TFvalidator is a comprehensive and user-friendly interactive web platform designed to predict transcription factor activity by integrating multi-omic data, including ATAC-seq, KnockTF, and ChIP-seq. This platform not only streamlines complex data analysis workflows but also effectively guides wet lab experiments, enhancing research efficiency and accuracy.
Chenshen Huang12†
, Haoxue Zhang345†, Yuyao Liu6, Ning Wang7
1. Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
2. Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
3. Department of Dermatovenerology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
4. Key Laboratory of Dermatology, Ministry of Education, Hefei, China.
5. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Hefei, China.
6. Bioinformatics R&D Department, Hefei GuangRe Biotechnology Co., Ltd
7. Department of Central Laboratory, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
† These authors contributed equally to this work.
Corresponding Author.
Citation: The article is currently being written.
Introduction
Currently, although there are numerous databases available for predicting transcription factors, these resources present several issues that hinder researchers' efficiency and the depth of their studies. Firstly, these databases are widely dispersed and fragmented, lacking centralized management and integration, which inconveniences users. A significant reason for this is that most databases have not been effectively optimized for search, making it difficult for researchers to find relevant information. Secondly, existing databases typically focus on specific aspects or characteristics of transcription factors, lacking comprehensiveness and systematic coverage, thus failing to meet the needs of interdisciplinary research. Lastly, many databases pay insufficient attention to the visualization of results, making it challenging for researchers to intuitively understand and apply the data even after obtaining it. Given these challenges, there is a pressing need to develop a comprehensive database that integrates the aforementioned research findings, provides efficient search capabilities, offers broad and thorough information, and emphasizes excellent data visualization. Such a platform would not only significantly enhance research efficiency but also foster interdisciplinary collaboration, accelerating innovation and development in the life sciences.
ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing): ATAC-seq enables high-throughput detection of open chromatin regions across the entire genome, which are typically sites where transcription factors bind. The high-resolution data provided by ATAC-seq helps in the precise identification of potential transcription factor binding sites. Beyond identifying these binding sites, ATAC-seq can also reveal changes in chromatin states, such as the activity of promoters and enhancers, thereby providing a more comprehensive understanding of gene expression regulation.
Knockout TF RNA-seq：By knocking out specific transcription factors and observing changes in gene expression, one can directly validate the function of these transcription factors. This approach allows for a clear understanding of the role of transcription factors within gene regulatory networks. CRISPR-Cas9 technology, with its high specificity, can precisely target specific transcription factors, thereby minimizing off-target effects. This method can be applied to various cell types and tissues, facilitating the study of transcription factor functions in different biological contexts. Additionally, it enables the observation of both immediate and long-term effects following the knockout of transcription factors, revealing their roles at different time points.
ChIP-seq (Chromatin Immunoprecipitation followed by sequencing): ChIP-seq can directly detect the binding sites of specific transcription factors across the entire genome, providing a detailed map of transcription factor binding. By using specific antibodies to enrich for DNA fragments bound by transcription factors, ChIP-seq offers high sensitivity and specificity, allowing for the accurate identification of binding sites. This technique is applicable to a wide range of sample types, including cell lines, tissues, and clinical samples, enabling the study of transcription factor binding patterns under various conditions.
Advantages of Integrated Application: ATAC-seq provides information on open chromatin regions, revealing which areas are potentially bound by transcription factors. ChIP-seq directly detects the binding sites of specific transcription factors, confirming their exact binding locations. RNA sequencing after knocking out a transcription factor (TF) validates the TF's function by observing changes in gene expression, thereby determining its regulatory role. Integrating data from these techniques allows for the construction of a more complete gene regulatory network, uncovering the dynamic changes in transcription factor activity under different conditions. For example, combining ATAC-seq and ChIP-seq data can identify which open chromatin regions are bound by specific transcription factors, and RNA sequencing after TF knockout can validate the functional significance of these binding sites. Each technique provides a different type of data, and their integrated analysis enhances the depth and breadth of the research, providing rich information for subsequent experimental design and functional validation. Specifically, ATAC-seq offers insights into chromatin accessibility, ChIP-seq provides binding site information, and RNA sequencing after TF knockout reveals changes in gene expression. Together, these methods provide a more comprehensive understanding of the regulatory mechanisms of transcription factors. Individual techniques may have their limitations. For instance, ChIP-seq might be constrained by antibody specificity, but ATAC-seq and RNA sequencing after TF knockout can provide additional evidence, enhancing the credibility of the conclusions. In summary, ATAC-seq, RNA sequencing after TF knockout, and ChIP-seq each have unique advantages. By integrating these techniques, researchers can achieve a more thorough and in-depth study of transcription factor functions and regulatory mechanisms, providing robust support for gene expression regulation research.
Statistics
Our Team
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Chenshen Huang, M.D., Ph.D. Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital Fuzhou China Email: chenshenhuang@126.com Research Focus: Transcriptional Regulation | Machine Learning | Tumor Immunology Main Contributions: Project Lead |
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Haoxue Zhang, MD Anhui Medical University Hefei China Email: 215672062@qq.com Research Focus: Cutaneous Melanoma | Bioinformatics Main Contributions: Database Paper Writing | Project Lead |
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Yuyao Liu, Postgraduate Bioinformatics R&D Department, Hefei GuangRe Biotechnology Co., Ltd Hefei China Email: ahmulyy@163.com Research Focus: Cutaneous Melanoma | R Package Development | Web Scraping with R | Database Construction Main Contributions: Database Development | Database Operations | Project Lead |
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Ning Wang, Ph.D. Huzhou Central Hospital Huzhou China Email: wning2425@163.com Research Focus: Transcriptional Regulation | Tumor Immunology Main Contributions: Database Development | Project Lead |
Help
Introduction
Data collection and processing
Function of TFvalidator
Starting from a Database
Starting with a gene of interest
Abbreviations
FAQ
Download
1. Is there a way to download all datasets in a batch?
Reply: Unfortunately, we cannot provide the raw data download since we don't own these data. Users could download the related data with the 'analysis' menu.
2. How to download the pictures of high resolution in TFvalidator?
TFvalidator offers vector graphics in PDF format, allowing users to download and display them with clarity. Additionally, users can freely adjust the resolution of the images according to their needs.
Acknowledgements
Thanks
We would like to express our sincere gratitude to all the researchers and contributors who have made their data openly available. The success of this study would not have been possible without the generous sharing of datasets, which has greatly facilitated our research and analysis. We are deeply appreciative of the commitment and dedication of the scientific community to open science, which fosters collaboration and accelerates scientific progress.
Author contributions
Chenshen Huang: For expertise in database design and implementation, and for leading the data integration efforts. For work on the database's security and privacy features, ensuring the protection of sensitive data.
Haoxue Zhang: For extensive work on data curation and quality control, ensuring the accuracy and reliability of the dataset. For contributions to the project management and coordination, keeping the project on track and ensuring timely delivery
Yuyao Liu: For developing the user interface and improving the overall user experience of the database. For critical role in data analysis and interpretation, providing valuable insights into the data. For expertise in data visualization, creating insightful and interactive visual representations of the data.
Ning Wang: For tireless efforts in testing and debugging the database, ensuring its robustness and functionality. For contributions to the documentation and user guides, making the database accessible and user-friendly. For support in data validation and for valuable feedback throughout the development process.
Funding information
1. Joint Funds for the Innovation of Science and Technology, Fujian Province (Grant number: 2023Y9299).
2. China Postdoctoral Science Foundation (Grant Number: 2024M750448).
3. The Natural Science Foundation of Zhejiang Province (Grant Number: LQ24H100001).
4. This research was funded in part by the Sparkle Database (Internal Grant No. GRSWSCITOP202401). More details can be found at https://grswsci.top/.
Competing Interests
We have declared that no competing interest exists.
Public Databases integrated
1. TCGA: The Immune Landscape of Cancer (The Cancer Genome Atlas Research Network, 2018).
References
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Updates
Terms of Use
TFvalidator is a database maintained by The Sparkle Research Network, providing comprehensive expression resources and functional analysis for transcriptional regulation. The Sparkle Research Network may update the content on https://grswsci.top/TFvalidator/ ("Content") from time to time. The Sparkle Research Network makes no warranties or representations, express or implied, regarding any of the Content, including but not limited to its current accuracy, completeness, timeliness, adequacy, or usefulness. By using this website, you agree that The Sparkle Research Network will not be liable for any losses or damages arising from your use of or reliance on the Content, or other websites or information to which this website may be linked.
TFvalidator is freely accessible for research use in an academic setting. You may view the Content solely for your own personal reference or for research in an academic setting. All academic research use of the Content must credit TFvalidator as the source and reference these Terms of Use. Outside of scientific publications, you may not redistribute or share the Content, in part or in whole, with any third party for any purpose without the express permission of The Sparkle Research Network.
Unless you have signed a license agreement with The Sparkle Research Network, you may not use any part of the Content for any of the following purposes:
1. Use or incorporation into a commercial product or towards the performance of a commercial service;
2. Research use in a commercial setting;
3. Use for patient services; or
4. generation of reports in a hospital or other patient care setting. You may not copy, transfer, reproduce, modify, or create derivative works of TFvalidator for any commercial purpose without the express permission of Sparkle Network. If you seek to use TFvalidator for such purposes, please request the appropriate license below:
5. Research use in a commercial setting
6. Use in a commercial product
7. Use for patient services or reports in a hospital setting Please contact us at ahmulyy@163.com.
Start preparation time 2024-11-01
Pilot run time 2024-11-20
Formal running time 2024-12-01
This study is part of the Sparkle Platform and operates as a dedicated sub-project within its ecosystem