The sequences of 18 cotton genome assemblies representing 14 Gossypium species and their respective gene annotations data, together with four transcriptomes were downloaded directly from relevant databases or sequenced by our laboratory and further used in GRAND (Table 1). In Table 1, the suffix of each species name indicates the institution that sequenced and published the genome [such as ICR: Institute of Cotton Research of CAAS, ZJU: Zhejiang University, HAU: Huazhong Agricultural University, USDA-ARS: US Department of Agriculture (USDA) Agricultural Research Service (ARS)]. The transcriptome data of G. hirsutum_TM-1 and G. barbadense_H7124_ZJU were downloaded from the NCBI Sequence Read Archive with the accession number PRJNA490626 and were published by Hu et al. (2019) from Zhejiang University. The other two transcriptomes, G. arboreum_ICR and G. hirsutum_ZM24_ICR, were sequenced and assembled by Du et al. (2018) and Yang et al. (2019) of the Institute of Cotton Research (ICR), respectively. Illumina reads were aligned to references G. hirsutum_TM-1_ICR (Yang et al., 2019), G. hirsutum_ZM24_ICR (Yang et al., 2019), and G. arboreum_ICR (Du et al., 2018), respectively, using TopHat 2.1.1 (Kim et al., 2013). Quantification of gene expression was then performed with Cufflinks version 2.2.1.² These data are shared freely on these websites without analysis tools to analyze them online, and the inconsistent format of the different datasets makes it more difficult to use them jointly. We have solved these problems and all the data are available for free download from GRAND.

The GRAND database relies on the Linux operating system, using J2EE as the framework, MySQL as the back-end database, and Apache Tomcat as the server. Genome sequence, annotation, expression, and variation data are stored in the MySQL database. A web interface based on JavaServer Pages (JSP), HTML5 and, CSS3 is constructed to enable end-users to access GRAND data through any modern browser on any kind of device. The GRAND database is hosted on a server equipped with eight 14-cores Intel Xeon Gold 5120 processors.

The orthologous genes among G. hirsutum TM-1 and ZM24 (Yang et al., 2019), G. hirsutum TM-1 (Hu et al., 2019), G. barbadense (Hu et al., 2019), G. arboreum (Du et al., 2018), G. hirsutum TM-1 (Wang et al., 2019), and G. barbadense (Wang et al., 2019) were identified using Inparanoid v4.1 (O’Brien et al., 2005) with default parameters. Tetraploid cottons were divided into A and D subgenomes. Then, we combined these results into one file for searching orthologous gene ID.

Based on gene expression data, networks of co-expressed genes of three cotton species (G. arboreum_ICR, G. hirsutum_TM-1_ICR, and G. hirsutum_ZM24_ICR) were constructed using Pearson’s correlation coefficient (PCC) values between pairs of genes and visualized by using JavaScript Cytoscape.js. For a given query gene, the network of top 20 target genes with the highest correlation values with the query gene is shown. In addition, a summary table of all co-expressed genes and corresponding functional annotations is provided below the network.

To provide users with a wealth of information about cotton, the GRAND database was built containing the latest and most comprehensive Gossypium genomic/transcriptomic datasets (including 18 assembled genomes and four transcriptomes; Figure 1; Table 1). The main structure of GRAND is shown in Figure 1 with four major modules: Browse, Search, Tools, and Download. GRAND provides search functions for various genomic information, including gene annotation information (KOG, GO, KEGG, and NR), gene sequences, genome variations (SNPs and INDELs), and expressional profiles and gene families, by entering a chromosomal region or longest transcripts ID. GRAND also integrated the genome visualization tools Gbrowse (Stein et al., 2002) and Jbrowse (Buels et al., 2016), allowing users to instantly browse, visualize, and retrieve sequence data and offer gene co-expression networks for different developmental stages and tissues/organs. Moreover, GRAND provides a suite of the toolbox for online analysis, such as BLAST- and BLAT-based sequence comparisons, orthologous gene ID across different species and PCR primer design (Untergasser et al., 2012). Besides, users can download cotton data selectively or in full. Tutorials for using all the tools in the database are provided in the Help module. This information in GRAND will be useful for both dry lab and wet lab biologists.

The browse detail page mainly includes the following modules: SNP Variation, INDEL Variation, Gene Annotation, and Gene Family. Users can search for the Nr, TrEMBL, KOG, KEGG, and GO annotations using Gene Annotation module. By cross-link, the genome variations related to each gene can be searched, including location, genome sequence, CDs sequence, transcript sequence, and peptide sequence (Figure 2A). Users can also quickly find related information about the gene family by searching for the target gene keywords (Figure 2B). The SNP or INDEL Variation shows the genome variations data (SNP or INDEL) on each chromosome of a group of individuals. Users can filter the data by reference genome, and SNP, or INDEL type (Figure 2C).

For example, the gene “Gh_D03G071700” is located on chromosome D03 of the G. hirsutum_TM-1_ICR genome and is annotated as the hAT family C-terminal dimerization region, which can be also found at the end page by searching the gene family module with PF05699. The SNP and Indel Variation can be directly queried by clicking on the Variation option and visualized by clicking on Gbrowse and Jbrowse (Figure 2), which are tools for displaying variations (SNPs and INDELs) and genes (structure) of the cotton individuals on chromosomes. The Gbrowse detail page includes the following basic information of this gene: name, position in the scaffold, length, CDS parts, and sequence. The detail page of Jbrowse is displayed in a popup window showing information about the gene, SNP, and INDEL in the 30 zkb region around this gene.

GRAND allows users to perform both BLAST and BLAT searches to rapidly align sequences to the genome. BLAST search implemented in GRAND using SequenceServer (Priyam et al., 2019) provides an interface with text-based and interactive visual outputs to search against nucleotide sequences and/or protein sequences, including BLASTn, BLASTp, BLASTx, tBLASTx, and tBLASTn programs. GRAND currently has a BLAST database for whole-genome sequences, CDSs, and predicted proteins for each reference genome assembly. Pasting the DNA/Protein sequences in the query box or uploading a fasta file is acceptable. The search result displayed on a result page comprises two parts: “Graphical view” and “List view” (Figure 3A). The Graphical view presents a brief graphical view of the BLAST results by the chart. The List view is a table showing detailed information on the alignment by the BLAST program, such as gene ID, total score, e-value, and length. The sequence of FASTA files can be annotated by comparing with database (Nr_vs_GO, KEGG, COG, SwissProt, TrEMBL, KOG and Pfam; Ashburner et al., 2000; Tatusov et al., 2000; Koonin et al., 2004; El-Gebali et al., 2019) in the “Anno function” section. The results of the KEGG annotation can be visualized in the “KEGG Dot Plot” section. Besides, to make it easier for users to quickly search for data of interest, the current version of GRAND has four submodules under the “Search” module. (i) Multicriteria Search. Search the genome variations (SNPs and INDELs) of each individual in this database using gene, region, or variation. Data can be filtered by variation type and genotype. (ii) Phenotype Search. The phenotype data of fiber-related traits, floral traits, seed-related traits, and other traits for cotton species. (iii) Comparative Search. Search and compare the genome variations (SNPs and INDELs) of two or more cotton individuals in this database using gene, region, and variation. (iv) Gene Search. Search and achieve gene annotation (KOG, GO, KEGG, and NR), gene structure, sequences by inputting the chromosomal region and gene ID. The SNPs, and INDELs can be also searched by cross-link (Figure 3B). After searching, a new webpage will pop out and display all the matched results. The details of each matched result can be viewed by clicking on it. The Orthologous Gene ID function can obtain orthologous gene IDs among different cotton species and different versions of cotton. For example, the orthologous genes ID of gene “Ga01G0003” are “Gh_A01G000300,” “Gh_D03G199000,” “Ghicr24_A01G000500,” and “GB_A10G2858” in other cotton species, respectively (Figure 3C). This result was consistent with the result of the BLAST above.

In addition to the modules mentioned above, GRAND also offers several additional tools. The “Expression Visualization” section shows the expression profiles in different tissues, and users can perform the analysis by partial selection or by selecting all. The results are presented as heatmaps and the expression values (FPKM) for each data set are displayed in the table at the bottom (Figure 4A). Users can also import the results generated above into the “Heatmap Draw” section for further adjustment and embellishment. Gene network analysis can be used to identify related genes in the same biological processes or pathways. Networks of co-expressed genes are constructed based on inter-gene expression data using Pearson’s correlation coefficient (PCC) between genes (Langfelder and Horvath, 2008). Enter the gene ID and set the PCC value threshold to visualize the top 20 target genes with the highest correlation value with the query gene, and click on any co-expressed genes in the network to view their co-expression network. In addition, a summary table of all co-expressed genes and corresponding functional annotations is provided below the network. Links to basic information about the genes are created for each target gene in the summary table (Figure 4B). GRAND database provides primer design function based on gene sequences from cotton (Figure 4C). Users can also design primers for CRISPR/Cas9/Cpf1 genome editing using the targetDesign tool via the website link (Figure 4D; Xie et al., 2017). Additionally, we provide an FTP server to store all the publicly released datasets used in GRAND, with an enhanced user interface, text preview, and directory download.

The download page provides users with selective FTP download for genome sequences and their annotation information, transcriptomics data, CDS, protein, etc.

GRAND currently still has some limitations. For example, only cotton genomic, transcriptomic and phenotypic data were collected here, some additional data, such as cotton molecular markers and metabolic data need to be expanded in the future. Moreover, there is no sequence feature extraction tool in the current database.