 60 million years (Myr) ago, and allopolyploidy reuniting divergent Gossypium genomes approximately 1-2 Myr ago, conferred about 30-36-fold duplication of ancestral angiosperm (flowering plant) genes in elite cottons (Gossypium hirsutum and Gossypium barbadense), genetic complexity equalled only by Brassica among sequenced angiosperms. Nascent fibre evolution, before allopolyploidy, is elucidated by comparison of spinnable-fibred Gossypium herbaceum A and non-spinnable Gossypium longicalyx F genomes to one another and the outgroup D genome of non-spinnable Gossypium raimondii. The sequence of a G. hirsutum A(t)D(t) (in which 't' indicates tetraploid) cultivar reveals many non-reciprocal DNA exchanges between subgenomes that may have contributed to phenotypic innovation and/or other emergent properties such as ecological adaptation by polyploids. Most DNA-level novelty in G. hirsutum recombines alleles from the D-genome progenitor native to its New World habitat and the Old World A-genome progenitor in which spinnable fibre evolved. Coordinated expression changes in proximal groups of functionally distinct genes, including a nuclear mitochondrial DNA block, may account for clusters of cotton-fibre quantitative trait loci affecting diverse traits. Opportunities abound for dissecting emergent properties of other polyploids, particularly angiosperms, by comparison to diploid progenitors and outgroups.

Prediction of cell penetrating peptides by support vector machines.

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

The proteogenomic mapping tool.

BACKGROUND: High-throughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods. To keep pace with the high speed of experimental data generation and to aid in structural genome annotation, experimentally observed peptides need to be mapped back to their source genome location quickly and exactly. Previously, the tools to do this have been limited to custom scripts designed by individual research groups to analyze their own data, are generally not widely available, and do not scale well with large eukaryotic genomes. RESULTS: The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality. CONCLUSIONS: The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data.

Prediction of peptides observable by mass spectrometry applied at the experimental set level.

BACKGROUND: When proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification. RESULTS: We have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection. CONCLUSION: Classifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification.