The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.

The NGS (next generation sequencing)-based metagenomic data analysis is becoming the mainstream for the study of microbial communities. Faced with a large amount of data in metagenomic research, effective data visualization is important for scientists to effectively explore, interpret and manipulate such rich information. The visualization of the metagenomic data, especially multi-sample data, is one of the most critical challenges. The different data sample sources, sequencing approaches and heterogeneous data formats make robust and seamless data visualization difficult. Moreover, researchers have different focuses on metagenomic studies: taxonomical or functional, sample-centric or genome-centric, single sample or multiple samples, etc. However, current efforts in metagenomic data visualization cannot fulfill all of these needs, and it is extremely hard to organize all of these visualization effects in a systematic manner. An extendable, interactive visualization tool would be the method of choice to fulfill all of these visualization needs. In this paper, we have present MetaSee, an extendable toolbox that facilitates the interactive visualization of metagenomic samples of interests. The main components of MetaSee include: (I) a core visualization engine that is composed of different views for comparison of multiple samples: Global view, Phylogenetic view, Sample view and Taxa view, as well as link-out for more in-depth analysis; (II) front-end user interface with real metagenomic models that connect to the above core visualization engine and (III) open-source portal for the development of plug-ins for MetaSee. This integrative visualization tool not only provides the visualization effects, but also enables researchers to perform in-depth analysis of the metagenomic samples of interests. Moreover, its open-source portal allows for the design of plug-ins for MetaSee, which would facilitate the development of any additional visualization effects.

Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Leaf rust, caused by Puccinia triticina Eriksson (Pt), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47, a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides. Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease.

Finding causal relationships between genotypic and phenotypic variation is a key focus of evolutionary biology, human genetics and plant breeding. To identify genome-wide patterns underlying trait diversity, we assembled a high-quality reference genome of Cardamine hirsuta, a close relative of the model plant Arabidopsis thaliana. We combined comparative genome and transcriptome analyses with the experimental tools available in C. hirsuta to investigate gene function and phenotypic diversification. Our findings highlight the prevalent role of transcription factors and tandem gene duplications in morphological evolution. We identified a specific role for the transcriptional regulators PLETHORA5/7 in shaping leaf diversity and link tandem gene duplication with differential gene expression in the explosive seed pod of C. hirsuta. Our work highlights the value of comparative approaches in genetically tractable species to understand the genetic basis for evolutionary change.

Structural differences between genomes are a major source of genetic variation that contributes to phenotypic differences. Transposable elements, mobile genetic sequences capable of increasing their copy number and propagating themselves within genomes, can generate structural variation. However, their repetitive nature makes it difficult to characterize fine-scale differences in their presence at specific positions, limiting our understanding of their impact on genome variation. Domesticated maize is a particularly good system for exploring the impact of transposable element proliferation as over 70% of the genome is annotated as transposable elements. High-quality transposable element annotations were recently generated for de novo genome assemblies of 26 diverse inbred maize lines. We generated base-pair resolved pairwise alignments between the B73 maize reference genome and the remaining 25 inbred maize line assemblies. From this data, we classified transposable elements as either shared or polymorphic in a given pairwise comparison. Our analysis uncovered substantial structural variation between lines, representing both simple and complex connections between TEs and structural variants. Putative insertions in SNP depleted regions, which represent recently diverged identity by state blocks, suggest some TE families may still be active. However, our analysis reveals that within these recently diverged genomic regions, deletions of transposable elements likely account for more structural variation events and base pairs than insertions. These deletions are often large structural variants containing multiple transposable elements. Combined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences.

Earliness and ripening behavior are important attributes of fruits on and off the vine, and affect quality and preference of both growers and consumers. Fruit ripening is a complex physiological process that involves metabolic shifts affecting fruit color, firmness, and aroma production. Melon is a promising model crop for the study of fruit ripening, as the full spectrum of climacteric behavior is represented across the natural variation. Using Recombinant Inbred Lines (RILs) population derived from the parental lines "Dulce" (reticulatus, climacteric) and "Tam Dew" (inodorus, non-climacteric) that vary in earliness and ripening traits, we mapped QTLs for ethylene emission, fruit firmness and days to flowering and maturity. To further annotate the main QTL intervals and identify candidate genes, we used Oxford Nanopore long-read sequencing in combination with Illumina short-read resequencing, to assemble the parental genomes de-novo. In addition to 2.5 million genome-wide SNPs and short InDels detected between the parents, we also highlight here the structural variation between these lines and the reference melon genome. Through systematic multi-layered prioritization process, we identified 18 potential polymorphisms in candidate genes within multi-trait QTLs. The associations of selected SNPs with earliness and ripening traits were further validated across a panel of 177 diverse melon accessions and across a diallel population of 190 F1 hybrids derived from a core subset of 20 diverse parents. The combination of advanced genomic tools with diverse germplasm and targeted mapping populations is demonstrated as a way to leverage forward genetics strategies to dissect complex horticulturally important traits.

Plants recognize surrounding microbes by sensing microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). Despite their significance for microbial control, the evolution of PTI responses remains largely uncharacterized. Here, by employing comparative transcriptomics of six Arabidopsis thaliana accessions and three additional Brassicaceae species to investigate PTI responses, we identified a set of genes that commonly respond to the MAMP flg22 and genes that exhibit species-specific expression signatures. Variation in flg22-triggered transcriptome responses across Brassicaceae species was incongruent with their phylogeny, while expression changes were strongly conserved within A. thaliana. We found the enrichment of WRKY transcription factor binding sites in the 5'-regulatory regions of conserved and species-specific responsive genes, linking the emergence of WRKY-binding sites with the evolution of gene expression patterns during PTI. Our findings advance our understanding of the evolution of the transcriptome during biotic stress.

Thousands of species will be sequenced in the next few years; however, understanding how their genomes work, without an unlimited budget, requires both molecular and novel evolutionary approaches. We developed a sensitive sequence alignment pipeline to identify conserved noncoding sequences (CNSs) in the Andropogoneae tribe (multiple crop species descended from a common ancestor ∼18 million years ago). The Andropogoneae share similar physiology while being tremendously genomically diverse, harboring a broad range of ploidy levels, structural variation, and transposons. These contribute to the potential of Andropogoneae as a powerful system for studying CNSs and are factors we leverage to understand the function of maize CNSs. We found that 86% of CNSs were comprised of annotated features, including introns, UTRs, putative cis-regulatory elements, chromatin loop anchors, noncoding RNA (ncRNA) genes, and several transposable element superfamilies. CNSs were enriched in active regions of DNA replication in the early S phase of the mitotic cell cycle and showed different DNA methylation ratios compared to the genome-wide background. More than half of putative cis-regulatory sequences (identified via other methods) overlapped with CNSs detected in this study. Variants in CNSs were associated with gene expression levels, and CNS absence contributed to loss of gene expression. Furthermore, the evolution of CNSs was associated with the functional diversification of duplicated genes in the context of maize subgenomes. Our results provide a quantitative understanding of the molecular processes governing the evolution of CNSs in maize.

Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.

Millions of species are currently being sequenced, and their genomes are being compared. Many of them have more complex genomes than model systems and raise novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication. Here, we introduce Anchored Wavefront alignment (AnchorWave), which performs whole-genome duplication-informed collinear anchor identification between genomes and performs base pair-resolved global alignment for collinear blocks using a two-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multikilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs. By contrast, other genome alignment tools showed low power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome as position matches or indels than the closest competitive approach when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor-binding sites at a rate of 1.05- to 74.85-fold higher than other tools with significantly lower false-positive alignments. AnchorWave complements available genome alignment tools by showing obvious improvement when applied to genomes with dispersed repeats, active TEs, high sequence diversity, and whole-genome duplication variation.

Short insertions, deletions (INDELs) and larger structural variants have been increasingly employed in genetic association studies, but few improvements over SNP-based association have been reported. In order to understand why this might be the case, we analysed two publicly available datasets and observed that 63% of INDELs called in A. thaliana and 64% in D. melanogaster populations are misrepresented as multiple alleles with different functional annotations, i.e. where the same underlying variant is represented by inconsistent alignments leading to different variant calls. To address this issue, we have developed the software Irisas to reclassify and re-annotate these variants, which we then used for single-locus tests of association. We also integrated them to predict the functional impact of SNPs, INDELs, and structural variants for burden testing. Using both approaches, we re-analysed the genetic architecture of complex traits in A. thaliana and D. melanogaster. Heritability analysis using SNPs alone explained on average 27% and 19% of phenotypic variance for A. thaliana and D. melanogaster respectively. Our method explained an additional 11% and 3%, respectively. We also identified novel trait loci that previous SNP-based association studies failed to map, and which contain established candidate genes. Our study shows the value of the association test with INDELs and integrating multiple types of variants in association studies in plants and animals.

Transcriptome profiles have been widely captured using short-read sequencing technology, but there are still limitations partially due to the read length. Here, we generated long reads using Oxford Nanopore PromethION™ technology and short reads using the Illumina sequencing platform to study the transcriptome of root, stem, and leaf of Camellia sinensis cv. Fudingdabai. We mapped the Nanopore reads to the Shuchazao of C. sinensis genome sequence, and the mapping rates ranged from 82.63% to 90.59% (average 86.44%); this is lower than that of the Illumina reads which was 87.83% to 91.14% (average 90.12%). Gene expression level was quantified using the Nanopore and Illumina data and we observed a good agreement. The same tea leaf flavor synthesis pathways were highlighted using both sequencing technologies when analyzing the differentially expressed genes between leaf and root. Alternative splicing was then analyzed, and the intron-retention was observed as the most common alternative splicing. Moreover Nanopore long reads could correct transcript isoform annotation for differential expression investigation purposes. Nanopore sequencing techniques can provide a novel reference basis for molecular analysis of tea plants.

We study natural DNA polymorphisms and associated phenotypes in the Arabidopsis relative Cardamine hirsuta. We observed strong genetic differentiation among several ancestry groups and broader distribution of Iberian relict strains in European C. hirsuta compared to Arabidopsis. We found synchronization between vegetative and reproductive development and a pervasive role for heterochronic pathways in shaping C. hirsuta natural variation. A single, fast-cycling ChFRIGIDA allele evolved adaptively allowing range expansion from glacial refugia, unlike Arabidopsis where multiple FRIGIDA haplotypes were involved. The Azores islands, where Arabidopsis is scarce, are a hotspot for C. hirsuta diversity. We identified a quantitative trait locus (QTL) in the heterochronic SPL9 transcription factor as a determinant of an Azorean morphotype. This QTL shows evidence for positive selection, and its distribution mirrors a climate gradient that broadly shaped the Azorean flora. Overall, we establish a framework to explore how the interplay of adaptation, demography, and development shaped diversity patterns of 2 related plant species.