The core of BioVizSeq is to use the coordinate information of elements on the sequence and the length information of the sequence as input files, and use ggplot2 for graphic drawing. Based on this, we have developed a basic plot function motif_plot to facilitate users in graphic drawing ( Figure 3 ). In theory, motif_plot can be graphically drawn using these two files for all sequences, whether they are proteins, DNA, or RNA sequences.

GTF and GFF are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. There are currently multiple software or tools based on GFF3/GTF files for gene structure display, such as GSDS 2.0 (Hu et al., 2015), TBtools, etc. But there are no similar tools based on ggplot2 of R language yet. The BioVizSeq provides two modes for displaying gene structures through GFF3/GTF files. One approach is to divide the process into two steps. Firstly, the gene length information, UTR, and CDS information of the gene are obtained through gff_to_loc, and then the gene structure is freely drawn using motif_plot. Another approach is to directly use gff_plot to plot gene structure in one step, but with relatively limited freedom. ( Figure 4 ).

The MEME Suite web server (https://meme-suite.org/meme/tools/meme) is commonly used in bioinformatics to identify and display conserved patterns in DNA, RNA, or protein sequences. Its advantage lies in the ability to efficiently identify potential functional motifs and provide reliable significance evaluations for these motifs through statistical methods, helping researchers reveal important biological features in the sequence. However, MEME motifs also have certain drawbacks, especially in terms of display effectiveness. Firstly, the motif images generated by MEME cannot be downloaded. Secondly, the images generated by MEME tools are usually relatively single and difficult to flexibly combine or modify with other graphics or data, which limits their application in diversified graphic displays. In addition, the visual representation of motifs is relatively fixed and may not meet the expectations of aesthetic or customization needs in some specific studies. Fortunately, MEME Suite web server provides two result files: meme.xml and mast.xml. BioVizSeq first parses the result file, and then uses the motif plot function to plot the motif.

Pfam is a widely used protein family database that provides a wealth of information on protein domains and families. It is currently hosted by InterPro (https://www.ebi.ac.uk/interpro/), an integrated database that integrates resources from multiple protein sequences and domains (Blum et al., 2025). The “by sequence” feature in Search InterPro allows users to search for domains, and functional sites by submitting sequences. However, its display results are in the form of a table, which is not conducive to direct comparative observation. BioVizSeq can organize this result file and display the domain.

NCBI’s Batch CD Search (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi) is a powerful tool that allows users to submit multiple protein sequences in batches and perform conservative domain searches and annotations on these sequences using the Conserved Domain Database (CDD). But its display results are also in tables. Therefore, BioVizSeq has developed the ability to showcase its results.

SMART (a Simple Modular Architecture Research Tool) (https://smart.embl.de/) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. It adopts a modular architecture that can quickly and accurately identify structural domains in protein sequences, and has been widely used in bioinformatics research. However, SMART usually requires a certain foundation of Linux operating system when analyzing protein sequences in batches. The results returned in bulk are text files, which are very unfavorable for displaying the results. BioVizSeq can automatically upload sequences in bulk and organize and plot the returned results, greatly reducing the operational threshold.

PlantCARE is a database of plant cis-acting regulatory elements, enhancers and repressors, and is widely used to study the mechanisms of plant gene expression regulation (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/). After the submission sequence runs, the user will receive an email containing the result file. From this perspective, it is quite convenient. However, the file size limit for uploading sequences is 100kb. In addition, the information in the result file cannot be directly used to draw images and display them. Firstly, the result of each promoter subsequence is a compressed file, and secondly, the information contained in the result file is relatively large. These data need to be filtered and integrated before they can be used for image display. These are very difficult for ordinary researchers. BioVizSeq can automatically split and upload sequence files larger than 100kb, filter the resulting files directly merged by users, and then draw publication level figures.

Researchers often combine multiple types of images together when analyzing gene families, such as evolutionary tree + gene structure + MEME motif + SMART domain. Therefore, we have specifically developed a function: combi_p. The result of combi_p is a list containing multiple graphic files, and users can freely combine the graphics within it. At the same time, based on this function and patchwork package (https://cran.r-project.org/package=patchwork), we have written a module in shinyapp specifically designed to facilitate users in combining graphics ( Figure 5 ).

In addition, we have also developed some other features, such as Advance Plantcare, to display the types and quantity distribution of cis acting elements on the promoter ( Figure 6 ). The reason for developing this feature is that there are many types and quantities of cis acting elements on the promoter, and if we use Basic plot, it is easy to overlap and difficult to observe. For genes, we often conduct statistical analysis on their coordinates, sequence length, number of exons/introns, and analyze a series of physicochemical properties of the encoded protein sequence. Therefore, we have also added these two functions in Others, where users can analyze and download the results.