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GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis.
Literature Information
| DOI | 10.1093/nar/gkz430 |
|---|---|
| PMID | 31114875 |
| Journal | Nucleic acids research |
| Impact Factor | 13.1 |
| JCR Quartile | Q1 |
| Publication Year | 2019 |
| Times Cited | 2645 |
| Keywords | Gene Expression Analysis, Cancer Subtype, Transcriptome, RNA-seq, API |
| Literature Type | Journal Article, Research Support, Non-U.S. Gov't |
| ISSN | 0305-1048 |
| Pages | W556-W560 |
| Issue | 47(W1) |
| Authors | Zefang Tang, Boxi Kang, Chenwei Li, Tianxiang Chen, Zemin Zhang |
TL;DR
GEPIA2 is an enhanced version of the original GEPIA web server, providing advanced gene expression analysis with higher resolution by integrating data from 198,619 isoforms and 84 cancer subtypes. This updated tool allows users to conduct detailed comparisons and custom analyses of RNA-seq data against TCGA and GTEx samples, significantly improving research capabilities in cancer genomics.
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Gene Expression Analysis · Cancer Subtype · Transcriptome · RNA-seq · API
Abstract
Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.
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Primary Questions Addressed
- How does GEPIA2 improve upon the original GEPIA in terms of data resolution and functionalities?
- What specific new analysis techniques from single-cell sequencing studies have been incorporated into GEPIA2?
- Can you elaborate on the types of custom analyses users can perform with their own RNA-seq data in GEPIA2?
- In what ways does GEPIA2 facilitate the comparison of gene expression across different cancer subtypes?
- How does the inclusion of 198,619 isoforms enhance the analysis capabilities of GEPIA2 compared to other gene expression profiling tools?
Key Findings
Research Background and Objectives
The GEPIA (Gene Expression Profiling Interactive Analysis) web server, introduced in 2017, has been a critical resource for gene expression analysis using tumor and normal samples from TCGA and GTEx databases. The aim of this study is to present GEPIA2, an enhanced version that offers higher resolution insights and more functionalities, such as the ability to analyze gene expression at the transcript level and focus on specific cancer subtypes.
Main Methods/Materials/Experimental Design
GEPIA2 expands upon the original GEPIA by incorporating new features and functionalities, allowing for detailed gene expression analysis. The methodology includes:
- Data Sources: Utilizes re-computed isoform expression data from UCSC Xena based on TCGA and GTEx.
- Analysis Categories: Divided into two major sections:
- Expression Analysis: General analysis, differential gene analysis, survival analysis, isoform details, correlation analysis, similar genes detection, and dimensionality reduction.
- Custom Data Analysis: Cancer subtype classifier and expression comparison.
- User Interaction: Users can upload their RNA-seq data for comparison with TCGA and GTEx datasets.
Key Results and Findings
- Expanded Data: GEPIA2 features 198,619 isoforms and 84 cancer subtypes, enabling a more granular analysis of gene expression.
- New Functionalities:
- Survival Map: Provides heat maps showing the prognostic impact of gene expression across various cancer types.
- Isoform Usage Profiling: Visualizes the expression distribution and usage of isoforms for specific genes.
- Uploaded Data Comparison: Allows users to perform differential expression analysis with their own data against public datasets.
- Cancer Subtype Classifier: Predicts cancer types and subtypes based on user-uploaded samples.
Main Conclusions/Significance/Innovation
GEPIA2 significantly enhances the capabilities of the original GEPIA platform, offering advanced tools for analyzing gene expression data. The new features enable researchers to perform in-depth analyses of cancer subtypes and isoform usage, which are crucial for understanding cancer biology and identifying potential therapeutic targets. This platform is expected to become a preferred tool for biologists and clinicians exploring cancer genomics.
Research Limitations and Future Directions
- Limitations: While GEPIA2 provides advanced functionalities, the reliance on existing datasets may limit the scope of some analyses. Users must ensure their uploaded data is of high quality for accurate comparisons.
- Future Directions: Future iterations of GEPIA could include more machine learning-based predictive tools and enhanced visualization features to facilitate deeper insights into gene expression dynamics across diverse cancer types. Additionally, expanding the database with more recent and comprehensive datasets could further improve analysis accuracy.
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Literatures Citing This Work
- Cancer biology as revealed by the research autopsy. - Christine A Iacobuzio-Donahue;Chelsea Michael;Priscilla Baez;Rajya Kappagantula;Jody E Hooper;Travis J Hollman - Nature reviews. Cancer (2019)
- More Than an Adipokine: The Complex Roles of Chemerin Signaling in Cancer. - Kerry B Goralski;Ashley E Jackson;Brendan T McKeown;Christopher J Sinal - International journal of molecular sciences (2019)
- Analysis of Promoter-Associated Chromatin Interactions Reveals Biologically Relevant Candidate Target Genes at Endometrial Cancer Risk Loci. - Tracy A O'Mara;Amanda B Spurdle;Dylan M Glubb; - Cancers (2019)
- Integration of Bioinformatics Resources Reveals the Therapeutic Benefits of Gemcitabine and Cell Cycle Intervention in SMAD4-Deleted Pancreatic Ductal Adenocarcinoma. - Yao-Yu Hsieh;Tsang-Pai Liu;Chia-Jung Chou;Hsin-Yi Chen;Kuen-Haur Lee;Pei-Ming Yang - Genes (2019)
- An Integrated Bioinformatics Analysis Repurposes an Antihelminthic Drug Niclosamide for Treating HMGA2-Overexpressing Human Colorectal Cancer. - Stephen Wan Leung;Chia-Jung Chou;Tsui-Chin Huang;Pei-Ming Yang - Cancers (2019)
- MiR-92a Family: A Novel Diagnostic Biomarker and Potential Therapeutic Target in Human Cancers. - Min Jiang;Xuelian Li;Xiaowei Quan;Xiaoying Li;Baosen Zhou - Frontiers in molecular biosciences (2019)
- High Expression of TTYH3 is Related to Poor Clinical Outcomes in Human Gastric Cancer. - Subbroto Kumar Saha;Polash Kumar Biswas;Minchan Gil;Ssang-Goo Cho - Journal of clinical medicine (2019)
- Expression profile analysis of two antisense lncRNAs to improve prognosis prediction of colorectal adenocarcinoma. - Milad Shademan;Azam Naseri Salanghuch;Khadijeh Zare;Morteza Zahedi;Mohammad Ali Foroughi;Kambiz Akhavan Rezayat;Hooman Mosannen Mozaffari;Kamran Ghaffarzadegan;Ladan Goshayeshi;Hesam Dehghani - Cancer cell international (2019)
- Bioinformatic Identification of miR-622 Key Target Genes and Experimental Validation of the miR-622-RNF8 Axis in Breast Cancer. - Chuanyang Liu;Lu Min;Jingyu Kuang;Chushu Zhu;Xin-Yuan Qiu;Lingyun Zhu - Frontiers in oncology (2019)
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