HarvestEase GUI: An Evolutionary Genetics Computational Program for Offline Visualization of STRUCTURE Output

HarvestEase GUI: An Evolutionary Genetics Computational Program for Offline Visualization of STRUCTURE Output | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 April 2025 V1 Latest version Share on HarvestEase GUI: An Evolutionary Genetics Computational Program for Offline Visualization of STRUCTURE Output Authors : deepanker das 0000-0002-7822-8107 , Siddhartha Maiti , and Devojit Sarma 0000-0002-2749-4114 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174417524.43091238/v1 689 views 210 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Bayesian clustering tools such as STRUCTURE are widely used in population genetics to infer genetic structure and assess the most likely number of clusters (K) within a dataset. However, interpreting STRUCTURE output requires additional post-processing particularly for calculating the ΔK statistic using the Evanno method making the analysis dependent on external online tools like STRUCTURE HARVESTER. With the prolonged unavailability of STRUCTURE HARVESTER and the reliance of existing pipelines on web-based services, researchers face barriers in performing offline and reproducible analyses. Here, we present HarvestEase GUI, an open-source, standalone application for offline visualization and interpretation of STRUCTURE output. HarvestEase GUI integrates the original STRUCTURE HARVESTER Python scripts within a user-friendly interface and automates key steps, including batch parsing of STRUCTURE outputs, calculation of mean likelihood and variance, and graphical estimation of ΔK values. Unlike many current solutions, our tool emphasizes accessibility and platform independence, requiring no internet connection or advanced programming skills. Benchmarking with published datasets confirms that HarvestEase GUI replicates the results of STRUCTURE HARVESTER with high accuracy and consistent visualization outputs. This tool enhances current workflows in population genetics by providing (i) automated and reproducible ΔK estimation, (ii) high-quality graphical summaries of clustering likelihoods, and (iii) support for offline environments with minimal computational resources. HarvestEase GUI enables more efficient and accessible STRUCTURE analyses across a broad range of research settings and will be valuable to both computational and field-based genetic studies. Introduction Within the field of population genetics, a key objective lies in elucidating population structure, and the program STRUCTURE (Pritchard, Stephens, & Donnelly, 2000) has emerged as a cornerstone methodology for addressing this objective through Bayesian clustering algorithms. This approach facilitates the identification of genetic differentiation among groups, thereby enabling the determination of the most probable number of genetic clusters within a dataset However, the practical application of STRUCTURE often necessitates supplementary tools for result interpretation, particularly in discerning the optimal number of clusters, denoted as K, from the program’s output (Evanno et al., 2005). Among the methods developed to streamline this process, the Evanno method has gained considerable prominence, owing to its efficacy in detecting the uppermost hierarchical level of population structure (Earl & VonHoldt, 2012). In addition to determining the optimal K, interpreting STRUCTURE results also involves addressing label switching and multimodality issues across replicate runs. The program CLUMPP (Cluster Matching and Permutation Program) is widely used for this purpose, as it aligns cluster assignments across multiple replicates to ensure consistent labelling of clusters (Jakobsson & Rosenberg, 2007). This step is crucial for accurate visualization and further analysis of population structure patterns. Addressing the computational demands of population genetic analyses, several software solutions have emerged to enhance the efficiency and accessibility of programs like STRUCTURE (Zhao, Beck, Fuller, & Peatman, 2020). For instance, StrAuto was developed to automate and parallelize STRUCTURE analyses, streamlining the workflow from data preparation to result compilation, including the use of STRUCTURE HARVESTER when available (Chhatre & Emerson, 2017). Similarly, fastSTRUCTURE leverages variational Bayesian methods to offer rapid approximate inference of population structure, particularly beneficial for handling large SNP datasets (Raj, Stephens, & Pritchard, 2014). Nevertheless, challenges persist in terms of usability and platform dependency. Many of these tools lack user-friendly graphical interfaces and are heavily dependent on web-based services, creating obstacles for researchers in resource-constrained settings or those requiring offline functionality. The growing volume of genetic data and the broad applicability of STRUCTURE analysis across various biological disciplines highlight the imperative for user-accessible software that can operate offline, specifically for implementing the Evanno method; a stand-alone application fulfils a critical need for accessible, efficient, and user-friendly analysis of population structure, especially where computational resources or internet connectivity are limited (Novembre & Peter, 2016). HarvestEase GUI is a standalone Python-based graphical interface designed for processing and visualizing output data generated by STRUCTURE, incorporating a modified version of the STRUCTURE HARVESTER code to facilitate the analysis of population genetic clustering. The GUI allows users to load STRUCTURE output files, automatically detect the range of K values explored, and compute the Evanno method’s ΔK statistic, which assists in identifying the uppermost hierarchical level of population structure. The prolonged unavailability of STRUCTURE HARVESTER has posed significant challenges for researchers relying on STRUCTURE analyses using multi-locus genetic data and the development of HarvestEase GUI restores these essential functionalities and introduces advantages over web-based solutions. Tool Development and validation HarvestEase GUI is developed in Python and incorporates a Tkinter-based graphical interface for ease of use. It utilizes the original STRUCTURE HARVESTER Python scripts to ensure computational accuracy while adding a layer of automation and visualization. The software supports batch processing of STRUCTURE output files, automatically extracting key metrics such as mean likelihood per K, variance visualization, and Evanno method results. The software operates without external dependencies, making it accessible across platforms. This eliminates the need for specialized software or web-based servers, simplifying deployment. To ensure computational accuracy, we tested HarvestEase GUI using the STRUCTURE results dataset by Renee Eriksen (Eriksen, 2014) and directly compared the outputs to those previously generated by STRUCTURE HARVESTER. Comparative analysis of the estimated likelihood values across varying K values confirms that HarvestEase GUI accurately reproduces the likelihood means and variance values as originally computed by STRUCTURE HARVESTER. Moreover, the ΔK values calculated by HarvestEase GUI were also in agreement with STRUCTURE HARVESTER output. These validation steps affirm the computational reliability and accuracy of HarvestEase GUI as a standalone tool for STRUCTURE output analysis, mirroring the functionality of the original STRUCTURE HARVESTER. The final software was exported using pyinstaller bundle to ensure portability and ease of distribution across different operating systems (Figure 1). This enables even users without Python expertise to readily deploy and utilize HarvestEase GUI, thereby broadening its accessibility and impact within the scientific community. The likelihood values across K estimates (Figure 2a & 2b), demonstrate that HarvestEase GUI precisely reproduces the likelihood means and variance values obtained by STRUCTURE HARVESTER. The standard deviation bars in the likelihood plot show identical variation trends, confirming the accuracy of the calculations. Similarly, the Evanno method results including Delta K calculations and their graphical representation indicate that the most likely number of genetic clusters aligns with previously published STRUCTURE HARVESTER results, verifying the implementation’s correctness. The values of Delta K, standard deviation, and mean likelihood are identical, confirming that the. HarvestEase GUI accurately calculates and visualizes population structure, consistent with the existing program outputs. Similarly, the Evanno method results including Delta K calculations and their graphical representation indicate that the most likely number of genetic clusters aligns with previously published STRUCTURE HARVESTER results, verifying the implementation’s correctness (Figure 2c & 2d). The values of Delta K, standard deviation, and mean likelihood are identical, confirming that the new software accurately replicates the calculations of the original tool while offering an improved user experience (Table 1 & Figure 3). The prolonged unavailability of STRUCTURE HARVESTER has posed significant challenges for researchers relying on STRUCTURE analyses using multi-locus genetic data. The development of HarvestEase GUI not only restores these essential functionalities but also introduces advantages over web-based solutions. Our validation study confirms that HarvestEase GUI provides identical computational outputs and visualizations compared to STRUCTURE HARVESTER, ensuring continuity in genetic data analyses. Future work will focus on integrating additional functionalities, such as improved visualization options and expanded support for different STRUCTURE output formats. HarvestEase GUI successfully replicates and extends the capabilities of STRUCTURE HARVESTER, providing an essential offline tool for population geneticists. By incorporating the original STRUCTURE HARVESTER Python scripts and enhancing them with automation and visualization, HarvestEase GUI ensures continued accessibility for STRUCTURE users. The software delivers identical computational outputs, enhanced usability, and robust visualization capabilities. Given the prolonged unavailability of the web-based tool, HarvestEase GUI serves as a necessary and reliable alternative, ensuring uninterrupted workflow for genetic clustering analyses. Its intuitive interface simplifies the workflow, reducing the need for manual data processing. Additionally, HarvestEase GUI is open-source and cross-platform, compatible with Windows, Linux, and macOS (Apple Silicon), ensuring accessibility across different computing environments. Conclusion HarvestEase GUI successfully replicates and extends the capabilities of STRUCTURE HARVESTER, providing an essential offline tool for population geneticists. By incorporating the original STRUCTURE HARVESTER Python scripts and enhancing them with automation and visualization, HarvestEase GUI overcomes the limitations of web-dependent platforms and ensures consistent performance across diverse computing environments. The availability of HarvestEase GUI addresses the need for open-source, accessible tools in population genetics, particularly for researchers who require offline functionality and customizable solutions. The development of user-friendly software, exemplified by HarvestEase GUI, transcends mere convenience; it represents a critical advancement towards bolstering the rigor, reproducibility, and transparency of data analysis and interpretation across a spectrum of biological disciplines. Its design is strategically geared towards researchers who may lack advanced computational skills, thereby lowering the barrier to entry for sophisticated population genetic analyses. The significance of such tools is particularly pronounced in resource-constrained settings or remote field locations where consistent internet access is not guaranteed. Through its intuitive design and streamlined workflow, HarvestEase GUI not only facilitates the efficient processing and visualization of STRUCTURE output data but also empowers researchers to derive more informed insights, fostering robust conclusions grounded in sound population genetic principles. not-yet-known not-yet-known not-yet-known unknown Acknowledgments We extend our gratitude to the developers of STRUCTURE HARVESTER for their contributions to population genetics software. Data availability All the data, codes and software executable files are available in https://github.com/deepanker95/Structure_harvester_gui.git freely available under an MIT open source license. not-yet-known not-yet-known not-yet-known unknown Funding The study was funded by Indian Council of Medical Council, New Delhi (https://www.icmr.gov.in/ ) to DKS (award number 6/9-7(208)/2019-ECD-II) and fellowship to DD (award number: Fellowship/109/2022-ECD-II). Author contributions DD: Investigation, Collection of data, Methodology, Formal analysis, Visualization, Review draft. SM : Conceptualization, Methodology, Supervision, Review draft. DKS: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Funding acquisition, Project administration, Writing –original draft. References https://doi.org/10.6084/m9.figshare.1237746.v1 Chhatre, V. E., & Emerson, K. J. (2017). StrAuto: automation and parallelization of STRUCTURE analysis. BMC bioinformatics, 18 , 1-5. Earl, D. A., & VonHoldt, B. M. (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation genetics resources, 4 , 359-361. Eriksen, R. (2014). Structure Harvester Results . Retrieved from: Jakobsson, M., & Rosenberg, N. A. (2007). CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics, 23 (14), 1801-1806. Novembre, J., & Peter, B. M. (2016). Recent advances in the study of fine-scale population structure in humans. Current opinion in genetics & development, 41 , 98-105. Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155 (2), 945-959. Raj, A., Stephens, M., & Pritchard, J. K. (2014). fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics, 197 (2), 573-589. Zhao, H., Beck, B., Fuller, A., & Peatman, E. (2020). EasyParallel: A GUI platform for parallelization of STRUCTURE and NEWHYBRIDS analyses. PLoS One, 15 (4), e0232110. Table legends: Table 1: Output OF Evanno method by HarvestEase GUI. not-yet-known not-yet-known not-yet-known unknown Figure legends: Figure 1: A schematic view of the framework of HarvestEase GUI. Figure 2: Mean Estimated Ln Probability of Data by K output graph generated by Harvestease GUI (a) and STRUCTURE HARVESTER web UI (b). Rate of change of likelihood distribution output graph generated by Harvestease GUI (c) and STRUCTURE HARVESTER web UI (d). Figure 3: Screenshot of Output of Evanno method by structure harvester web UI. Supplementary Material File (table 1.docx) Download 12.15 KB Information & Authors Information Version history V1 Version 1 09 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bioinfomatics/phyloinfomatics landscape genetics molecular evolution population genetics - empirical Authors Affiliations deepanker das 0000-0002-7822-8107 ICMR National Institute for Research in Environmental Health View all articles by this author Siddhartha Maiti Vellore Institute of Technology, Bhopal View all articles by this author Devojit Sarma 0000-0002-2749-4114 [email protected] ICMR National Institute for Research in Environmental Health View all articles by this author Metrics & Citations Metrics Article Usage 689 views 210 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation deepanker das, Siddhartha Maiti, Devojit Sarma. HarvestEase GUI: An Evolutionary Genetics Computational Program for Offline Visualization of STRUCTURE Output. Authorea . 09 April 2025. DOI: https://doi.org/10.22541/au.174417524.43091238/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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