PlantRep: a database of plant repetitive elements

Key message We re-annotated repeats of 459 plant genomes and released a new database: PlantRep (http://www.plantrep.cn/). PlantRep sheds lights of repeat evolution and provides fundamental data for deep exploration of genome.

Concerted expansion and contraction of immune receptor gene repertoires in plant genomes

Recent reports suggest that cell-surface and intracellular immune receptors function synergistically to activate robust defence against pathogens, but whether or not they co-evolve is unclear. Here we determined the copy numbers of cell-surface and intracellular immune receptors in 208 species. Surprisingly, these receptor gene families contract and/or expand together in plant genomes, suggesting the mutual potentiation of immunity initiated by cell-surface and intracellular receptors is reflected in the concerted co-evolution of the size of their repertoires across plant species.

Scripting Analyses of Genomes in Ensembl Plants

Ensembl Plants (http://plants.ensembl.org) offers genome-scale information for plants, with four releases per year. As of release 47 (April 2020) it features 79 species and includes genome sequence, gene models, and functional annotation. Comparative analyses help reconstruct the evolutionary history of gene families, genomes, and components of polyploid genomes. Some species have gene expression baseline reports or variation across genotypes. While the data can be accessed through the Ensembl genome browser, here we review specifically how our plant genomes can be interrogated programmatically and the data downloaded in bulk. These access routes are generally consistent across Ensembl for other non-plant species, including plant pathogens, pests, and pollinators.

Review on the Development and Applications of Medicinal Plant Genomes

With the development of sequencing technology, the research on medicinal plants is no longer limited to the aspects of chemistry, pharmacology, and pharmacodynamics, but reveals them from the genetic level. As the price of next-generation sequencing technology becomes affordable, and the long-read sequencing technology is established, the medicinal plant genomes with large sizes have been sequenced and assembled more easily. Although the review of plant genomes has been reported several times, there is no review giving a systematic and comprehensive introduction about the development and application of medicinal plant genomes that have been reported until now. Here, we provide a historical perspective on the current situation of genomes in medicinal plant biology, highlight the use of the rapidly developing sequencing technologies, and conduct a comprehensive summary on how the genomes apply to solve the practical problems in medicinal plants, like genomics-assisted herb breeding, evolution history revelation, herbal synthetic biology study, and geoherbal research, which are important for effective utilization, rational use and sustainable protection of medicinal plants.

Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data

The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail.

The 3D architecture of the pepper (Capsicum annum) genome and its relationship to function and evolution

The architecture of topologically associating domains (TADs) varies across plant genomes. Understanding the functional consequences of this diversity requires insights into the pattern, structure, and function of TADs. Here, we present a comprehensive investigation of the 3D genome organization of pepper (Capsicum annuum) and its association with gene expression and genomic variants. We report the first chromosome-scale long-read genome assembly of pepper and generate Hi-C contact maps for four tissues. The contact maps indicate that 3D structure varies somewhat across tissues, but generally the genome was segregated into subcompartments that were correlated with transcriptional state. In addition, chromosomes were almost continuously spanned by TADs, with the most prominent found in large genomic regions that were rich in retrotransposons. A substantial fraction of TAD boundaries were demarcated by chromatin loops, suggesting loop extrusion is a major mechanism for TAD formation; many of these loops were bordered by genes, especially in highly repetitive regions, resulting in gene clustering in three dimensional space. Integrated analysis of Hi-C profiles and transcriptomes showed that change in 3D chromatin structures (e.g. subcompartments, TADs, and loops) was not the primary mechanism contributing to differential gene expression between tissues, but chromatin structure does play a role in transcription stability. TAD boundaries were significantly enriched for breaks of synteny and depletion of sequence variation, suggesting that TADs constrain patterns of genome structural evolution in plants. Together, our work provides insights into principles of 3D genome folding in large plant genomes and its association with function and evolution.

DeepPlnc: Discovering plant lncRNAs through multimodal deep learning on sequential data

Various noncoding elements of genome have gained attention for their regulatory roles where the lncRNAs are very recent and most intriguing for their possible functions. Due to limited information about lncRNAs, their characterization remains a big challenge, especially in plants. Plant lncRNAs differ a lot from others even in the mode of transcription and display poor sequence conservation. Scarce resources exist to annotate for lncRNAs with satisfactory reliability. Here, we present a deep learning approach-based software, DeepPlnc, to accurately identify plant lncRNAs across the plant genomes. DeepPlnc, unlike most of the existing software, can even accurately annotate the incomplete length transcripts also which are very common in de novo assembled transcriptomes. It has incorporated a bi-modal architecture of Convolution Neural Nets while extracting information from the sequences of nucleotides and secondary structure representations for plant lncRNAs. DeepPlnc scored high on all the considered performance metrics while breaching the average accuracy of >95% when tested across different experimentally validated datasets. The software was comprehensively benchmarked against some of the recently published tools to identify the plant lncRNAs where it consistently outperformed all the compared tools for all the performance metrics and for all the considered benchmarking datasets. DeepPlnc will be an important resource for reference free identification and annotation of transcriptome and genome for lncRNAs in plants. DeepPlnc has been made freely available as a web-server at https://scbb.ihbt.res.in/DeepPlnc/. Besides this, a stand-alone version is also provided at GitHub at https://github.com/SCBB-LAB/DeepPlnc/.