Home

After inputting the genes/genomic region and submitting the query (box 1), users can quickly get an overview of the results of the genes/genomic region in all modules through a one-click access on the homepage (box 2).

Genomics

Genome data

The genomics module collects 40 published genome assemblies. Users can click on the genome ID to access detailed information about the genome, such as its data source, total sequence length, chromosome length, and etc.

Gene search

The gene search page provides gene structure and function information of B. juncea genomes. Various query modes are provided in this module. The users can enter the gene ID, gene name, genomic region, and gene list of interest, to obtain the structure and function information of all homologous genes and their phylogenetic relationship. For example, if users submit the gene 'AT1G03630' (box 1), users can obtain the physical location and functional description information of the homologous genes of the target gene (box 2), the phylogenetic relationship of homologous genes (box 3).

Gene cluster

Gene cluster provides a query of gene cluster results for B. juncea genomes. The user can query the copy number of the target gene by entering a gene ID or gene name. For example, if users submit the gene 'AT4G17030' (box 1), results in boxes 2-5 will be obtained. The gene IDs in the gene cluster of different genome assemblies can be obtained (box 2); the copy number difference of the gene cluster in B. juncea genomes is visualized (box 3); the functional description of the genes in this gene cluster is given (box 4); the physical location information of the genes in this gene cluster will be obtained (box 5).

Gene family

Gene family integrates information from 179 gene families. In this module, the user can select a gene family to query for gene information, gene copy number, and gene copy number distribution in different subfamilies. Box 1 lists all gene families. If users select and submit 'ABC Superfamily' at box 1, the gene list of the corresponding gene family (box 2) and the gene copy number in all subfamilies will be obtained (box 3). Besides, users can also query the information of gene family by inputting a gene ID (box 4).

Gene index

The gene index was constructed by integrating the gene collinearity of two mustard genomes, resulting in a total of 80917 gene indexes. Users can view the gene index of related genes (box 2) by entering the gene ID/name of Arabidopsis, gene ID of the mustard genome, genomic region, or gene list (box 1).

Genome synteny

Genome synteny was constructed by the comparative genomic analyses between B. juncea genomes and between B. juncea and B. rapa/B. nigra. To browse the genome alignments, we integrated the Gbrowse and Dotplot browsers. In the Gbrowse browser, users can select the genome (box 1) and genomic region (box 2) to obtain the genome collinearity result (box 2), and browse by adjusting the position and resolution of the genome. The Dotplot browser enables users to explore the global genome collinearity between genome assemblies (box 3-4).

Transcription factor

In the Transcription Factor (TF) module, we annotated a total of 6,326 genes that encode transcription factors in 58 families, based on the TF information in the Arabidopsis genome and the homologous gene relationship between the B. juncea and Arabidopsis genomes. Users can browse the number of genes in all gene families and view the number of genes in a particular family by clicking on the family name. For instance, when users click on AP2 (box 1), they will get a list of genes belonging to this family (box 2). Furthermore, users can enter the gene ID (box 3) and obtain its TF family information (box 4).

Pathway

The Pathway module provides pathway information for the B. juncea genes (box 1). In this module, users can select the pathway of interest to query the pathway plot (box 2). Relevant genes are displayed visually in the corresponding pathway (box 3).

Population

Population information

In the Population information module, users can browse the information of 1,614 accessions such as origin, subpopulation, resource, and sequencing depth (box 1) and the proportion of different subpopulations on each continent (box 2).

Population structure

In the population structure module, users can browse the PCA (box 1), population structure (box 2) and phylogenetic tree (box 3) of 1,614 accessions based on their genotypes. Users can move the mouse over these plots to query the population structure information of specific accession.

Selective signals

In the selective signals module, Fst, π, Tajima's D and XP-EHH are provided to evaluate the population differentiation between groups and genetic diversity of each group.

F_ST

Users can first browse the mean value of Fst between population pairs (box1). By entering the gene/genomic region (box 2), Users can obtain the Fst result in the specific genomic region (box 3-4).

π

By entering the gene/genomic region and selecting the population, Users can obtain the π value in the specific genomic region to evaluate and compare the population diversity between different populations.

Tajima's D

By entering the gene/genomic region and selecting the population, users can obtain the Tajima's D value in the specific genomic region to evaluate and compare the population diversity between different populations.

XP-EHH

Users can first browse the mean value of XP-EHH between population pairs (box1). By entering the gene/genomic region (box 2), users can obtain the XP-EHH result in specific genomic region (box 3-4).

Germplasm

In the Germlasm module, the information of 6 germplasms with different morphological characteristics is provided, including the image, scientific name, the use type, origin and features.

Variations

Single locus model

In the Single-locus module, users can search for genetic variation information according to the gene ID, genomic region (box 1). The database integrated 8,869,856 SNPs /InDels, and users can query by SNP, Haplotype, and Merge mode. For example, under the “SNP” mode (box 2), by entering the gene ID, users can obtain the information of the target gene (box 3), the variation distribution on the gene structure (box 4). Then, users can select the flanking region to browse the upstream/downstream variations of the genes (box 5) and the variants' annotations (box 6-7). Moreover, by submitting a specific gene, users can obtain the ratio of different alleles in all populations (box 8-9), the association between variants and phenotypes (box 10-11). Under the “Haplotype” mode (box 12), by entering the gene ID, users can obtain the variation information of the target gene and its flanking region (box 14). Then, haplotypes of the target gene were formed by combined the variations selected (box 14). Furthermore, the distribution of haplotypes in different subpopulations and continents were presented (box 15-16), as well as the associations between haplotypes and phenotypes (box 17). Under the “Merged” mode, the above results of the target variation is summarized (box 18-20).

Multi locus model

In the Multi-locus module, users can input multiple gene ID/genomic region to search for variants' information of multiple genomic regions (box 1). Then, users can choose multiple variants to form haplotypes (box 2-3), and obtain the frequency of different haplotypes (box 3). Moreover, the haplotype-based association analyses are provided to identify trait-related haplotypes (box 4).

Transcriptomics

Tissue expression profile

In the tissue expression profile, 941 libraries from 15 tissues were collected. Users can obtain the expression level and tissue expression profile (box 3-4) by inputting the gene ID/Name and selecting the T84-66 gene (box 1-2). Then, users can browse the dynamic change of gene expression level at different time points, as well as the difference expression level between different groups (grouped according to seed size, leaf color, nitrogen use efficiency, and seed development) (box 5).

eFP

Electronic Fluorescent Pictographic (eFP) provides an intuitive visualization, allowing users to comprehensively view gene expression levels among 9 tissues at different stages of development. By entering "Data Mode", "Primary Gene ID", and "Secondary Gene ID", users can see the specific expression of genes in various tissuess and move the mouse over the tissue in the figure to query the gene expression value.
The following is a detailed explanation of three modes in eFP:
Absolute: The absolute expression level for a given gene.
Relative: The relative expression of any given gene is compared to a control data (the median expression value of that gene for all samples that were measured), which can be used to study where the gene is most-prominently expressed.
Compare: The relative expression of any given gene (primary gene) can be compared to any other gene (reference gene).

Co-expression network

Gene-gene network

The co-expression network is generated by calculating the Pearson correlation coefficient of gene pairs' expression level. By inputting the gene ID (box 1) and choosing the threshold (box 2), users can obtain co-expression networks (box 3) and the results (box 4) in a table.

lncRNA-mRNA network

The co-expression network is generated by calculating the Pearson correlation coefficient of lncRNA-mRNA pairs. By inputting the gene ID and choosing the threshold, users can obtain co-expression networks (box 3) and the results in a table (box 4). By clicking the lncRNA ID in a table, the lncRNA information can be presented in a new window (box 5).

Population expression profile

In this module, the gene expression data of 204 accessions are integrated. By inputting the gene/gene list (box 1), users can browse the expression profile in this population (box 2-3).

Differential expression

In this module, mRNA/lncRNA comparative transcriptomics analyses related to leaf color, leaf shape, leaf vein color, seed size and seed development are conducted to identify differentially expressed genes/lncRNAs related to important trait.

mRNA

After choosing the "Phenotype" and "Group" (box 1), Users can first browse the samples applied in this analysis (box 2). Then, the differentially expressed genes can be represented by volcano plot (box 3). The top 20 up/down-regulated genes with the smallest P value are also represented (box 4).

lncRNA

After choosing the “Phenotype” and “Group“ (box 1), users can first browse the samples applied in this analysis (box 2). Then, the differentially expressed genes can be represented by volcano plot (box 3). The top 20 up/down-regulated genes with the smallest P value are also represented (box 4). Users can browse the detail information of lncRNAs by clicking on the lncRNA ID (box 5).

Phenomics

In the phenotype module, we collected 16 traits of 628 accessions, including 9 seed quality traits, 1 flowering time, 1 maturity time, and 5 agronomic traits (box 1). Users can browse the statistics of the phenotypes (box 2). By clicking the trait, the trait values of every accession are listed , and the density plot and the comparison between different subpopulations are also provided (box 3).

Metabolomics

In the metabolomics module, a total 412 metabolites without redundancy from 3 studies were collected, including 321 metabolites related to cadmium (Cd) stress, 67 metabolites related to moonlight, and 65 related to salt stress. Taking the metabolites involving in mustard roots' response to cadmium stress as an example, metabolites are grouped into 7 class (box 1). When users select the “Amino acids”, the differently accumulated metabolites (DAMs) of different comparisons are displayed by Venn diagram (box 2). T1, T2, and T3 represent different treatment; T1 represents the control group without the Cd stress; T2 represents the samples were treated for 48 hours by Cd; T3 represents the samples were treated for 7 days by Cd. The detail information of DAMs is in the box 3, and their contents are visualized by heatmap (box 4).

Multi-omics

GWAS

In GWAS module, after selecting the phenotype name and threshold (box 1), the GWAS result of the variants with the smallest P-values in each 500kb window are displayed by a Manhattan plot. The user can zoom in or out by sliding the mouse wheel, and then move the mouse to the corresponding window to browse the lead SNP and the corresponding p-value in the window (box 2). By clicking the bar in the Manhattan plot, the SNP information and the local Manhattan plot corresponding to the region of the bar is represented (box 4-5). Moreover, users can input the genomic region to directly obtain the local GWAS results (box 3).

TWAS

In the TWAS module, the user can firstly click the phenotype name and then click "submit" to view the TWAS results of the phenotype. The first is a genome-wide Manhattan plot of all gene TWAS. Users can zoom in or out by sliding the mouse wheel, and then move the mouse to the corresponding point to browse the TWAS statistics of the gene. The detail information of TWAS is displayed in the table.

eQTL

In eQTL module, the user can search for genes by gene ID, gene name, gene region and gene list. In the results, the first is a list of all SNPs significantly associated with the expression of this gene and the corresponding statistics, including beta and P-value. Then, a circos diagram of the relationship between SNP and gene regulation was shown. Users can move the mouse to the corresponding point in the diagram to view the value of the statistic corresponding to "eSNP-eGene".

SMR

In the SMR module, the users can firstly select the phenotype name, and then click "submit" to view the SMR results of the phenotype. The first is a Manhattan plot of the SMR of all genes in the whole genome and the detail SMR results of all genes. Users can zoom in or out by sliding the mouse wheel, and then move the mouse to the corresponding point to browse the SMR results.

Colocalization analysis

In Colocalization analysis (COLOC) module, users can input the gene ID or trait to see which genes and traits are significantly influenced by a common variant. For example, when users select the trait "α-tocopherol content in seed", they can firstly browse the summary table of COLOC results (box 1). If the column "COLOC" is "True", that means the expression level of the gene and “α-tocopherol content in seed” are influenced by common variants (box 2). To further figure out which variants are the "common variants", users can choose the gene BjuVA02G31360 and submit it. Then, the PP.H4.abf is bigger than 0.5, which means "both traits are associated and share a single causal variant" (box 3). Finally, they can find the lead SNP with the largest PP.H4 value is "AA_Chr02:18340573", and the local Manhattan plots of the GWAS results of BjuVA02G31360 expression level and α-tocopherol content in seed are showed (box 4).

Association network

By integrating the results of integrated analyses, eQTL-gene and gene-phenotype association networks of genes/traits are provided to give a global insight into the genetic mechanisms underlying mustard-related traits. After selecting a trait or a gene and submitting it (box 1), users can see all the gene-phenotype associations and variation-gene associations related to the trait or the target gene are presented by the information table and networks (box 2-3). Nonetheless, User can input the genes of interest to see the networks containing the gene-phenotype and eQTL-gene associations.

Tools

JBrowse

Function: Jbrowse tool (Diesh et al., 2023) is for visualizing and exploring the genomic data.
Data: 40 genome assemblies were provided in Jbowser, including four B. juncea genome assemblies, 32 B. rapa genome assemblies and four B. nigra genome assemblies.
Usage: The user clicks the circle in front of the genome assembly to select the genome, and then in the Jbrowser, the local track file can be uploaded.

BLAST

Function: BLAST (Boratyn et al., 2013) is for searching for similar sequences by comparing the query sequence to sequences in the database.
Data: In BLAST, the 40 genome assemblies were provided, including 4 B. juncea genome assemblies, 32 B. rapa, and 4 B. nigra. Their genome, CDS, protein and gene sequences were used to construct the BLAST library.
Usage:
1. Click the circle in front of the genome assembly and select the sequence type. 2. Paste the sequence in the box below. 3. Select the database type and click "Submit" to submit. Results:
The first is a graphical representation of the genomic positions to which the sequences are aligned. Then there is a list of all the positions aligned, and the user can click them to query the detailed sequence alignment.

Seq fetch

Function: Seq fetch is for extracting sequences of a specific genomic region.
Usage:
1. Select a genome version.
2. Input the gene in the box and the length of a flanking region, or input the genomic region. Results: Users can directly copy the sequences from the dialog box or download the sequence files.

GO enrichment

Function: GO enrichment tool is for evaluating which biological pathways in GO database (Harris et al., 2004) are significantly enriched in the gene set.
Usage:
1. Select the genome.
2. Paste the gene list in the sequence box or upload the gene list file.
3. Click "submit".
Results:
1. List of results of GO enrichment analysis. You can switch the category of GO by clicking BP, MF and CC above, and click "Download" in the upper right to download the results. 2. The dot plot and bar plot of GO enrichment analysis results. Move the mouse over the figure to query the statistics of the corresponding GO enrichment analysis results. Click the arrow at the top right to download the image.

KEGG enrichment

Function: KEGG enrichment tool is for evaluating which biological pathways in the KEGG database (Kanehisa, 2002) are significantly enriched in the gene set.
Usage:
1. Select the genome.
2. Paste the gene list in the sequence box or upload the gene list file, and click "submit".
Results:
1. The result list of KEGG enrichment analysis. Click "Download" in the upper right to download the results.
2. Dot plot, bar plot and network plot of KEGG enrichment analysis results. Move the mouse over the figure to query the statistics of the corresponding KEGG enrichment analysis results. Click the arrow at the top right to download the image.

SNP match

Function: SNP match tool (Pisupati et al., 2017) is for assessing the genetic similarity between different accessions.
Usage:
1. paste or upload the genotype data (VCF format) of the accession of interest and click "search". SNPmatch will predict the similarity between the accession of interest and the 1614 accessions in the database.
Results:
1. The result list of SNPmatch: Click "Download" at the top right to download.
2. The bar plot of SNPmatch results, the x axis is the accession name, and the y axis is the similarity with the accession of interest. Move the mouse to the figure to query the corresponding data. Click the arrow at the upper right to download the image.

Primer3

Function: Primer3 (Untergasser et al., 2012) is for generating primer sequences specific to the target gene.
Usage:
1. Change the parameters if needed before primer design.
2. Enter gene ID, genomic region, gene index, or sequence.
3. Select the genes for primer design and submit.
Results:
1. The statistical results of primer design: Click "Download" in the upper right to download. Users can select primers for e-PCR alignment.
2. Raw output of Primer3, including visualization of primer sequences and their positions on the genome.

e-PCR

Function: e-PCR (Rotmistrovsky et al., 2004) is for predicting the polymerase chain reaction (PCR) amplification products based on the primer sequence.
Usage:
1. Select the genome on the dataset box.
2. Select the file format (STS or Primers).
3. Paste the sequence into the box or upload the sequence file.
4. Adjust alignment parameters, including mismatch value(mism), gap size(gap) and sequence length range(lo-hi").
5. Submit.
Results:
1. The list of e-PCR results, each row is the position of the sequence amplified by the primers.
2. Statistical chart of e-PCR comparison results, including the ratio of mismatches and gaps.
3. The raw output result of e-PCR.

Valcano plot

Function: Visualize the data in the form of volcano plot.
Usage:
1. Submit an input file according to the sample file format provided.
2. Select a threshold and colors.
Results:
The volcano plot can be obtained and downloaded.

Data2heatmap

Function: Visualize the data in the form of heatmap plot.
Usage:
1. Submit the input file according to the sample file format provided.
2. Choose the mode to display the value.
3. Adjust the size of the chart and text.
4. Submit.
Results:
Show the expression level heat map of input genes.

Data2geomap

Function: Visualize the data in the form of Geographical plot.
Usage:
1. Select the image type (Geopie or Geoheatmap);
2. Select the region of the map (World, China or the United States);
3. input the data and click "Submit" to submit.
Results:
Generate geographic distribution maps based on user-selected image types, regions, and input data. Click the upper right icon to download.

LDheatmap

Function: LDheatmap (Shin et al., 2006) can measure the pairwise linkage disequilibrium (LD) for SNPs.
Usage:
Input the genomic region and click 'submit'.
Results:
Based the LD between the variants in the genomic region, LD heatmaps are generated. Click "Download" at the upper right to download the plot.

CRISPR-Local

Function: CRISPR-Local tool (Sun et al., 2019) screensg all possible on-target sgRNAs for CRISPR/Cas9 and CRISPR/cpf1 systems and scoring them with the Rule set 2 algorithm (Doench et al., 2016).
Usage:
1. Select a genome version.
2. Select a search mode.
3. Select a cas protein type.
4. Input a gene list.
Results:
1. Present possible on-target sgRNAs on a table.

COLOC

Function: By combining users' GWAS results with eQTL results from the database, the COLOC tool (Wallace, 2021) enables exploration of the common genetic variation affecting both the target trait and gene expression. A posterior probability of hypothesis 4 (PPH4) ≥ 0.5 was considered as evidence supporting the presence of at least one shared causal variant in both the GWAS and cis-eQTL mapping.
Usage:
1. Input a gene ID.
2. Input GWAS result.
3. Input the number of accessions used in GWAS.
Results:
1. Summary of colocalization result of the eQTL and GWAS.
2. Detailed results of colocalization analysis.
3. GWAS/eQTL results of 250 kb flanking region of the target gene.

SMR

Function: By combining users' own GWAS results with eQTL results from the database, the SMR (summary data-based mendelian randomization) tool (Zhu et al., 2016) investigates whether there is a significant causal relationship between the target trait and gene expression. Genes with a P-value of SMR < 1/n and a P-value of the heterogeneity in dependent instruments (HEIDI) test ≥ 0.05 were considered as candidate genes for the phenotype. n represents the number of genes involved in this analysis, which was the number of rows in the result table.
Usage:
1. Input GWAS results.
Results:
1. Present SMR results in a table.

Literature

The retrieval of mustard-related literature was conducted in the Web of Science by searching for the query (TI=(brassica juncea)), and a total of 1,841 relevant literature were obtained and stored in BjuIR as of October 25th, 2023. Users can query in keyword, journal or pulication year (box 1). Take the word "flowering" as an example, users can obtain the statistics of literature related to "flowering" in the box 2, a keyword co-occurrence network for visualizing trends in the studies of interest, and the detail formation in the box 3.