Exploring the metabolic characteristics of different plant organs and tissues at a spatial level can help us to better understand the functional mechanisms of biological tissues and cells. Mass spectrometry imaging (MSI) provides a reliable tool for this purpose. However, its application for high-resolution metabolic mapping across various plant organs remains a significant challenge due to the intrinsic biological properties of plant samples and unfavorable analysis conditions. This study aimed to develop a novel MSI platform that can expand more diverse plant samples in spatial metabolomics research and enhance the detection efficiency of plant metabolites. The platform (AMG–LDI–MSI) based on an Au nanoparticles-loaded MoS₂ and doped graphene oxide (Au@MoS₂/GO) flexible film substrate combined with laser desorption/ionization (LDI)–MSI was established to enhance the detection and visualization of metabolites in various plant tissues. It has a non-sectioning, matrix-free, dual-ion mode imaging strategy, enabling high-throughput detection of metabolites and high-resolution molecular imaging within a micrometer scale. The Au@MoS₂/GO as a new substrate can offer high sensitivity and molecular coverage for diverse plant metabolites (10 classes) under the positive and negative ion modes. Moreover, the AMG–LDI–MSI platform overcomes the limitations of plant tissues (e.g., fragile leaf, water-rich fruit, or lignified roots) for in situ imaging. We successfully applied the platform to map the metabolite spatial dynamics in different types of fresh tissues (rhizome, main root, branch root, fruit, leaf, and root nodule) from medicinal plants, obtained the high-quality mass spectral imaging data, and demonstrated the universality and applicability of the platform to multiple plant tissues. These results demonstrate the significant advantages of enhancing the detection of multiple tissue metabolites in plants and their high-resolution imaging. It has overcome the limitations of previously reported MSI methods, suggesting that it could become a widely used tool for deciphering metabolic networks in plant biology.

The data that support the findings of this study are available in the main text and Supporting Information of this article.

The burgeoning multi-omics data have provided deep insights into the regulatory mechanisms underlying plant growth and development. However, revealing the complete landscape of gene regulatory networks underpinning various developmental processes remains challenging. Here, a multi-omics integrative gene network of the pear fruit development process was constructed through integrating 3D genomic, transcriptomic, transcription factor (TF) binding, chromatin accessibility, protein structure, and proteomic data. This integrative network comprises over 45,678 elements interconnected by more than 3.15 million edges and exhibits great potential in predicting regulatory and interactive relationships involved in the formation of key fruit quality traits (e.g., sugar, stone cell). In particular, the integrative network was applied to predict interactors of PbrII5, an inhibitor of vacuolar sucrose hydrolysis, and the predicted interactors were further validated through molecular experiments. Moreover, the network showed good performance in automatically predicting fruit trait-related genes by leveraging machine learning models. Specifically, a set of sugar metabolism-related genes was newly predicted, and their functions were verified through overexpression in pear fruit. In addition, extensive regulatory network divergence was observed between duplicated genes, with neofunctionalization being the dominant evolutionary process reshaping network connections of duplicated genes. Lastly, a multi-omics network database, pearGRN (http://peargrn.njau.edu.cn), was developed to facilitate further research for resolving complex gene regulatory relationships. This study lays a strong foundation for revealing novel regulatory mechanisms underlying fruit development and quality formation.

The raw Hi-C and ATAC-seq data have been deposited in the National Genomics Data Center (NGDC, https://www.cncb.ac.cn/?lang=en) under BioProject accession PRJCA020047 with CRA accession CRA012754. The clean lncRNA-seq, circRNA-seq, and the raw sRNA-seq data have been deposited in NGDC under BioProject accession PRJCA029491 with CRA accession CRA018572. The code and scripts used in this study have been deposited on GitHub at https://github.com/lihongxiang97/Pear-Integrative-Network.

Alkaloids, renowned for their pivotal physiological roles in plant defense and chemical medium, constitute a structurally diverse class of bioactive natural products with substantial therapeutic potential in modern drug development. There is currently no dedicated alkaloid database, highlighting an urgent need for such a resource. Here, we present AlkaPlorer (https://alkaplorer.qmclab.com/), the first systematic alkaloid database, which has compiled over 130,000 alkaloids from 12,250 species, with reported activity against 6,583 biological targets. AlkaPlorer not only integrates comprehensive experimentally validated data and computationally predicted properties for each alkaloid, but also establishes standardized notation and associations among various data elements, forming a correlative-type dataset. Extensive chemoinformatic analyses on structural scaffolds, biosynthetic precursors, physicochemical properties, and phylogenetic distributions across plant taxa are performed based on AlkaPlorer, providing new insights into the chemical diversity, structural evolution, and biosynthetic regularity of plant alkaloids. AlkaPlorer enables easy access and efficient retrieval and provides a foundational resource for AI-driven applications in plant metabolism and alkaloid research.

All structural data can be accessed through the Data page of AlkaPlorer (https://alkaplorer.qmclab.com/data).

Transcriptome deep sequencing (RNA-seq) data analysis is often affected by reference bias introduced by the use of a single linear reference (SLR) genome. Graph-based pangenomes can mitigate this bias by integrating the SLR genome with complex genetic variations within a species; however, their application remains limited owing to a lack of dedicated analytical tools. Here, we present PanGraphRNA, an integrated bioinformatics platform for RNA-seq data analysis using a graph pangenome as reference. Built on the Galaxy web-based framework, PanGraphRNA provides functional modules for constructing, evaluating, and applying graph pangenomes across different population scales, thus enabling accessibility, traceability, and reproducibility throughout the analysis. Applied to both real and simulated RNA-seq data sets from Arabidopsis (Arabidopsis thaliana), PanGraphRNA outperformed the SLR approach, achieving higher read alignment accuracy and more precise gene expression quantification. PanGraphRNA enabled the identification of drought stress-induced genes and flowering time-related quantitative trait loci that were previously missed with the conventional SLR approach. Furthermore, we successfully applied PanGraphRNA to process RNA-seq data sets from rice (Oryza sativa) and maize (Zea mays). By providing standardized, containerized workflows, PanGraphRNA will facilitate transcriptomic research in key plant species, including Arabidopsis, rice, and maize.

The PanGraphRNA project is available at GitHub (https://github.com/cma2015/PanGraphRNA). Scripts to reproduce the analysis in this study are also available from the project webpage. The RNA-seq data generated in this study have been deposited in the National Genomics Data Center (CNCB-NGDC Members and Partners, 2022) under project accession numbers PRJCA058957, and are publicly accessible at https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA058957. Additionally, the pre-built graph pangenomes for three major plant species (Arabidopsis, rice, and maize) are openly available on figshare (https://figshare.com/projects/PanGraphRNA_-_Graph_pangenome_index/252995).

Deep learning-based genomic prediction (DL-based GP) has shown promising performance compared to traditional GP methods in plant breeding, particularly in handling large, complex multi-omics data sets. However, the effective development and widespread adoption of DL-based GP still face substantial challenges, including the need for large, high-quality data sets, inconsistencies in performance benchmarking, and the integration of environmental factors. Here, we summarize the key obstacles impeding the development of DL-based GP models and propose future developing directions, such as modular approaches, data augmentation, and advanced attention mechanisms.

In recent decades, research on model organisms has significantly increased our understanding of core biological processes in plant science. However, this focus has created a substantial knowledge bottleneck due to the limited phylogenetic and ecological spectrum covered. Gymnosperms, especially conifers, represent a molecular and ecological diversity hotspot among seed plants. Despite their importance, research on these species is notably underrepresented, primarily due to a slower pace of investigation resulting from a lack of community-based resources and databases. To fill this gap, we developed the P(inus)ra(diata)-G(ene)E(xpression) (Pra-GE)-ATLAS, which consists of several tools and two main modules: transcriptomics and proteomics, presented in this work for the forestry commercial and stress-sensitive species Pinus radiata. We have summarized and centralized all the available information to provide a comprehensive view of the gene expression landscape. To illustrate how applications of the database lead to new biological insights, we have integrated multiple regulatory layers across tissues and stressors. While stress favors the retention of small introns, harmonized alternative splicing analyses reveal that genes with conifers' iconic large introns tend to be under constitutive regulation. Furthermore, the degree of convergence between stressors differed between regulatory layers, with proteomic responses remaining highly distinctive even through intergenerational memory tolerance. Overall, the Pra-GE-ATLAS aims to narrow the distance between angiosperms and gymnosperms resources, deepening our understanding of how characteristic pine features have evolved. Pra-GE-ATLAS DB is available at: http://pra-ge-atlas.valmei.es.

All the data generated in this study are available at the Pra-GE-ATLAS database https://rocesv.github.io/Pra-GE-ATLAS and https://doi.org/10.5281/zenodo.10494507. The code used in this work is available at https://github.com/RocesV/Pra-GE-ATLAS_manuscript. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD047869.

The Sapindaceae family, encompassing a wide range of plant forms such as herbs, vines, shrubs, and trees, is widely distributed across tropical and subtropical regions. This family includes economically important crops like litchi, longan, rambutan, and ackee. With the wide application of genomic technologies in recent years, several Sapindaceae plant genomes have been decoded, leading to an accumulation of substantial omics data in this field. This surge in data highlights the pressing need for a unified genomic data center capable of storing, sharing, and analyzing these data. Here, we introduced SapBase, that is, the Sapindaceae Genome Database. SapBase houses seven published plant genomes alongside their corresponding gene structure and functional annotations, small RNA annotations, gene expression profiles, gene pathways, and synteny block information. It offers user-friendly features for gene information mining, co-expression analysis, and inter-species comparative genomic analysis. Furthermore, we showcased SapBase's extensive capacities through a detailed bioinformatic analysis of a MYB gene in litchi. Thus, SapBase could serve as an integrative genomic resource and analysis platform for the scientific exploration of Sapinaceae species and their comparative studies with other plants.

Pineapple is the third most crucial tropical fruit worldwide and available in five varieties. Genomes of different pineapple varieties have been released to date; however, none of them are complete, with all exhibiting substantial gaps and representing only two of the five pineapple varieties. This significantly hinders the advancement of pineapple breeding efforts. In this study, we sequenced the genomes of three varieties: a wild pineapple variety, a fiber pineapple variety, and a globally cultivated edible pineapple variety. We constructed the first gap-free reference genome (Ref) for pineapple. By consolidating multiple sources of evidence and manually revising each gene structure annotation, we identified 26,656 protein-coding genes. The BUSCO evaluation indicated a completeness of 99.2%, demonstrating the high quality of the gene structure annotations in this genome. Utilizing these resources, we identified 7,209 structural variations across the three varieties. Approximately 30.8% of pineapple genes were located within ±5 kb of structural variations, including 30 genes associated with anthocyanin synthesis. Further analysis and functional experiments demonstrated that the high expression of AcMYB528 aligns with the accumulation of anthocyanins in the leaves, both of which may be affected by a 1.9-kb insertion fragment. In addition, we developed the Ananas Genome Database, which offers data browsing, retrieval, analysis, and download functions. The construction of this database addresses the lack of pineapple genome resource databases. In summary, we acquired a seamless pineapple reference genome with high-quality gene structure annotations, providing a solid foundation for pineapple genomics and a valuable reference for pineapple breeding.

All raw sequencing data of HiFi, Hi-C, Nanopore and CUT&Tag are available at the National Center for Biotechnology Information database with a project ID PRJNA1049071. The genomes of six haplotypes and Ref, along with their annotations, have been deposited in the Pineapple Genome Database (https://ananas.watchbio.cn/).

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Whole-genome genotyping methods are important for breeding. However, it has been challenging to develop a robust method for simultaneous foreground and background genotyping that can easily be adapted to different genes and species. In our study, we accidently discovered that in adapter ligation-mediated PCR, the amplification by primer-template mismatched annealing (PTMA) along the genome could generate thousands of stable PCR products. Based on this observation, we consequently developed a novel method for simultaneous foreground and background integrated genotyping by sequencing (FBI-seq) using one specific primer, in which foreground genotyping is performed by primer-template perfect annealing (PTPA), while background genotyping employs PTMA. Unlike DNA arrays, multiple PCR, or genome target enrichments, FBI-seq requires little preliminary work for primer design and synthesis, and it is easily adaptable to different foreground genes and species. FBI-seq therefore provides a prolific, robust, and accurate method for simultaneous foreground and background genotyping to facilitate breeding in the post-genomics era.

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, under accession number CRA004651 that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. The source code for the tags detection pipeline is available at https://github.com/dashengzhao/FBI-seq.

Protein kinases regulate virtually all cellular processes, but it remains challenging to determine the functions of all protein kinases, collectively called the “kinome”, in any species. We developed a computational approach called EXPLICIT-Kinase to predict the functions of the Arabidopsis kinome. Because the activities of many kinases can be regulated transcriptionally, their gene expression patterns provide clues to their functions. A universal gene expression predictor for Arabidopsis was constructed to predict the expression of 30,172 non-kinase genes based on the expression of 994 kinases. The model reconstituted highly accurate transcriptomes for diverse Arabidopsis samples. It identified the significant kinases as predictor kinases for predicting the expression of Arabidopsis genes and pathways. Strikingly, these predictor kinases were often regulators of related pathways, as exemplified by those involved in cytokinesis, tissue development, and stress responses. Comparative analyses revealed that portions of these predictor kinases are shared and conserved between Arabidopsis and maize. As an example, we identified a conserved predictor kinase, RAF6, from a stomatal movement module. We verified that RAF6 regulates stomatal closure. It can directly interact with SLAC1, a key anion channel for stomatal closure, and modulate its channel activity. Our approach enables a systematic dissection of the functions of the Arabidopsis kinome.

Transcriptome deep sequencing (RNA-seq) has become a routine method for global gene expression profiling. However, its application to large-scale experiments remains limited by cost and labor constraints. Here we describe a massively parallel 3′ end RNA-seq (MP3RNA-seq) method that introduces unique sample barcodes during reverse transcription to permit sample pooling immediately following this initial step. MP3RNA-seq allows for handling of hundreds of samples in a single experiment, at a cost of about $6 per sample for library construction and sequencing. MP3RNA-seq is effective for not only high-throughput gene expression profiling, but also genotyping. To demonstrate its utility, we applied MP3RNA-seq to 477 double haploid lines of maize. We identified 19,429 genes expressed in at least 50% of the lines and 35,836 high-quality single nucleotide polymorphisms for genotyping analysis. Armed with these data, we performed expression and agronomic trait quantitative trait locus (QTL) mapping and identified 25,797 expression QTLs for 15,335 genes and 21 QTLs for plant height, ear height, and relative ear height. We conclude that MP3RNA-seq is highly reproducible, accurate, and sensitive for high-throughput gene expression profiling and genotyping, and should be generally applicable to most eukaryotic species.

Lonicera japonica Thunb., a traditional Chinese herb, has been used for treating human diseases for thousands of years. Recently, the genome of L. japonica has been decoded, providing valuable information for research into gene function. However, no comprehensive database for gene functional analysis and mining is available for L. japonica. We therefore constructed LjaFGD (www.gzybioinformatics.cn/LjaFGD and bioinformatics.cau.edu.cn/LjaFGD), a database for analyzing and comparing gene function in L. japonica. We constructed a gene co-expression network based on 77 RNA-seq samples, and then annotated genes of L. japonica by alignment against protein sequences from public databases. We also introduced several tools for gene functional analysis, including Blast, motif analysis, gene set enrichment analysis, heatmap analysis, and JBrowse. Our co-expression network revealed that MYB and WRKY transcription factor family genes were co-expressed with genes encoding key enzymes in the biosynthesis of chlorogenic acid and luteolin in L. japonica. We used flavonol synthase 1 (LjFLS1) as an example to show the reliability and applicability of our database. LjaFGD and its various associated tools will provide researchers with an accessible platform for retrieving functional information on L. japonica genes to further biological discovery.

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Streptococcus canis Cas9 (ScCas9) is an RNA-guided endonuclease with NNG protospacer adjacent motif (PAM) specificity whose genome-editing activity in rice is locus-dependent. Here we investigated the performance of a ScCas9 variant named Sc⁺⁺ at different NNG PAM sites in the rice genome; Sc⁺⁺ harbors a T1227K mutation and the substitution of a positively charged loop (residues 367–376). Sc⁺⁺ nuclease achieved broader genome editing compared to the original ScCas9, and its nickase improved targeted base editing in transgenic rice plants. Using the high-efficiency adenine base editor rBE73b, we generated many new OsGS1 alleles suitable for screening of rice germplasm for potential herbicide resistance in the future. The CRISPR/Sc⁺⁺ system expands the genome-editing toolkit for rice.

Plant extracellular vesicles (EVs) play critical roles in the cross-kingdom trafficking of molecules from hosts to interacting microbes, most notably in plant defense responses. However, the isolation of pure, intact EVs from plants remains challenging. A variety of methods have been utilized to isolate plant EVs from apoplastic washing fluid (AWF). Here, we compare published plant EV isolation methods, and provide our recommended method for the isolation and purification of plant EVs. This method includes a detailed protocol for clean AWF collection from Arabidopsis thaliana leaves, followed by EV isolation via differential centrifugation. To further separate and purify specific subclasses of EVs from heterogeneous vesicle populations, density gradient ultracentrifugation and immunoaffinity capture are then utilized. We found that immunoaffinity capture is the most precise method for specific EV subclass isolation when suitable specific EV biomarkers and their corresponding antibodies are available. Overall, this study provides a guide for the selection and optimization of EV isolation methods for desired downstream applications.

Deciphering protein‐protein interactions (PPIs) is fundamental for understanding signal transduction pathways in plants. The split firefly luciferase (Fluc) complementation (SLC) assay has been widely used for analyzing PPIs. However, concern has risen about the bulky halves of Fluc interfering with the functions of their fusion partners. Nano luciferase (Nluc) is the smallest substitute for Fluc with improved stability and luminescence. Here, we developed a dual‐use system enabling the detection of PPIs through the Nluc‐based SLC and co‐immunoprecipitation assays. This was realized by coexpression of two proteins under investigation in fusion with the HA‐ or FLAG‐tagged Nluc halves, respectively. We validated the robustness of this system by reproducing multiple previously documented PPIs in protoplasts or Agrobacterium‐transformed plants. We next applied this system to evaluate the homodimerization of Arabidopsis CERK1, a coreceptor of fungal elicitor chitin, and its heterodimerization with other homologs in the absence or presence of chitin. Moreover, split fragments of Nluc were fused to two cytosolic ends of Arabidopsis calcium channels CNGC2 and CNGC4 to help sense the allosteric change induced by the bacterial elicitor flg22. Collectively, these results demonstrate the usefulness of the Nluc‐based SLC assay for probing constitutive or inducible PPIs and protein allostery in plant cells.

The extensive phenotypic diversity within natural populations of Arabidopsis is associated with differences in gene expression. Transcript levels can be considered as inheritable quantitative traits, and used to map expression quantitative trait loci (eQTL) in genome‐wide association studies (GWASs). In order to identify putative genetic determinants for variations in gene expression, we used publicly available genomic and transcript variation data from 665 Arabidopsis accessions and applied the single nucleotide polymorphism‐set (Sequence) Kernel Association Test (SKAT) method for the identification of eQTL. Moreover, we used the penalized orthogonal‐components regression (POCRE) method to increase the power of statistical tests. Then, gene annotations were used as test units to identify genes that are associated with natural variations in transcript accumulation, which correspond to candidate regulators, some of which may have a broad impact on gene expression. Besides increasing the chances to identify real associations, the analysis using POCRE and SKAT significantly reduced the computational cost required to analyze large datasets. As a proof of concept, we used this approach to identify eQTL that represent novel candidate regulators of immune responses. The versatility of this approach allows its application to any process that is subjected to natural variation among Arabidopsis accessions.

DNA markers play important roles in plant breeding and genetics. The Insertion/Deletion (InDel) marker is one kind of co-dominant DNA markers widely used due to its low cost and high precision. However, the canonical way of searching for InDel markers is time-consuming and labor-intensive. We developed an end-to-end computational solution (InDel Markers Development Platform, IMDP) to identify genome-wide InDel markers under a graphic pipeline environment. IMDP constitutes assembled genome sequences alignment pipeline (AGA-pipe) and next-generation re-sequencing data mapping pipeline (NGS-pipe). With AGA-pipe we are able to identify 12,944 markers between the genome of rice cultivars Nipponbare and 93-11. Using NGS-pipe, we reported 34,794 InDels from re-sequencing data of rice cultivars Wu-Yun-Geng7 and Guang-Lu-Ai4. Combining AGA-pipe and NGS-pipe, we developed 205,659 InDels in eight japonica and nine indica cultivars and 2,681 InDels showed a subgroup-specific pattern. Polymerase chain reaction (PCR) analysis of subgroup-specific markers indicated that the precision reached 90% (86 of 95). Finally, to make them available to the public, we have integrated the InDels/markers information into a website (Rice InDel Marker Database, RIMD, http://202.120.45.71/). The application of IMDP in rice will facilitate efficiency for development of genome-wide InDel markers, in addition it can be used in other species with reference genome sequences and NGS data.

 

Lu Y, Cui X, Li R, Huang P, Zong J, Yao D, Li G, Zhang D, Yuan Z (2015) Development of genome-wide insertion/deletion markers in rice based on graphic pipeline platform. J Integr Plant Biol 57: 980–991 doi: 10.1111/jipb.12354

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The clustered regularly interspaced short palindromic repeats (CRISPR)-associated system enables biologists to edit genomes precisely and provides a powerful tool for perturbing endogenous gene regulation, modulation of epigenetic markers, and genome architecture. However, there are concerns about the specificity of the system, especially the usages of knocking out a gene. Previous designing tools either were mostly built-in websites or ran as command-line programs, and none of them ran locally and acquired a user-friendly interface. In addition, with the development of CRISPR-derived systems, such as chromosome imaging, there were still no tools helping users to generate specific end-user spacers. We herein present CRISPR Primer Designer for researchers to design primers for CRISPR applications. The program has a user-friendly interface, can analyze the BLAST results by using multiple parameters, score for each candidate spacer, and generate the primers when using a certain plasmid. In addition, CRISPR Primer Designer runs locally and can be used to search spacer clusters, and exports primers for the CRISPR-Cas system-based chromosome imaging system.

 

Yan M, Zhou SR, Xue HW (2015) CRISPR Primer Designer: Design primers for knockout and chromosome imaging CRISPR-Cas system. J Integr Plant Biol 57: 613–617. doi: 10.1111/jipb.12295

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