PGSB PlantsDB: updates to the database framework for comparative plant genome research

Structural variation in the pangenome of wild and domesticated barley

Pangenomes are collections of annotated genome sequences of multiple individuals of a species1. The structural variants uncovered by these datasets are a major asset to genetic analysis in crop plants2. Here we report a pangenome of barley comprising long-read sequence assemblies of 76 wild and domesticated genomes and short-read sequence data of 1,315 genotypes. An expanded catalogue of sequence variation in the crop includes structurally complex loci that are rich in gene copy number variation. To demonstrate the utility of the pangenome, we focus on four loci involved in disease resistance, plant architecture, nutrient release and trichome development. Novel allelic variation at a powdery mildew resistance locus and population-specific copy number gains in a regulator of vegetative branching were found. Expansion of a family of starch-cleaving enzymes in elite malting barleys was linked to shifts in enzymatic activity in micro-malting trials. Deletion of an enhancer motif is likely to change the developmental trajectory of the hairy appendages on barley grains. Our findings indicate that allelic diversity at structurally complex loci may have helped crop plants to adapt to new selective regimes in agricultural ecosystems.

Biological Insights and Recent Advances in Plant Long Non-Coding RNA

Long non-coding RNA (lncRNA) refers to an RNA molecule longer than 200 nucleotides (nt) that plays a significant role in regulating essential molecular and biological processes. It is commonly found in animals, plants, and viruses, and is characterized by features such as epigenetic markers, developmental stage-specific expression, and tissue-specific expression. Research has shown that lncRNA participates in anatomical processes like plant progression, while also playing a crucial role in plant disease resistance and adaptation mechanisms. In this review, we provide a concise overview of the formation mechanism, structural characteristics, and databases related to lncRNA in recent years. We primarily discuss the biological roles of lncRNA in plant progression as well as its involvement in response to biotic and abiotic stresses. Additionally, we examine the current challenges associated with lncRNA and explore its potential application in crop production and breeding. Studying plant lncRNAs is highly significant for multiple reasons: It reveals the regulatory mechanisms of plant growth and development, promotes agricultural production and food security, and drives research in plant genomics and epigenetics. Additionally, it facilitates ecological protection and biodiversity conservation.

Unravelling sorghum functional genomics and molecular breeding: past achievements and future prospects

Sorghum, renowned for its substantial biomass production and remarkable tolerance to various stresses, possesses extensive gene resources and phenotypic variations. A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands. In this context, we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade, accompanied by a summary of functional genomic platforms. We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits. Lastly, we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles, with the aim of accelerating the development of desired sorghum varieties.

Origin and evolution of the triploid cultivated banana genome

Most fresh bananas belong to the Cavendish and Gros Michel subgroups. Here, we report chromosome-scale genome assemblies of Cavendish (1.48 Gb) and Gros Michel (1.33 Gb), defining three subgenomes, Ban, Dh and Ze, with Musa acuminata ssp. banksii, malaccensis and zebrina as their major ancestral contributors, respectively. The insertion of repeat sequences in the Fusarium oxysporum f. sp. cubense (Foc) tropical race 4 RGA2 (resistance gene analog 2) promoter was identified in most diploid and triploid bananas. We found that the receptor-like protein (RLP) locus, including Foc race 1-resistant genes, is absent in the Gros Michel Ze subgenome. We identified two NAP (NAC-like, activated by apetala3/pistillata) transcription factor homologs specifically and highly expressed in fruit that directly bind to the promoters of many fruit ripening genes and may be key regulators of fruit ripening. Our genome data should facilitate the breeding and super-domestication of bananas.

PlantTribes2: Tools for comparative gene family analysis in plant genomics

Plant genome-scale resources are being generated at an increasing rate as sequencing technologies continue to improve and raw data costs continue to fall; however, the cost of downstream analyses remains large. This has resulted in a considerable range of genome assembly and annotation qualities across plant genomes due to their varying sizes, complexity, and the technology used for the assembly and annotation. To effectively work across genomes, researchers increasingly rely on comparative genomic approaches that integrate across plant community resources and data types. Such efforts have aided the genome annotation process and yielded novel insights into the evolutionary history of genomes and gene families, including complex non-model organisms. The essential tools to achieve these insights rely on gene family analysis at a genome-scale, but they are not well integrated for rapid analysis of new data, and the learning curve can be steep. Here we present PlantTribes2, a scalable, easily accessible, highly customizable, and broadly applicable gene family analysis framework with multiple entry points including user provided data. It uses objective classifications of annotated protein sequences from existing, high-quality plant genomes for comparative and evolutionary studies. PlantTribes2 can improve transcript models and then sort them, either genome-scale annotations or individual gene coding sequences, into pre-computed orthologous gene family clusters with rich functional annotation information. Then, for gene families of interest, PlantTribes2 performs downstream analyses and customizable visualizations including, (1) multiple sequence alignment, (2) gene family phylogeny, (3) estimation of synonymous and non-synonymous substitution rates among homologous sequences, and (4) inference of large-scale duplication events. We give examples of PlantTribes2 applications in functional genomic studies of economically important plant families, namely transcriptomics in the weedy Orobanchaceae and a core orthogroup analysis (CROG) in Rosaceae. PlantTribes2 is freely available for use within the main public Galaxy instance and can be downloaded from GitHub or Bioconda. Importantly, PlantTribes2 can be readily adapted for use with genomic and transcriptomic data from any kind of organism.

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes

LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classification of these elements remains a challenge today. Moreover, current software can be relatively slow (from hours to days), sometimes involve a lot of manual work and do not reach satisfactory levels in terms of precision and sensitivity. Here we present Inpactor2, an accurate and fast application that creates LTR-retrotransposon reference libraries in a very short time. Inpactor2 takes an assembled genome as input and follows a hybrid approach (deep learning and structure-based) to detect elements, filter partial sequences and finally classify intact sequences into superfamilies and, as very few tools do, into lineages. This tool takes advantage of multi-core and GPU architectures to decrease execution times. Using the rice genome, Inpactor2 showed a run time of 5 minutes (faster than other tools) and has the best accuracy and F1-Score of the tools tested here, also having the second best accuracy and specificity only surpassed by EDTA, but achieving 28% higher sensitivity. For large genomes, Inpactor2 is up to seven times faster than other available bioinformatics tools.

The chromosome-scale genome of Phoebe bournei reveals contrasting fates of terpene synthase (TPS)-a and TPS-b subfamilies

Terpenoids, including aromatic volatile monoterpenoids and sesquiterpenoids, function in defense against pathogens and herbivores. Phoebe trees are remarkable for their scented wood and decay resistance. Unlike other Lauraceae species investigated to date, Phoebe species predominantly accumulate sesquiterpenoids instead of monoterpenoids. Limited genomic data restrict the elucidation of terpenoid variation and functions. Here, we present a chromosome-scale genome assembly of a Lauraceae tree, Phoebe bournei, and identify 72 full-length terpene synthase (TPS) genes. Genome-level comparison shows pervasive lineage-specific duplication and contraction of TPS subfamilies, which have contributed to the extreme terpenoid variation within Lauraceae species. Although the TPS-a and TPS-b subfamilies were both expanded via tandem duplication in P. bournei, more TPS-a copies were retained and constitutively expressed, whereas more TPS-b copies were lost. The TPS-a genes on chromosome 8 functionally diverged to synthesize eight highly accumulated sesquiterpenes in P. bournei. The essential oil of P. bournei and its main component, β-caryophyllene, exhibited antifungal activities against the three most widespread canker pathogens of trees. The TPS-a and TPS-b subfamilies have experienced contrasting fates over the evolution of P. bournei. The abundant sesquiterpenoids produced by TPS-a proteins contribute to the excellent pathogen resistance of P. bournei trees. Overall, this study sheds light on the evolution and adaptation of terpenoids in Lauraceae and provides valuable resources for boosting plant immunity against pathogens in various trees and crops.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Parasitic plant small RNA analyses unveil parasite-specific signatures of microRNA retention, loss, and gain

Parasitism is a successful life strategy that has evolved independently in several families of vascular plants. The genera Cuscuta and Orobanche represent examples of the two profoundly different groups of parasites: one parasitizing host shoots and the other infecting host roots. In this study, we sequenced and described the overall repertoire of small RNAs from Cuscuta campestris and Orobanche aegyptiaca. We showed that C. campestris contains a number of novel microRNAs (miRNAs) in addition to a conspicuous retention of miRNAs that are typically lacking in other Solanales, while several typically conserved miRNAs seem to have become obsolete in the parasite. One new miRNA appears to be derived from a horizontal gene transfer event. The exploratory analysis of the miRNA population (exploratory due to the absence of a full genomic sequence for reference) from the root parasitic O. aegyptiaca also revealed a loss of a number of miRNAs compared to photosynthetic species from the same order. In summary, our study shows partly similar evolutionary signatures in the RNA silencing machinery in both parasites. Our data bear proof for the dynamism of this regulatory mechanism in parasitic plants.

Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning

Transposable elements are mobile sequences that can move and insert themselves into chromosomes, activating under internal or external stimuli, giving the organism the ability to adapt to the environment. Annotating transposable elements in genomic data is currently considered a crucial task to understand key aspects of organisms such as phenotype variability, species evolution, and genome size, among others. Because of the way they replicate, LTR retrotransposons are the most common transposable elements in plants, accounting in some cases for up to 80% of all DNA information. To annotate these elements, a reference library is usually created, a curation process is performed, eliminating TE fragments and false positives and then annotated in the genome using the homology method. However, the curation process can take weeks, requires extensive manual work and the execution of multiple time-consuming bioinformatics software. Here, we propose a machine learning-based approach to perform this process automatically on plant genomes, obtaining up to 91.18% F1-score. This approach was tested with four plant species, obtaining up to 93.6% F1-score (Oryza granulata) in only 22.61 s, where bioinformatics methods took approximately 6 h. This acceleration demonstrates that the ML-based approach is efficient and could be used in massive sequencing projects.

Chromosome-scale assembly and annotation of the perennial ryegrass genome

BACKGROUND

The availability of chromosome-scale genome assemblies is fundamentally important to advance genetics and breeding in crops, as well as for evolutionary and comparative genomics. The improvement of long-read sequencing technologies and the advent of optical mapping and chromosome conformation capture technologies in the last few years, significantly promoted the development of chromosome-scale genome assemblies of model plants and crop species. In grasses, chromosome-scale genome assemblies recently became available for cultivated and wild species of the Triticeae subfamily. Development of state-of-the-art genomic resources in species of the Poeae subfamily, which includes important crops like fescues and ryegrasses, is lagging behind the progress in the cereal species.

RESULTS

Here, we report a new chromosome-scale genome sequence assembly for perennial ryegrass, obtained by combining PacBio long-read sequencing, Illumina short-read polishing, BioNano optical mapping and Hi-C scaffolding. More than 90% of the total genome size of perennial ryegrass (approximately 2.55 Gb) is covered by seven pseudo-chromosomes that show high levels of collinearity to the orthologous chromosomes of Triticeae species. The transposon fraction of perennial ryegrass was found to be relatively low, approximately 35% of the total genome content, which is less than half of the genome repeat content of cultivated cereal species. We predicted 54,629 high-confidence gene models, 10,287 long non-coding RNAs and a total of 8,393 short non-coding RNAs in the perennial ryegrass genome.

CONCLUSIONS

The new reference genome sequence and annotation presented here are valuable resources for comparative genomic studies in grasses, as well as for breeding applications and will expedite the development of productive varieties in perennial ryegrass and related species.

Genome sequences of five Sitopsis species of Aegilops and the origin of polyploid wheat B subgenome

Common wheat (Triticum aestivum, BBAADD) is a major staple food crop worldwide. The diploid progenitors of the A and D subgenomes have been unequivocally identified; that of B, however, remains ambiguous and controversial but is suspected to be related to species of Aegilops, section Sitopsis. Here, we report the assembly of chromosome-level genome sequences of all five Sitopsis species, namely Aegilops bicornis, Ae. longissima, Ae. searsii, Ae. sharonensis, and Ae. speltoides, as well as the partial assembly of the Amblyopyrum muticum (synonym Aegilops mutica) genome for phylogenetic analysis. Our results reveal that the donor of the common wheat B subgenome is a distinct, and most probably extinct, diploid species that diverged from an ancestral progenitor of the B lineage to which the still extant Ae. speltoides and Am. muticum belong. In addition, we identified interspecific genetic introgressions throughout the evolution of the Triticum/Aegilops species complex. The five Sitopsis species have various assembled genome sizes (4.11-5.89 Gb) with high proportions of repetitive sequences (85.99%-89.81%); nonetheless, they retain high collinearity with other genomes or subgenomes of species in the Triticum/Aegilops complex. Differences in genome size were primarily due to independent post-speciation amplification of transposons. We also identified a set of Sitopsis genes pertinent to important agronomic traits that can be harnessed for wheat breeding. These newly assembled genome resources provide a new roadmap for evolutionary and genetic studies of the Triticum/Aegilops complex, as well as for wheat improvement.

Finding and Characterizing Repeats in Plant Genomes

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of the available software that can help biologists to scan automatically for these repeats in sequence data or check hypothetical models intended to characterize their structures. Since transposable elements (TEs) are a major source of repeats in plants, many methods have been used or developed for this broad class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided two sections on this topic (for the analysis of genomes or directly of sequenced reads), as well as a selection of the main existing software. It may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of an efficient search for repeats and more complex patterns. We first introduce the key concepts of the art of indexing and mapping or querying sequences. We end the chapter with the more prospective issue of building models of repeat families. We present the Machine Learning approach first, seeking to build predictors automatically for some families of ET, from a set of sequences known to belong to this family. A second approach, the linguistic (or syntactic) approach, allows biologists to describe themselves and check the validity of models of their favorite repeat family.

Computational Analysis of Transposable Elements and CircRNAs in Plants

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

The Barley and Wheat Pan-Genomes

To unlock the genetic potential in crops, multi-genome comparisons are an essential tool. Decreasing costs and improved sequencing technologies have democratized plant genome sequencing and led to a vast increase in the amount of available reference sequences on the one hand and enabled the assembly of even the largest and most complex and repetitive crops genomes such as wheat and barley. These developments have led to the era of pan-genomics in recent years. Pan-genome projects enable the definition of the core and dispensable genome for various crop species as well as the analysis of structural and functional variation and hence offer unprecedented opportunities for exploring and utilizing the genetic basis of natural variation in crops. Comparing, analyzing, and visualizing these multiple reference genomes and their diversity requires powerful and specialized computational strategies and tools.

High-Quality Genomes and High-Density Genetic Map Facilitate the Identification of Genes From a Weedy Rice

Genes have been lost or weakened from cultivated rice during rice domestication and breeding. Weedy rice (Oryza sativa f. spontanea) is usually recognized as the progeny between cultivated rice and wild rice and is also known to harbor an gene pool for rice breeding. Therefore, identifying genes from weedy rice germplasms is an important way to break the bottleneck of rice breeding. To discover genes from weedy rice germplasms, we constructed a genetic map based on w-hole-genome sequencing of a F₂ population derived from the cross between LM8 and a cultivated rice variety. We further identified 31 QTLs associated with 12 important agronomic traits and revealed that ORUFILM03g000095 gene may play an important role in grain length regulation and participate in grain formation. To clarify the genomic characteristics from weedy rice germplasms of LM8, we generated a high-quality genome assembly using single-molecule sequencing, Bionano optical mapping, and Hi-C technologies. The genome harbored a total size of 375.8 Mb, a scaffold N50 of 24.1 Mb, and originated approximately 0.32 million years ago (Mya) and was more closely related to Oryza sativa ssp. japonica. and contained 672 unique genes. It is related to the formation of grain shape, heading date and tillering. This study generated a high-quality reference genome of weedy rice and high-density genetic map that would benefit the analysis of genome evolution for related species and suggested an effective way to identify genes related to important agronomic traits for further rice breeding.

Unveiling the transcriptomic complexity of Miscanthus sinensis using a combination of PacBio long read- and Illumina short read sequencing platforms

Background

Miscanthus sinensis Andersson is a perennial grass that exhibits remarkable lignocellulose characteristics suitable for sustainable bioenergy production. However, knowledge of the genetic resources of this species is relatively limited, which considerably hampers further work on its biology and genetic improvement.

Results

In this study, through analyzing the transcriptome of mixed samples of leaves and stems using the latest PacBio Iso-Seq sequencing technology combined with Illumina HiSeq, we report the first full-length transcriptome dataset of M. sinensis with a total of 58.21 Gb clean data. An average of 15.75 Gb clean reads of each sample were obtained from the PacBio Iso-Seq system, which doubled the data size (6.68 Gb) obtained from the Illumina HiSeq platform. The integrated analyses of PacBio- and Illumina-based transcriptomic data uncovered 408,801 non-redundant transcripts with an average length of 1,685 bp. Of those, 189,406 transcripts were commonly identified by both methods, 169,149 transcripts with an average length of 619 bp were uniquely identified by Illumina HiSeq, and 51,246 transcripts with an average length of 2,535 bp were uniquely identified by PacBio Iso-Seq. Approximately 96 % of the final combined transcripts were mapped back to the Miscanthus genome, reflecting the high quality and coverage of our sequencing results. When comparing our data with genomes of four species of Andropogoneae, M. sinensis showed the closest relationship with sugarcane with up to 93 % mapping ratios, followed by sorghum with up to 80 % mapping ratios, indicating a high conservation of orthologs in these three genomes. Furthermore, 306,228 transcripts were successfully annotated against public databases including cell wall related genes and transcript factor families, thus providing many new insights into gene functions. The PacBio Iso-Seq data also helped identify 3,898 alternative splicing events and 2,963 annotated AS isoforms within 10 function categories.

Conclusions

Taken together, the present study provides a rich data set of full-length transcripts that greatly enriches our understanding of M. sinensis transcriptomic resources, thus facilitating further genetic improvement and molecular studies of the Miscanthus species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07971-x.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

SorGSD: updating and expanding the sorghum genome science database with new contents and tools

BACKGROUND

As the fifth major cereal crop originated from Africa, sorghum (Sorghum bicolor) has become a key C4 model organism for energy plant research. With the development of high-throughput detection technologies for various omics data, much multi-dimensional and multi-omics information has been accumulated for sorghum. Integrating this information may accelerate genetic research and improve molecular breeding for sorghum agronomic traits.

RESULTS

We updated the Sorghum Genome SNP Database (SorGSD) by adding new data, new features and renamed it to Sorghum Genome Science Database (SorGSD). In comparison with the original version SorGSD, which contains SNPs from 48 sorghum accessions mapped to the reference genome BTx623 (v2.1), the new version was expanded to 289 sorghum lines with both single nucleotide polymorphisms (SNPs) and small insertions/deletions (INDELs), which were aligned to the newly assembled and annotated sorghum genome BTx623 (v3.1). Moreover, phenotypic data and panicle pictures of critical accessions were provided in the new version. We implemented new tools including ID Conversion, Homologue Search and Genome Browser for analysis and updated the general information related to sorghum research, such as online sorghum resources and literature references. In addition, we deployed a new database infrastructure and redesigned a new user interface as one of the Genome Variation Map databases. The new version SorGSD is freely accessible online at http://ngdc.cncb.ac.cn/sorgsd/ .

CONCLUSIONS

SorGSD is a comprehensive integration with large-scale genomic variation, phenotypic information and incorporates online data analysis tools for data mining, genome navigation and analysis. We hope that SorGSD could provide a valuable resource for sorghum researchers to find variations they are interested in and generate customized high-throughput datasets for further analysis.

Sorghum's Whole-Plant Transcriptome and Proteome Responses to Drought Stress: A Review

Sorghum is a cereal crop with key agronomic traits of drought and heat stress tolerance, making it an ideal food and industrial commodity for hotter and more arid climates. These stress tolerances also present a useful scientific resource for studying the molecular basis for environmental resilience. Here we provide an extensive review of current transcriptome and proteome works conducted with laboratory, greenhouse, or field-grown sorghum plants exposed to drought, osmotic stress, or treated with the drought stress-regulatory phytohormone, abscisic acid. Large datasets from these studies reveal changes in gene/protein expression across diverse signaling and metabolic pathways. Together, the emerging patterns from these datasets reveal that the overall functional classes of stress-responsive genes/proteins within sorghum are similar to those observed in equivalent studies of other drought-sensitive model species. This highlights a monumental challenge of distinguishing key regulatory genes/proteins, with a primary role in sorghum adaptation to drought, from genes/proteins that change in expression because of stress. Finally, we discuss possible options for taking the research forward. Successful exploitation of sorghum research for implementation in other crops may be critical in establishing climate-resilient agriculture for future food security.

Using precision phenotyping to inform de novo domestication

An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and classify them, none of these tools can individually obtain accurate results. Here, we used Machine Learning algorithms based on k-mer counts to classify LTR retrotransposons from other genomic sequences and into lineages/families with an F1-Score of 95%, contributing to develop a free-alignment and automatic method to analyze these sequences.

ClassifyTE: A stacking based prediction of hierarchical classification of transposable elements

MOTIVATION

Transposable Elements (TEs) or jumping genes are DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even mediate duplications and large insertions and deletions in the genome, promoting gross genetic rearrangements. The proper classification of identified jumping genes is important for analyzing their genetic and evolutionary effects. An effective classifier, which can explain the role of TEs in germline and somatic evolution more accurately, is needed. In this study, we examine the performance of a variety of machine learning (ML) techniques and propose a robust method, ClassifyTE, for the hierarchical classification of TEs with high accuracy, using a stacking-based ML method.

RESULTS

We propose a stacking-based approach for the hierarchical classification of TEs. When trained on three different benchmark datasets, our proposed system achieved 4%, 10.68%, and 10.13% average percentage improvement (using the hF measure) compared to several state-of-the-art methods. We developed an end-to-end automated hierarchical classification tool based on the proposed approach, ClassifyTE, to classify TEs up to the super-family level. We further evaluated our method on a new TE library generated by a homology-based classification method and found relatively high concordance at higher taxonomic levels. Thus, ClassifyTE paves the way for a more accurate analysis of the role of TEs.

AVAILABILITY

The source code and data are available at https://github.com/manisa/ClassifyTE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning

Long terminal repeat (LTR) retrotransposons are mobile elements that constitute the major fraction of most plant genomes. The identification and annotation of these elements via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in genome size variation, LTR retrotransposons are also associated with the function and structure of different chromosomal regions and can alter the function of coding regions, among others. Several sequence databases of plant LTR retrotransposons are available for public access, such as PGSB and RepetDB, or restricted access such as Repbase. Although these databases are useful to identify LTR-RTs in new genomes by similarity, the elements of these databases are not fully classified to the lineage (also called family) level. Here, we present InpactorDB, a semi-curated dataset composed of 130,439 elements from 195 plant genomes (belonging to 108 plant species) classified to the lineage level. This dataset has been used to train two deep neural networks (i.e., one fully connected and one convolutional) for the rapid classification of these elements. In lineage-level classification approaches, we obtain up to 98% performance, indicated by the F1-score, precision and recall scores.

The barley pan-genome reveals the hidden legacy of mutation breeding

Genetic diversity is key to crop improvement. Owing to pervasive genomic structural variation, a single reference genome assembly cannot capture the full complement of sequence diversity of a crop species (known as the 'pan-genome'1). Multiple high-quality sequence assemblies are an indispensable component of a pan-genome infrastructure. Barley (Hordeum vulgare L.) is an important cereal crop with a long history of cultivation that is adapted to a wide range of agro-climatic conditions2. Here we report the construction of chromosome-scale sequence assemblies for the genotypes of 20 varieties of barley-comprising landraces, cultivars and a wild barley-that were selected as representatives of global barley diversity. We catalogued genomic presence/absence variants and explored the use of structural variants for quantitative genetic analysis through whole-genome shotgun sequencing of 300 gene bank accessions. We discovered abundant large inversion polymorphisms and analysed in detail two inversions that are frequently found in current elite barley germplasm; one is probably the product of mutation breeding and the other is tightly linked to a locus that is involved in the expansion of geographical range. This first-generation barley pan-genome makes previously hidden genetic variation accessible to genetic studies and breeding.

Multiple wheat genomes reveal global variation in modern breeding

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome¹, and the lack of genome-assembly data for multiple wheat lines^2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses^4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1⁶, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Comparison of multiple genome assemblies from wheat reveals extensive diversity that results from the complex breeding history of wheat and provides a basis for further potential improvements to this important food crop.

TERL: classification of transposable elements by convolutional neural networks

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences. Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks. This classification method tries to learn the best representation of the input data to classify it correctly. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for superfamilies and 95.7% and 91.5% for the order sequences from RepBase, respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level, respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. Contact:murilocruz@alunos.utfpr.edu.br.

A chromosome-level genome assembly of the wild rice Oryza rufipogon facilitates tracing the origins of Asian cultivated rice

Oryza rufipogon Griff. is a wild progenitor of the Asian cultivated rice Oryza sativa. To better understand the genomic diversity of the wild rice, high-quality reference genomes of O. rufipogon populations are needed, which also facilitate utilization of the wild genetic resources in rice breeding. In this study, we generated a chromosome-level genome assembly of O. rufipogon using a combination of short-read sequencing, single-molecule sequencing, BioNano and Hi-C platforms. The genome sequence (399.8 Mb) was assembled into 46 scaffolds on the 12 chromosomes, with contig N50 and scaffold N50 of 13.2 Mb and 20.3 Mb, respectively. The genome contains 36,520 protein-coding genes, and 49.37% of the genome consists of repetitive elements. The genome has strong synteny with those of the O. sativa subspecies indica and japonica, but containing some large structural variations. Evolutionary analysis unveiled the polyphyletic origins of O. sativa, in which the japonica and indica genome formations involved different divergent O. rufipogon (including O. nivara) lineages, accompanied by introgression of genomic regions between japonica and indica. This high-quality reference genome provides insight on the genome evolution of the wild rice and the origins of the O. sativa subspecies, and valuable information for basic research and rice breeding.

European maize genomes highlight intraspecies variation in repeat and gene content

The diversity of maize (Zea mays) is the backbone of modern heterotic patterns and hybrid breeding. Historically, US farmers exploited this variability to establish today’s highly productive Corn Belt inbred lines from blends of dent and flint germplasm pools. Here, we report de novo genome sequences of four European flint lines assembled to pseudomolecules with scaffold N50 ranging from 6.1 to 10.4 Mb. Comparative analyses with two US Corn Belt lines explains the pronounced differences between both germplasms. While overall syntenic order and consolidated gene annotations reveal only moderate pangenomic differences, whole-genome alignments delineating the core and dispensable genome, and the analysis of heterochromatic knobs and orthologous long terminal repeat retrotransposons unveil the dynamics of the maize genome. The high-quality genome sequences of the flint pool complement the maize pangenome and provide an important tool to study maize improvement at a genome scale and to enhance modern hybrid breeding.

De novo genome assemblies of four European flint maize lines and comparison with two US Corn Belt genomes provide insights into the dynamics of intraspecies variation in repeat and gene content in maize genomes.

Barley's Second Spring as A Model Organism for Chloroplast Research

Barley (Hordeum vulgare) has been widely used as a model crop for studying molecular and physiological processes such as chloroplast development and photosynthesis. During the second half of the 20th century, mutants such as albostrians led to the discovery of the nuclear-encoded, plastid-localized RNA polymerase and the retrograde (chloroplast-to-nucleus) signalling communication pathway, while chlorina-f2 and xantha mutants helped to shed light on the chlorophyll biosynthetic pathway, on the light-harvesting proteins and on the organization of the photosynthetic apparatus. However, during the last 30 years, a large fraction of chloroplast research has switched to the more “user-friendly” model species Arabidopsis thaliana, the first plant species whose genome was sequenced and published at the end of 2000. Despite its many advantages, Arabidopsis has some important limitations compared to barley, including the lack of a real canopy and the absence of the proplastid-to-chloroplast developmental gradient across the leaf blade. These features, together with the availability of large collections of natural genetic diversity and mutant populations for barley, a complete genome assembly and protocols for genetic transformation and gene editing, have relaunched barley as an ideal model species for chloroplast research. In this review, we provide an update on the genomics tools now available for barley, and review the biotechnological strategies reported to increase photosynthesis efficiency in model species, which deserve to be validated in barley.

Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements

Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.

DeepTE: a computational method for de novo classification of transposons with convolutional neural network

Motivation

Transposable elements (TEs) classification is an essential step to decode their roles in genome evolution. With a large number of genomes from non-model species becoming available, accurate and efficient TE classification has emerged as a new challenge in genomic sequence analysis.

Results

We developed a novel tool, DeepTE, which classifies unknown TEs using convolutional neural networks (CNNs). DeepTE transferred sequences into input vectors based on k-mer counts. A tree structured classification process was used where eight models were trained to classify TEs into super families and orders. DeepTE also detected domains inside TEs to correct false classification. An additional model was trained to distinguish between non-TEs and TEs in plants. Given unclassified TEs of different species, DeepTE can classify TEs into seven orders, which include 15, 24 and 16 super families in plants, metazoans and fungi, respectively. In several benchmarking tests, DeepTE outperformed other existing tools for TE classification. In conclusion, DeepTE successfully leverages CNN for TE classification, and can be used to precisely classify TEs in newly sequenced eukaryotic genomes.

Availability and implementation

DeepTE is accessible at https://github.com/LiLabAtVT/DeepTE.

Supplementary information

Supplementary data are available at Bioinformatics online.

De novo Genome Assembly of the indica Rice Variety IR64 Using Linked-Read Sequencing and Nanopore Sequencing

IR64 is a rice variety with high-yield that has been widely cultivated around the world. IR64 has been replaced by modern varieties in most growing areas. Given that modern varieties are mostly progenies or relatives of IR64, genetic analysis of IR64 is valuable for rice functional genomics. However, chromosome-level genome sequences of IR64 have not been available previously. Here, we sequenced the IR64 genome using synthetic long reads obtained by linked-read sequencing and ultra-long reads obtained by nanopore sequencing. We integrated these data and generated the de novo assembly of the IR64 genome of 367 Mb, equivalent to 99% of the estimated size. Continuity of the IR64 genome assembly was improved compared with that of a publicly available IR64 genome assembly generated by short reads only. We annotated 41,458 protein-coding genes, including 657 IR64-specific genes, that are missing in other high-quality rice genome assemblies IRGSP-1.0 of japonica cultivar Nipponbare or R498 of indica cultivar Shuhui498. The IR64 genome assembly will serve as a genome resource for rice functional genomics as well as genomics-driven and/or molecular breeding.

Elucidating the Functional Role of Predicted miRNAs in Post- Transcriptional Gene Regulation Along with Symbiosis in Medicago truncatula

Background:

microRNAs are small non-coding RNAs which inhibit translational and post-transcriptional processes whereas long non-coding RNAs are found to regulate both transcriptional and post-transcriptional gene expression. Medicago truncatula is a well-known model plant for studying legume biology and is also used as a forage crop. In spite of its importance in nitrogen fixation and soil fertility improvement, little information is available about Medicago non-coding RNAs that play important role in symbiosis.

Objective:

In this study we have tried to understand the role of Medicago ncRNAs in symbiosis and regulation of transcription factors.

Methods:

We have identified novel miRNAs by computational methods considering various parameters like length, MFEI, AU content, SSR signatures and tried to establish an interaction model with their targets obtained through psRNATarget server.

Results:

149 novel miRNAs are predicted along with their 770 target proteins. We have also shown that 51 of these novel miRNAs are targeting 282 lncRNAs.

Conclusion:

In this study role of Medicago miRNAs in the regulation of various transcription factors are elucidated. Knowledge gained from this study will have a positive impact on the nitrogen fixing ability of this important model plant, which in turn will improve the soil fertility.

Analysis of potential redundancy among Arabidopsis 6-phosphogluconolactonase isoforms in peroxisomes

Recent work revealed that PGD2, an Arabidopsis 6-phosphogluconate dehydrogenase (6-PGD) catalysing the third step of the oxidative pentose-phosphate pathway (OPPP) in peroxisomes, is essential during fertilization. Earlier studies on the second step, catalysed by PGL3, a dually targeted Arabidopsis 6-phosphogluconolactonase (6-PGL), reported the importance of OPPP reactions in plastids but their irrelevance in peroxisomes. Assuming redundancy of 6-PGL activity in peroxisomes, we examined the sequences of other higher plant enzymes. In tomato, there exist two 6-PGL isoforms with the strong PTS1 motif SKL. However, their analysis revealed problems regarding peroxisomal targeting: reporter-PGL detection in peroxisomes required construct modification, which was also applied to the Arabidopsis isoforms. The relative contribution of PGL3 versus PGL5 during fertilization was assessed by mutant crosses. Reduced transmission ratios were found for pgl3-1 (T-DNA-eliminated PTS1) and also for knock-out allele pgl5-2. The prominent role of PGL3 showed as compromised growth of pgl3-1 seedlings on sucrose and higher activity of mutant PGL3-1 versus PGL5 using purified recombinant proteins. Evidence for PTS1-independent uptake was found for PGL3-1 and other Arabidopsis PGL isoforms, indicating that peroxisome import may be supported by a piggybacking mechanism. Thus, multiple redundancy at the level of the second OPPP step in peroxisomes explains the occurrence of pgl3-1 mutant plants.

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Chromosome-scale genome sequence assemblies underpin pan-genomic studies. Recent genome assembly efforts in the large-genome Triticeae crops wheat and barley have relied on the commercial closed-source assembly algorithm DeNovoMagic. We present TRITEX, an open-source computational workflow that combines paired-end, mate-pair, 10X Genomics linked-read with chromosome conformation capture sequencing data to construct sequence scaffolds with megabase-scale contiguity ordered into chromosomal pseudomolecules. We evaluate the performance of TRITEX on publicly available sequence data of tetraploid wild emmer and hexaploid bread wheat, and construct an improved annotated reference genome sequence assembly of the barley cultivar Morex as a community resource.

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Chromosome-scale genome sequence assemblies underpin pan-genomic studies. Recent genome assembly efforts in the large-genome Triticeae crops wheat and barley have relied on the commercial closed-source assembly algorithm DeNovoMagic. We present TRITEX, an open-source computational workflow that combines paired-end, mate-pair, 10X Genomics linked-read with chromosome conformation capture sequencing data to construct sequence scaffolds with megabase-scale contiguity ordered into chromosomal pseudomolecules. We evaluate the performance of TRITEX on publicly available sequence data of tetraploid wild emmer and hexaploid bread wheat, and construct an improved annotated reference genome sequence assembly of the barley cultivar Morex as a community resource.

Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data

The Wheat@URGI portal has been developed to provide the international community of researchers and breeders with access to the bread wheat reference genome sequence produced by the International Wheat Genome Sequencing Consortium. Genome browsers, BLAST, and InterMine tools have been established for in-depth exploration of the genome sequence together with additional linked datasets including physical maps, sequence variations, gene expression, and genetic and phenomic data from other international collaborative projects already stored in the GnpIS information system. The portal provides enhanced search and browser features that will facilitate the deployment of the latest genomics resources in wheat improvement.

Identification of induced mutations in hexaploid wheat genome using exome capture assay

Wheat is a staple food crop of many countries. Improving resilience to biotic and abiotic stresses remain key breeding targets. Among these, rust diseases are the most detrimental in terms of depressing wheat production. In the present study, chemical mutagenesis was used to induce mutations in the wheat variety NN-Gandum-1. This cultivar is moderately resistant to leaf and yellow rust. The aim of mutagenesis was to improve resistance to the disease as well as to study function of genes conferring resistance to the disease. In the present investigation, a 0.8% EMS dose was found optimum for supporting 45-55% germination of NN-Gandum-1. A total of 3,634 M2 fertile plants were produced from each of the M1 plant. Out of these, 33 (0.91%) and 20 plants (0.55%) showed absolute resistance to leaf and yellow rust, respectively. While 126 (3.46%) and 127 plants (3.49%) exhibited high susceptibility to the leaf and yellow rust, respectively. In the M4 generation, a total of 11 M4 lines (nine absolute resistant and two highly susceptible) and one wild type were selected for NGS-based exome capture assay. A total of 104,779 SNPs were identified that were randomly distributed throughout the wheat sub genomes (A, B and D). Induced mutations in intronic sequences predominated. The highest total number of SNPs detected in this assay were mapped to chr.2B (14,273 SNPs), which contains the highest number of targeted base pairs in the assay. The average mutation density across all regions interrogated was estimated to be one mutation per 20.91 Mb. The highest mutation frequency was found in chr.2D (1/11.7 kb) and the lowest in chr.7D (1/353.4 kb). Out of the detected mutations, 101 SNPs were filtered using analysis criteria aimed to enrich for mutations that may affect gene function. Out of these, one putative SNP detected in Lr21 were selected for further analysis. The SNP identified in chimeric allele (Lr21) of a resistant mutant (N1-252) was located in a NBS domain of chr.1BS at 3.4 Mb position. Through computational analysis, it was demonstrated that this identified SNP causes a substitution of glutamic acid with alanine, resulting in a predicted altered protein structure. This mutation, therefore, is a candidate for contributing to the resistance phenotype in the mutant line. Based on this work, we conclude that the wheat mutant resource developed is useful as a source of novel genetic variation for forward-genetic screens and also as a useful tool for gaining insights into the important biological circuits of different traits of complex genomes like wheat.

Footprints of parasitism in the genome of the parasitic flowering plant Cuscuta campestris

A parasitic lifestyle, where plants procure some or all of their nutrients from other living plants, has evolved independently in many dicotyledonous plant families and is a major threat for agriculture globally. Nevertheless, no genome sequence of a parasitic plant has been reported to date. Here we describe the genome sequence of the parasitic field dodder, Cuscuta campestris. The genome contains signatures of a fairly recent whole-genome duplication and lacks genes for pathways superfluous to a parasitic lifestyle. Specifically, genes needed for high photosynthetic activity are lost, explaining the low photosynthesis rates displayed by the parasite. Moreover, several genes involved in nutrient uptake processes from the soil are lost. On the other hand, evidence for horizontal gene transfer by way of genomic DNA integration from the parasite's hosts is found. We conclude that the parasitic lifestyle has left characteristic footprints in the C. campestris genome.

A comprehensive review of web-based resources of non-coding RNAs for plant science research

Non-coding RNAs (ncRNAs) are transcribed from genome but not translated into proteins. Many ncRNAs are key regulators of plants growth and development, metabolism and stress tolerance. In order to make the web-based ncRNA resources for plant science research be more easily accessible and understandable, we made a comprehensive review for 83 web-based resources of three types, including genome databases containing ncRNA data, microRNA (miRNA) databases and long non-coding RNA (lncRNA) databases. To facilitate effective usage of these resources, we also suggested some preferred resources of miRNAs and lncRNAs for performing meaningful analysis.

The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution

The draft genome of the moss model, Physcomitrella patens, comprised approximately 2000 unordered scaffolds. In order to enable analyses of genome structure and evolution we generated a chromosome-scale genome assembly using genetic linkage as well as (end) sequencing of long DNA fragments. We find that 57% of the genome comprises transposable elements (TEs), some of which may be actively transposing during the life cycle. Unlike in flowering plant genomes, gene- and TE-rich regions show an overall even distribution along the chromosomes. However, the chromosomes are mono-centric with peaks of a class of Copia elements potentially coinciding with centromeres. Gene body methylation is evident in 5.7% of the protein-coding genes, typically coinciding with low GC and low expression. Some giant virus insertions are transcriptionally active and might protect gametes from viral infection via siRNA mediated silencing. Structure-based detection methods show that the genome evolved via two rounds of whole genome duplications (WGDs), apparently common in mosses but not in liverworts and hornworts. Several hundred genes are present in colinear regions conserved since the last common ancestor of plants. These syntenic regions are enriched for functions related to plant-specific cell growth and tissue organization. The P. patens genome lacks the TE-rich pericentromeric and gene-rich distal regions typical for most flowering plant genomes. More non-seed plant genomes are needed to unravel how plant genomes evolve, and to understand whether the P. patens genome structure is typical for mosses or bryophytes.

Wild emmer genome architecture and diversity elucidate wheat evolution and domestication

Wheat (Triticum spp.) is one of the founder crops that likely drove the Neolithic transition to sedentary agrarian societies in the Fertile Crescent more than 10,000 years ago. Identifying genetic modifications underlying wheat's domestication requires knowledge about the genome of its allo-tetraploid progenitor, wild emmer (T. turgidum ssp. dicoccoides). We report a 10.1-gigabase assembly of the 14 chromosomes of wild tetraploid wheat, as well as analyses of gene content, genome architecture, and genetic diversity. With this fully assembled polyploid wheat genome, we identified the causal mutations in Brittle Rachis 1 (TtBtr1) genes controlling shattering, a key domestication trait. A study of genomic diversity among wild and domesticated accessions revealed genomic regions bearing the signature of selection under domestication. This reference assembly will serve as a resource for accelerating the genome-assisted improvement of modern wheat varieties.

The Sequenced Angiosperm Genomes and Genome Databases

Angiosperms, the flowering plants, provide the essential resources for human life, such as food, energy, oxygen, and materials. They also promoted the evolution of human, animals, and the planet earth. Despite the numerous advances in genome reports or sequencing technologies, no review covers all the released angiosperm genomes and the genome databases for data sharing. Based on the rapid advances and innovations in the database reconstruction in the last few years, here we provide a comprehensive review for three major types of angiosperm genome databases, including databases for a single species, for a specific angiosperm clade, and for multiple angiosperm species. The scope, tools, and data of each type of databases and their features are concisely discussed. The genome databases for a single species or a clade of species are especially popular for specific group of researchers, while a timely-updated comprehensive database is more powerful for address of major scientific mysteries at the genome scale. Considering the low coverage of flowering plants in any available database, we propose construction of a comprehensive database to facilitate large-scale comparative studies of angiosperm genomes and to promote the collaborative studies of important questions in plant biology.

Bioinformatics in the plant genomic and phenomic domain: The German contribution to resources, services and perspectives

Plant genetic resources are a substantial opportunity for plant breeding, preservation and maintenance of biological diversity. As part of the German Network for Bioinformatics Infrastructure (de.NBI) the German Crop BioGreenformatics Network (GCBN) focuses mainly on crop plants and provides both data and software infrastructure which are tailored to the needs of the plant research community. Our mission and key objectives include: (1) provision of transparent access to germplasm seeds, (2) the delivery of improved workflows for plant gene annotation, and (3) implementation of bioinformatics services that link genotypes and phenotypes. This review introduces the GCBN's spectrum of web-services and integrated data resources that address common research problems in the plant genomics community.

Sweet sorghum as biofuel feedstock: recent advances and available resources

Sweet sorghum is a promising target for biofuel production. It is a C4 crop with low input requirements and accumulates high levels of sugars in its stalks. However, large-scale planting on marginal lands would require improved varieties with optimized biofuel-related traits and tolerance to biotic and abiotic stresses. Considering this, many studies have been carried out to generate genetic and genomic resources for sweet sorghum. In this review, we discuss various attributes of sweet sorghum that make it an ideal candidate for biofuel feedstock, and provide an overview of genetic diversity, tools, and resources available for engineering and/or marker-assisting breeding of sweet sorghum. Finally, the progress made so far, in identification of genes/quantitative trait loci (QTLs) important for agronomic traits and ongoing molecular breeding efforts to generate improved varieties, has been discussed.

Massive expansion and differential evolution of small heat shock proteins with wheat (Triticum aestivum L.) polyploidization

Wheat (Triticum aestivum), one of the world’s most important crops, is facing unprecedented challenges due to global warming. To evaluate the gene resources for heat adaptation in hexaploid wheat, small heat shock proteins (sHSPs), the key plant heat protection genes, were comprehensively analysed in wheat and related species. We found that the sHSPs of hexaploid wheat were massively expanded in A and B subgenomes with intrachromosomal duplications during polyploidization. These expanded sHSPs were under similar purifying selection and kept the expressional patterns with the original copies. Generally, a strong purifying selection acted on the α-crystallin domain (ACD) and theoretically constrain conserved function. Meanwhile, weaker purifying selection and strong positive selection acted on the N-terminal region, which conferred sHSP flexibility, allowing adjustments to a wider range of substrates in response to genomic and environmental changes. Notably, in CI, CV, ER, MI and MII subfamilies, gene duplications, expression variations and functional divergence occurred before wheat polyploidization. Our results indicate the massive expansion of active sHSPs in hexaploid wheat may also provide more raw materials for evolving functional novelties and generating genetic diversity to face future global climate changes, and highlight the expansion of stress response genes with wheat polyploidization.