Many web services offer features for microsatellite mining. However, they are widely different in terms of functionality and the analysis, input, output content, and output return style (Mudunuri et al., 2010b). In this way, EasySSR was compared to other web tools in order to demonstrate the main functionalities that are common to them or exclusive to our tool. For this validation, six review articles were screened to discover web tools that have a feature for the identification of Microsatellites (Leclercq et al., 2007; Sharma et al., 2007; Merkel and Gemmell, 2008; Mudunuri et al., 2010b; Lim et al., 2013; Mathur et al., 2020). The publishing articles for each tool were analyzed in April 2023, and the platforms were tested through the links available in the articles to check if they were still active. If the tool was functional, the article citation rates were analyzed through Google Scholar, and these data alongside with the search link were tabulated. The 10 most cited web tools were used for features comparison against EasySSR. The features used for comparison were partially based on the ones analyzed by Merkel and Gemmell (2008), Mudunuri et al. (2010b) in their articles. Besides the Citations and Author/Publishing Year, the following categories and features were used in this comparison: i) ANALYSIS: Microsatellite only, Maximum motif length, Perfect SSRs, Imperfect SSRs, Compound SSRs, Flexible Parameters, and Large-scale analysis; ii) INPUT: Limits Max, File Size, Analyze web of many whole genomes, Accepts multiple FASTA files, Integration with NCBI, and Box for cut-and-paste small sequences; iii) OUTPUT CONTENT: Text file, HTML file, PTT file, Coding/Non-coding, Flanking Sequences, Sample comparison sheets, and Sample comparison graphs; iv) OUTPUT RETURN: Web results, Email results, and Download results.

Differently from the benchmark tests, this comparison aimed to demonstrate the capacity of EasySSR to handle large datasets while being a versatile shortcut for online data analysis. For this, 54 complete genomes of C. pseudotuberculosis (CP) were selected, which have been previously studied by Pinheiro et al. (2022), who also used IMEx 2.1 as the microsatellite mining tool. The sequences were obtained at NCBI through the accession numbers stated in Table 4, with the same version as the ones stated in the original article by Pinheiro et al. (2022), and downloaded as FASTA and GenBank annotation files.

For this analysis, the dataset was processed in EasySSR with slightly different parameters, in custom mode and default mode. In general, the main parameters were the same for both analyses: Minimum Repeat Number–Mono:12, Di: 6, Tri: 4, Tetra: 3, Penta: 3, Hexa: 3, flanking sequences of size 15 bp, dMax compound of 0, Standardization level 3, extracting all types of SSR, and yes for identify coding/non-coding regions, generate alignment, and text outputs. However, the first analysis was conducted by searching for perfect SSRs only, with the same parameters as Pinheiro et al. (2022), by using the custom parameters mode and setting the imperfection and mismatches as 0, expecting to have the same results as them. Then, the second analysis was conducted by searching for perfect and imperfect SSRs, using the EasySSR default parameters, which were also based on and adapted from Pinheiro et al. (2022), but with Imperfection % - Mono: 10%, Di: 10%, Tri: 10%, Tetra: 10%, Penta: 10%, Hexa:10% and Mismatch in Pattern: Mono: 1; Di:1; Tri:1; Tetra:2; Penta:2; Hexa:2. The results were compared with Pinheiro et al. (2022) through the graphs and charts generated as the output of EasySSR.

EasySSR requires only a project name and one or more FASTA files containing nucleotide sequences or genomes (draft/complete) for the identification and comparison of STRs (Figure 2A). If the user intends to identify coding/non-coding regions, a GENBANK file should also be uploaded for each FASTA file. Only the FASTA file is mandatory, whereas the GENBANK file is optional. When an annotation file is not uploaded, the algorithm will automatically assume that all sequences in the FASTA file are non-coding. However, with an annotation file, the algorithm will leverage the provided information to calculate the distribution of motifs in coding and non-coding regions. In the case of a multi-FASTA file input, EasySSR will identify SSRs, but the file will be treated and analyzed as a single draft genome. The algorithm treats each FASTA file as an independent genome, comparing them separately, and utilizes the input FASTA files filename as the sequence name in the EasySSR outputs. This web application uses secure HTTPS (Hypertext Transfer Protocol Secure) connections to transfer data between the client and the server, ensuring that the data are not intercepted during transmission and not used for purposes other than the intended analysis, with the project data being stored in the EasySSR database for a month-long period.

The tool runs with intuitive default or custom flexible parameters and has no limit size for input (Figure 2A). In this way, users can load as many genomes as they want for their analysis, depending only on the computational structure available. The user does not need to input any parameter in the default parameters mode but, rather, just select this option and execute EasySSR. The preset default parameters are based on Pinheiro et al. (2022): Repeat Number: 1–12, 2–6, 3–4, 4–3, 5-3, and 6–3; adapted to allow the imperfection maximum of 10% with 1 or 2 mismatches: Imperfection % (p%): 1%–10%, 2%–10%, 3%–10%, 4%–10%, 5%–10%, 6%–10%; and Mismatch in Pattern: 1–1; 2–1; 3–1; 4–2; 5–2; 6–2. Maximum distance for compound SSR: 0 bp; Standardization Level: Level 3; Flanking Sequences: 15 bp; Extract all SSR types, Generate Alignment, and Text Output: “Yes.” In this way, the user can easily write a project name, input the files to be analyzed, and press the “Upload and Run” button, as shown in Figure 2A. The loading screen will be then exhibited, as demonstrated in Figure 2B, until the analysis is complete.

EasySSR Custom mode (Figure 3) enables users to adjust analysis parameters (A to J) based on preferences, with brief descriptions conveniently accessible via the information icon i). This user-friendly feature aids in selecting suitable values, empowering customization to specific requirements. The only mandatory fields for user input in Custom mode are from A to D: (A) Mismatches; (B) Imperfection %. To restrict the analysis to perfect SSR only, also known as Misa-mode, the user can define all the settings in parameters (A) and (B) to 0; (C) Minimum Repeat Number; and (D) Size of Flanking Sequences. The other parameters, from (E) to (J), can be used as the preset: (E) Generate Alignment and (F) Generate Text output are fixed in YES since EasySSR processes those files to generate the summarized outputs, charts, and tables; (G) Identify Coding Regions is preset as YES but can be set as NO; (H) Maximum distance for Compound SSR is preset at 0 but can be set from −1 to 100; (I) Standardization level is preset at 3 but can be set as 0, 1, 2, 3, or F; (J) SSR types to extract is preset at 0 to extract all SSR types, but users can set from 1 to 6 to extract only a type of SSR.

After the analysis, the web page is updated automatically, and the EasySSR reports page is exhibited (Figure 4). The user can see a blue button to download the report folder in ZIP format, containing both the files used for input (FASTA, GenBank, and the generated PTT) and the complete IMEX output files for each genome individually, in HTML and TEXT formats comprising summary, align, results, and statistics about compound, perfect, and imperfect.

Back to the EasySSR Reports interface, the user has 07 interactive donut charts with the comparative analysis of total motifs, perfect, and imperfect proportions, total of perfect SSR per motif class, total of imperfect SSR per motif class, proportion of perfect motifs in coding/non-coding regions, proportion of imperfect motifs in coding/non-coding regions, and the general comparison of SSR in coding/non-coding regions (Figure 4). It also plots 02 interactive bar charts containing the top 10 SSR motifs present in the genomes analyzed (Figure 5). The first stacked bar chart (Figure 5A) depicts the frequency distribution of the motif iterations present in all the analyzed genomes. In contrast, the second chart (Figure 5B) represents the frequency distribution of the motifs across the genomes. The x-axis displays the frequency of the motif (Figure 5B) and motif iteration (Figure 5A) in each genome. At the same time, the stacked bars represent the absolute frequency of the motif (Figure 5B) and motif iteration (Figure 5A) across all genomes. The y-axis ranks the motif (Figure 5B) and motif iterations (Figure 5A) from highest to lowest based on their frequency and presence in the genomes. The top of the y-axis corresponds to the motif (Figure 5B) and motif iteration (Figure 5B) that is present in the highest number of genomes and has the highest absolute frequency in the stacked bar.

In addition to the charts, EasySSR analysis includes three tables with filters and search options (Figure 6). The first table (Figure 6A) provides data on each motif, including its iterations, Genome, Left Flanking, Right Flanking, Start, and End positions. The second table, Frequency of Motifs per Genome (Figure 6B), has been created to enhance the representation of motif frequency distribution across the different genomes. It offers a detailed count of each motif’s occurrence in the genomes and a “total” column indicating the number of genomes in which each motif is present. This addition offers a more comprehensive and user-friendly view of the data. The third table is the statistic table (Figure 6C). It contains various summarized quantitative data about the perfect and imperfect SSRs identified in each genome. These statistics include the genome size, total SSR count, percentage proportion of SSRs per base pair (calculated using the formula = [(SSR*100)/genome_size)], total SSR in Coding/Non-coding regions, total SSR per motif class, and subgroup analyses of perfect/imperfect and coding/non-coding SSRs.

These data are available for individual download. The plotted charts are in PNG/JPEG format and the tables in CSV, Excel (.xlsx), and PDF formats, also with the copy/print options. The user can save the EasySSR Reports HTML page using their browser option or write down the project number to consult within a month.

Web-tools for microsatellite mining are important as they simplify the search and analysis of microsatellite data; they do not require an investment of time for the user to install and run the software, neither do they require device storage and dependencies for installation (Sousa et al., 2018). Plenty of web tools have been released over time, but many accession links available in the articles are not functional totally or partially anymore, as is the case with ATRhunter (Wexler et al., 2004), Tandem Swan (Boeva et al., 2006), STRING (Parisi et al., 2003), MICAS and IMEx web (Sreenu, 2003), MsatFinder (Thurston and Field, 2005), RISA (Kim et al., 2012), and LSAT (Biswas et al., 2018). The web tools still available have a variety of specific features but are very limited in many aspects in comparison to command-line tools. After analyzing the citation rates and checking their availability, we defined the top 10 most-cited SSR web tools that were still operational in April 2023: TRF web (Benson, 1999), Repeat Masker web (Tarailo-Graovac and Chen, 2009), Misa-Web (Yang et al., 2018), Batch Primer3 (You et al., 2008), Mreps (Kolpakov, 2003), Websat (Martins et al., 2009), SSR Locator (da Maia et al., 2008), STAR (Delgrange and Rivals, 2004), Imperfect SSR Finder (Stieneke and Eujayl, 2007), and PolyMorph Predict (Das et al., 2019), Their features were compared with EasySSR and summarized in Table 1.

The main limitations observed were the limited size of the input files, rare flexibilization of parameters, and the lack of identification of flanking sequences, downloadable outputs, summarized and post-processed graphical outputs, and features for online sample comparison, and that there is no exclusive focus on Microsatellites motifs (1–6 bp) but also on other Tandem repeats such as Minisatellites (10–30 bp) and Satellites (>100 pb). In some cases, even if the web service does exist, the full functionality is restricted to the command-line version, limiting the online service to basic and small analysis.

TRF (Benson, 1999) and Repeats Masker (Tarailo-Graovac and Chen, 2009) are by far the most used tools, according to the citation rate. Alongside Mreps (Kolpakov, 2003) and STAR (Delgrange and Rivals, 2004), they are tools that are not limited to microsatellites but aim to identify all tandem repeats, including other types such as Minisatellites and Satellites. STAR is a tool focused on locating a given motif in a DNA sequence, instead of screening all motifs like the other Tandem Repeat tools (Delgrange and Rivals, 2004). To individuals who need to focus just on microsatellites, SSR-specific web applications such as EasySSR, Misa-web (Beier et al., 2017), Websat (Martins et al., 2009), SSR Locator (da Maia et al., 2008), and Imperfect SSR finder (Stieneke and Eujayl, 2007) may be more appropriate due to their specific range of motifs.

Batch Primer3 (You et al., 2008), Websat (Martins et al., 2009), and Polymorph predict (Das et al., 2019), in contrast to EasySSR, have integrated the primer design function. Nevertheless, at the time this work was being produced, Polymorph predict (Das et al., 2019) was malfunctioning by running only their native sample data (“Chromosome 2”) instead of the user input. Websat (Martins et al., 2009) restricts accepting input files containing more than 150,000 characters. Furthermore, its primary focus lies in designing primers for a limited number of manually selected SSRs, making it unsuitable for users needing comprehensive, automated online analysis on a large scale, a capability provided by BatchPrimer3 and EasySSR. BatchPrimer3 (You et al., 2008) functions well for large-scale primer analysis and SSR screening because the output is a list containing the identified SSRs and their respective flanking primers with details, statistics, and outputs in HTML, Text file, and Excel, but it does not analyze imperfect and compound SSRs, nor does it determine whether they are in coding or non-coding regions, and it does not perform online sample comparison like EasySSR.

The command-line version of Misa (Thiel et al., 2003; Beier et al., 2017) is a versatile tool that provides analysis of perfect and compound SSRs, being one of the gold standards in SSR mining. Many tools, such as Polymorph predict (Das et al., 2019), integrate Misa in their analysis, while others write additional advanced scripts to process Misa outputs, such as Galasso and Ponzoni (2015). However, many of the applications are limited to computational experts who can develop scripts or at least comprehend how to execute them in the command-line. For non-experienced users, command-line tools are not as user-friendly as online services. Misa also has a web-server, but it does not provide the user all the features and capabilities of the command line, accepting only a single file with a maximum size of 2 Mb as input. Unfortunately, many users may find this to be a significant impediment to their research because a single prokaryote genome may be larger than 2 Mb. Misa-web results are two files: raw SSR data and statistics, not shown on a web interface but instead transmitted over email. On the other hand, EasySSR is able to process many genomes in a single run, with no maximum or minimum size limit, and summarize and compare them. It analyzes not only perfect and compound SSRs but also imperfect SSRs, offering the user the flexibility to include or exclude imperfects from their SSR mining. By running IMEX (Mudunuri and Nagarajaram, 2007) for SSR identification, EasySSR has the same or greater accuracy than Misa, as shown through the benchmark tests in Table 2. Furthermore, EasySSR is a web-based service that offers more functionalities with the same analysis as command-line tools, identifies coding/non-coding regions, and performs the post-processing and data comparison instead of giving the user only the raw data as output.

Among the webtools, Imperfect SSR finder (Stieneke and Eujayl, 2007) and EasySSR are the only ones to be able to analyze perfect, imperfect, and compound SSR. However, even though Imperfect SSR finder has no cap for input size, it does not accept more than one FASTA file, does not compare samples, has no information in the output about flanking sequences or the SSR position in coding non-coding regions, and does not generate user-friendly outputs as charts.

An overall comparison of EasySSR and the most-cited 10 web tools for SSR mining shows that EasySSR clearly distinguishes itself by being a web tool that accepts for input both multi-FASTA and multiple FASTA files, in the same run, without a maximum size limit. Among all web tools, EasySSR is the only one to have the same features as command-line tools, being able to identify coding/non-coding information if an annotation file is uploaded, compare large datasets, and return processed outputs for online or local analyses.

EasySSR was run two times for the dataset containing 54 complete genomes of C. pseudotuberculosis (CP): i) With custom parameters, mining perfect SSR only, and ii) With default parameters, mining both perfect and imperfect SSR.

With EasySSR, which also runs IMEx as the microsatellite mining tool, it was possible to locate all SSR in coding and non-coding regions and to visualize the proportion through charts (Figures 4, 5) or generate new charts from the data available in the EasySSR statistic, motif frequency, and summary tables (Figure 6). The analysis for perfect SSR only was completed within 5 min and 38 s (Figure 9), while the analysis for perfect and imperfect took 8 min and 41 s (Figure 4). The complete output datasheets for perfect SSR and perfect/imperfect analysis of the 54 complete genomes of C. pseudotuberculosis are available in Supplementary Table S1.

The EasySSR quantitative results for perfect SSR were in concordance with those stated by Pinheiro et al. (2022), as demonstrated in Table 4, and the current analysis included further comparison of the motif classes proportions. In total, 2,891 perfect SSR, 2,613 in coding regions, and 278 in non-coding regions were found, with 30 mono, 11 di, 1,301 tri, 1,201 tetra, 189 penta, and 159 hexanucleotides as proportions demonstrated in Figure 9A and with data and accession numbers available in Table 4 ordered by sequence name. The genomes had an average incidence of 53,5 perfect SSRs. Most genomes have less than 57 SSRs, ranging from 48 (CP_262, equi biovar) to 57. CP_258, CP_38, CP_CIP and CP_MEX30 (equi biovar), were the only ones to have 57 perfect SSR, however the distribution of those microsatellites is not the same in all four sequences. As shown in Figure 9B, in CP_258 and CP_CIP, their distribution pattern (Simple Sequence Repeats Signature) is 49 SSR in coding to 8 SSR in non-coding regions, with 0 mono, 0 di, 25 tri, 23 tetra, 6 penta, and 3 hexanucleotides in both strains. Meanwhile, the distribution for CP_38 (2.33731 mb) is 47 coding/10 non-coding, with 0 mono, 2 di, 24 tri, 23 tetra, 6 penta, and 2 hexanucleotides, while the distribution for CP_MEX30 (2.33751 mb) was 50 coding/7 non-coding, 3 mono, 1 di, 24 tri, 24 tetra, 6 penta, and 3 hexanucleotides.

In the analysis where imperfect microsatellites were allowed, the Simple Sequence Repeats Signature changed. The total of the SSRs identified was 68,942 SSR, 60,390 in coding regions, and 8,552 in non-coding regions, with 50 mono, 4,268 di, 37,411 tri, 23,025 tetra, 2,524 penta, and 1,664 hexanucleotides, with a proportion of 2,146 perfect SSRs to 66,796 imperfect SSRs (Figure 4). The genomes had an average incidence of 40 perfect SSRs and 1,237 imperfect SSRs per genome, as shown in the data summarized in Figures 6, 10 through different visualization modes, with Figure 6B representing the output as shown in the EasySSR output page and Figure 10 showing the complete table ordered by sequence name for better comparison with Table 4 (Perfect SSRs output). The perfect SSRs found ranges from 33 (CP_262, equi biovar) to 44 (CP_PAT10, ovis biovar). CP_258, CP_CIP, CP_38, and CP_MEX30 had, respectively 40, 40, 38, and 43 perfect SSRs. The distribution of perfect SSRs was the same in CP_258 and CP_CIP with Mono: 0; Di: 0; Tri: 18; Tetra: 17; Penta: 2; and Hexa: 3. It is possible to notice that when mismatches were allowed in a tract, EasySSR through the IMEx algorithm could extend tracts that were previously interrupted by an imperfection and considered as perfect because it had passed the repetition cutoff when they were actually part of longer imperfect tracts; thus, the average amount of perfect SSRs per genome decreased from 53.5 to 40 in the analysis that included imperfections.

Laskar et al. (2021), (2022), Jilani and Ali (2022) used similar information about incidence, prevalence, composition, and localization in their studies of Simple Sequence Repeats Signature in viruses using IMEx. Those analyses might seem basic, but they require a lot of data tabulation before the tables are ready for analysis, a feature that is already automated by EasySSR. This is a small demonstration of the versatility of EasySSR output, which made this analysis possible in minutes due to the processed information given as a result, allowing the researcher to invest their time in further analysis that otherwise would be too time demanding.

EasySSR bar charts show the top 10 most-frequent motifs present in all the strains (Figure 5). They are interactive graphs that can be used to remove specific strains from visualization or verify how many times that specific motif was found in different loci in that genome. In this way, it is possible to verify that the GCT, TGC, and GCA were present in all the 54 genomes used by Pinheiro et al. (2022). The amount of GCT motifs present in a genome varied from 26 to 37 different loci, for example, (Figure 5A). It might present itself as a useful shortcut tool to marker development. Pinheiro et al. (2022) identified CAC and GGAA as putative markers based on their differential localization in the biovars. EasySSR did not reach the same results for those markers as it has a different approach, where the bar charts demonstrate quantitatively how many times the motif appears in each genome and ranks them based on how many genomes of the dataset are present, aiming to find motifs that are common to all sequences. However, EasySSR can also be used for analysis, such as the one conducted by Pinheiro et al. (2022), as their EasySSR summary table contains information about the motif, iteration, and position (start and end), and it is easily downloadable in friendly formats such as “xlsx” and “.csv” that can be imported for further analysis using others statistic tools present in the R programming language, for example,. In this way, EasySSR outputs are versatile and can be used as a guide for visual analysis through the interactive graphs or processed by other tools with any approach the user wants.