De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore.

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.

Functional genomics of Brassica napus: Progresses, challenges, and perspectives.

Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.

AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes.

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibility and chromosomal interaction, as well as genetic variation data from 24 501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Interaction between phenylpropane metabolism and oil accumulation in the developing seed of Brassica napus revealed by high temporal-resolution transcriptomes.

BACKGROUND: Brassica napus is an important oilseed crop providing high-quality vegetable oils for human consumption and non-food applications. However, the regulation between embryo and seed coat for the synthesis of oil and phenylpropanoid compounds remains largely unclear. RESULTS: Here, we analyzed the transcriptomes in developing seeds at 2-day intervals from 14 days after flowering (DAF) to 64 DAF. The 26 high-resolution time-course transcriptomes are clearly clustered into five distinct groups from stage I to stage V. A total of 2217 genes including 136 transcription factors, are specifically expressed in the seed and show high temporal specificity by being expressed only at certain stages of seed development. Furthermore, we analyzed the co-expression networks during seed development, which mainly included master regulatory transcription factors, lipid, and phenylpropane metabolism genes. The results show that the phenylpropane pathway is prominent during seed development, and the key enzymes in the phenylpropane metabolic pathway, including TT5, BAN, and the transporter TT19, were directly or indirectly related to many key enzymes and transcription factors involved in oil accumulation. We identified candidate genes that may regulate seed oil content based on the co-expression network analysis combined with correlation analysis of the gene expression with seed oil content and seed coat content. CONCLUSIONS: Overall, these results reveal the transcriptional regulation between lipid and phenylpropane accumulation during B. napus seed development. The established co-expression networks and predicted key factors provide important resources for future studies to reveal the genetic control of oil accumulation in B. napus seeds.

INSurVeyor: improving insertion calling from short read sequencing data.

Insertions are one of the major types of structural variations and are defined as the addition of 50 nucleotides or more into a DNA sequence. Several methods exist to detect insertions from next-generation sequencing short read data, but they generally have low sensitivity. Our contribution is two-fold. First, we introduce INSurVeyor, a fast, sensitive and precise method that detects insertions from next-generation sequencing paired-end data. Using publicly available benchmark datasets (both human and non-human), we show that INSurVeyor is not only more sensitive than any individual caller we tested, but also more sensitive than all of them combined. Furthermore, for most types of insertions, INSurVeyor is almost as sensitive as long reads callers. Second, we provide state-of-the-art catalogues of insertions for 1047 Arabidopsis Thaliana genomes from the 1001 Genomes Project and 3202 human genomes from the 1000 Genomes Project, both generated with INSurVeyor. We show that they are more complete and precise than existing resources, and important insertions are missed by existing methods.

Comparative transcriptome profiling reveals the multiple levels of crosstalk in phytohormone networks in Brassica napus.

Plant hormones are the intrinsic factors that control plant development. The integration of different phytohormone pathways in a complex network of synergistic, antagonistic and additive interactions has been elucidated in model plants. However, the systemic level of transcriptional responses to hormone crosstalk in Brassica napus is largely unknown. Here, we present an in-depth temporal-resolution study of the transcriptomes of the seven hormones in B. napus seedlings. Differentially expressed gene analysis revealed few common target genes that co-regulated (up- and down-regulated) by seven hormones; instead, different hormones appear to regulate distinct members of protein families. We then constructed the regulatory networks between the seven hormones side by side, which allowed us to identify key genes and transcription factors that regulate the hormone crosstalk in B. napus. Using this dataset, we uncovered a novel crosstalk between gibberellin and cytokinin in which cytokinin homeostasis was mediated by RGA-related CKXs expression. Moreover, the modulation of gibberellin metabolism by the identified key transcription factors was confirmed in B. napus. Furthermore, all data were available online from http://yanglab.hzau.edu.cn/BnTIR/hormone. Our study reveals an integrated hormone crosstalk network in Brassica napus, which also provides a versatile resource for future hormone studies in plant species.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding.

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

CottonMD: a multi-omics database for cotton biological study.

Cotton is an important economic crop, and many loci for important traits have been identified, but it remains challenging and time-consuming to identify candidate or causal genes/variants and clarify their roles in phenotype formation and regulation. Here, we first collected and integrated the multi-omics datasets including 25 genomes, transcriptomes in 76 tissue samples, epigenome data of five species and metabolome data of 768 metabolites from four tissues, and genetic variation, trait and transcriptome datasets from 4180 cotton accessions. Then, a cotton multi-omics database (CottonMD, http://yanglab.hzau.edu.cn/CottonMD/) was constructed. In CottonMD, multiple statistical methods were applied to identify the associations between variations and phenotypes, and many easy-to-use analysis tools were provided to help researchers quickly acquire the related omics information and perform multi-omics data analysis. Two case studies demonstrated the power of CottonMD for identifying and analyzing the candidate genes, as well as the great potential of integrating multi-omics data for cotton genetic breeding and functional genomics research.

Comparison of dynamic 3D chromatin architecture uncovers heterosis for leaf size in Brassica napus.

INTRODUCTION: Heterosis is the major event driving plant development and promoting crop breeding, but the molecular bases for this phenomenon remain elusive. OBJECTIVES: We aim to explore the effect of three-dimensional (3D) chromatin architecture on the underlying mechanism of heterosis. METHODS: Here, we constructed the North Carolina II (NC-II) population to select superior and inferior heterosis sets by comparing mid-parent heterosis (MPH) in Brassica napus. To decipher the impact of 3D chromatin architecture on the underlying mechanism of heterosis, we combined genetics, transcriptomics and 3D genomics approaches. RESULTS: We suggest that F1 hybrids with superior heterosis tend to contain more transcriptionally active A compartments compared with F1 hybrids with inferior heterosis, and approximately 19-21% compartment significantly altered in the F1 hybrids relative to the parental lines. Further analyses show that chromatin compartments correlate with genetic variance among parents, which may form the basis for differentially active chromatin compartments. Having more A compartments in F1 hybrids confers a more accessible chromatin circumstance, which promotes a higher proportion of highly expressed ELD (expression level dominance) genes in superior heterosis F1 hybrids (46-64%) compared with inferior heterosis F1 hybrids (22-31%). Moreover, genes related to hormones which affect plant growth, are more up-regulated with changes of 3D genome architecture, and we validate that increased hormone content contributes to cell proliferation and expansion by influencing the key genes of cell cycle thereby promoting leaf size. CONCLUSION: Dynamic 3D chromatin architecture correlates with genetic variance among parents and contributes to heterosis in Brassica napus.

Differential m6A RNA landscapes across hematopoiesis reveal a role for IGF2BP2 in preserving hematopoietic stem cell function.

N6-methyladenosine (m6A) on mRNA plays critical roles in various cellular processes. However, the landscape and dynamics of m6A modification in hematopoietic system remain unknown. Here, we delineate a comprehensive m6A landscape across hematopoietic hierarchy and uncover that IGF2BP2 is required for preserving the function of hematopoietic stem cells (HSCs). Our data reveal a cell-type-specific m6A landscape in hematopoiesis. m6A modifications arise mostly in the early stage of hematopoiesis and prefer to play distinct roles for determining mRNA fates in HSCs and committed progenitors. Mechanistically, increased m6A-IGF2BP2 expression controls transcriptional state and maintenance of HSCs. IGF2BP2 deficiency induces quiescence loss and impairs HSC function. Moreover, IGF2BP2 loss increases mitochondrial activity of HSCs by accelerating Bmi1 mRNA decay, leading to de-repression of mitochondria-related genes. Collectively, our results present a fascinating portrait of m6A modification of hematopoietic hierarchy and reveal a key role of IGF2BP2 in maintaining HSC function by restraining mitochondrial activity.

Calling large indels in 1047 Arabidopsis with IndelEnsembler.

Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap.