Molecular Plant
Volume 16, Issue 11, 6 November 2023, Pages 1733-1742
Journal home page for Molecular Plant

Resource article
TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining

https://doi.org/10.1016/j.molp.2023.09.010Get rights and content

Abstract

Since the official release of the stand-alone bioinformatics toolkit TBtools in 2020, its superior functionality in data analysis has been demonstrated by its widespread adoption by many thousands of users and references in more than 5000 academic articles. Now, TBtools is a commonly used tool in biological laboratories. Over the past 3 years, thanks to invaluable feedback and suggestions from numerous users, we have optimized and expanded the functionality of the toolkit, leading to the development of an upgraded version—TBtools-II. In this upgrade, we have incorporated over 100 new features, such as those for comparative genomics analysis, phylogenetic analysis, and data visualization. Meanwhile, to better meet the increasing needs of personalized data analysis, we have launched the plugin mode, which enables users to develop their own plugins and manage their selection, installation, and removal according to individual needs. To date, the plugin store has amassed over 50 plugins, with more than half of them being independently developed and contributed by TBtools users. These plugins offer a range of data analysis options including co-expression network analysis, single-cell data analysis, and bulked segregant analysis sequencing data analysis. Overall, TBtools is now transforming from a stand-alone software to a comprehensive bioinformatics platform of a vibrant and cooperative community in which users are also developers and contributors. By promoting the theme “one for all, all for one”, we believe that TBtools-II will greatly benefit more biological researchers in this big-data era.

Introduction

Bioinformatic data analysis has become an indispensable part of biological research. Different research projects have distinct data analysis needs. To handle massive amounts of biological data, researchers need to master the use of numerous bioinformatics software, especially those only available under a command-line environment, and assemble them into a practical workflow. This presents a great challenge for most wet-lab researchers and biologists. To ease this predicament, we published the first version of TBtools software (Chen et al., 2020), which includes over 130 featured functions. It provides a viable option for researchers and is widely used in the biology community. In recent years, the field of plant genomics and bioinformatics has undergone significant advancements. For instance, an increasing number of Telomere-to-Telomere (T2T) and haplotype-resolved genomes have been released, making genome-based research and comparative genomics more feasible for various plant species (Naish et al., 2021; Sun et al., 2022; Shang et al., 2023; Shi et al., 2023). In response, the TBtools software has undergone continuous updates and adaptations to remain at the forefront of these developments.

To date, TBtools has been installed and used on many thousands of computers, and the toolkit has been referenced in more than 5000 academic articles. With the growth of the TBtools user community, we constantly receive feedback and suggestions from our daily interactions with users, most of whom are frontline biological researchers. We have come to realize that a dilemma is arising in biological data analysis. On the one hand, different users have distinct demands for data analysis. They want to do personalized analysis given the specific biological process in which they are interested. Each researcher desires specific data analysis tools or workflows to retrieve the best results. Therefore, we have been asked to incorporate more and more functions into TBtools to meet the increasing demands of this type of personalized data analysis, for instance, analysis of different next-generation sequencing data generated from various sequencing strategies, such as chromatin immunoprecipitation sequencing, DNA affinity purification and sequencing, and bulked segregant analysis sequencing (BSA-seq). On the other hand, the development and addition of new features to TBtools (over 100 since the initial publication) has made the toolkit overly multifaceted and cumbersome, diluting its original focus on functions of broad interest and demand. This in turn has made it difficult for users to quickly locate the functions they need. Furthermore, an increase in the size of the stand-alone software would create complications in terms of distribution, installation, and employment. To alleviate this dilemma, we developed an updated version, TBtools-II, in which we have added a range of new features including a plugin mode. This mode enables users to develop their own plugins and manage their installation, selection, and removal according to their needs. We have extensively tested these new features and plugins. They have proven to be a valuable addition to TBtools and will benefit the growing plant biology user community.

Section snippets

Function enhancements

Thanks to valuable feedback and suggestions from users, we have not only improved the current functionalities of TBtools but have also added a number of innovative features spanning diverse topics, including genomic data analyses, comparative genomics, phylogenetics, omics data analysis, and graphics configuration (Table 1).

BLAST Zone: A new function for advanced comparative genomic analysis

Although there are several BLAST-based tools available for comparing two sequences or sequence files, including those present in the first release of TBtools, there is still

Discussion

Since its official public release in 2020, TBtools has been used by hundreds of thousands of researchers and has gained far more attention than expected. Over the past 3 years, we have strived to deliver a great user experience while refining the data analysis efficiency of TBtools. The core functionality has been optimized and upgraded by the incorporation of more than 100 new functions, resulting in this upgraded version—TBtools-II. As an integral part of this upgrade, we introduced the

Data and code availability

TBtools-II is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases. Demo data can be freely downloaded from https://tbtools.cowtransfer.com/s/631b6a609d354e.

All tests were conducted using six threads, with a peak memory footprint at around 12 GB on a personal computer with a single Intel Core i5 processor (12600KF), 16 GB RAM, and 500 GB storage space and jobs completed in less than 5 h.

Funding

This work is supported by the Key Area Research and Development Program of Guangdong Province (2022B0202070003, and 2021B0707010004). This work is also supported by the National Science Foundation of China (#32072547, and #32102320); the National Key Research and Development Program (2021YFF1000101, and 2019YFD1000500); the Special Support Program of Guangdong Province (2019TX05N193); the Scientific Research Foundation of the Hunan Provincial Education Department (20A261), ); and the open

Author contributions

C.C. and R.X. conceived the project; C.C. and R.X. and designed the functions of the toolkit. C.C. performed all the Java coding. J.L. and X.W. developed the “Seurat ShinyApp” and “WGCNA shinyApp” plugins, respectively. Y.W., Z.Z., J.X., Y.L., J.F., H.C., and Y.H. tested the functions and helped with the preparation of the tutorial manual. C.C. and R.X. prepared the figures and wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We thank all labmates in the Xia lab, the He lab, and the Xu lab for their generous help. We express our gratitude to all the TBtools plugin developers. We extend our appreciation to Guiyang Watchbio Co., Ltd., Yang Shao, Qi Zhao, and Jianming Zeng for their constructive feedback in building the TBtools ecosystem. We are also grateful for the kind advice from many thousands of TBtools users, especially the >40 advanced users. No conflict of interest is declared.

References (28)

  • N. Fernandez-Pozo et al.

    The Sol Genomics Network (SGN)—from genotype to phenotype to breeding

    Nucleic Acids Res.

    (2014)
  • E.P. Garrison et al.

    Haplotype-based variant detection from short-read sequencing

    arXiv

    (2012)
  • B. Giardine et al.

    Galaxy: A platform for interactive large-scale genome analysis

    Genome Res.

    (2005)
  • E. Greenfest-Allen et al.

    iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

    bioRxiv

    (2017)
  • Cited by (113)

    View all citing articles on Scopus

    Published by the Molecular Plant Shanghai Editorial Office in association with Cell Press, an imprint of Elsevier Inc., on behalf of CSPB and CEMPS, CAS.

    View full text