WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics

被引:33
作者
Elizarraras, John M. [1 ]
Liao, Yuxing [1 ]
Shi, Zhiao [1 ]
Zhu, Qian [1 ,2 ]
Pico, Alexander R. [3 ]
Zhang, Bing [1 ,2 ]
机构
[1] Baylor Coll Med, Lester & Sue Smith Breast Ctr, Houston, TX 77030 USA
[2] Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA
[3] Gladstone Inst, Inst Data Sci & Biotechnol, San Francisco, CA 94158 USA
基金
美国国家卫生研究院;
关键词
ENRICHMENT ANALYSIS;
D O I
10.1093/nar/gkae456
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org. Graphical Abstract
引用
收藏
页码:W415 / W421
页数:7
相关论文
共 28 条
  • [1] scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
    Alquicira-Hernandez, Jose
    Sathe, Anuja
    Ji, Hanlee P.
    Quan Nguyen
    Powell, Joseph E.
    [J]. GENOME BIOLOGY, 2019, 20 (01)
  • [2] RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes
    Braisted, John
    Patt, Andrew
    Tindall, Cole
    Sheils, Timothy
    Neyra, Jorge
    Spencer, Kyle
    Eicher, Tara
    Mathe, Ewy A.
    [J]. BIOINFORMATICS, 2023, 39 (01)
  • [3] Machine learning for multi-omics data integration in cancer
    Cai, Zhaoxiang
    Poulos, Rebecca C.
    Liu, Jia
    Zhong, Qing
    [J]. ISCIENCE, 2022, 25 (02)
  • [4] RefMet: a reference nomenclature for metabolomics
    Fahy, Eoin
    Subramaniam, Shankar
    [J]. NATURE METHODS, 2020, 17 (12) : 1173 - 1174
  • [5] Construction of a human cell landscape at single-cell level
    Han, Xiaoping
    Zhou, Ziming
    Fei, Lijiang
    Sun, Huiyu
    Wang, Renying
    Chen, Yao
    Chen, Haide
    Wang, Jingjing
    Tang, Huanna
    Ge, Wenhao
    Zhou, Yincong
    Ye, Fang
    Jiang, Mengmeng
    Wu, Junqing
    Xiao, Yanyu
    Jia, Xiaoning
    Zhang, Tingyue
    Ma, Xiaojie
    Zhang, Qi
    Bai, Xueli
    Lai, Shujing
    Yu, Chengxuan
    Zhu, Lijun
    Lin, Rui
    Gao, Yuchi
    Wang, Min
    Wu, Yiqing
    Zhang, Jianming
    Zhan, Renya
    Zhu, Saiyong
    Hu, Hailan
    Wang, Changchun
    Chen, Ming
    Huang, He
    Liang, Tingbo
    Chen, Jianghua
    Wang, Weilin
    Zhang, Dan
    Guo, Guoji
    [J]. NATURE, 2020, 581 (7808) : 303 - +
  • [6] Mapping the Mouse Cell Atlas by Microwell-Seq
    Han, Xiaoping
    Wang, Renying
    Zhou, Yincong
    Fei, Lijiang
    Sun, Huiyu
    Lai, Shujing
    Saadatpour, Assieh
    Zhou, Zimin
    Chen, Haide
    Ye, Fang
    Huang, Daosheng
    Xu, Yang
    Huang, Wentao
    Jiang, Mengmeng
    Jiang, Xinyi
    Mao, Jie
    Chen, Yao
    Lu, Chenyu
    Xie, Jin
    Fang, Qun
    Wang, Yibin
    Yue, Rui
    Li, Tiefeng
    Huang, He
    Orkin, Stuart H.
    Yuan, Guo-Cheng
    Chen, Ming
    Guo, Guoji
    [J]. CELL, 2018, 172 (05) : 1091 - +
  • [7] Pathway information extracted from 25 years of pathway figures
    Hanspers, Kristina
    Riutta, Anders
    Summer-Kutmon, Martina
    Pico, Alexander R.
    [J]. GENOME BIOLOGY, 2020, 21 (01) : 273
  • [8] Multi-omics approaches to disease
    Hasin, Yehudit
    Seldin, Marcus
    Lusis, Aldons
    [J]. GENOME BIOLOGY, 2017, 18
  • [9] Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges
    Khatri, Purvesh
    Sirota, Marina
    Butte, Atul J.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2012, 8 (02)
  • [10] A proteogenomics data-driven knowledge base of human cancer
    Liao, Yuxing
    Savage, Sara R.
    Dou, Yongchao
    Shi, Zhiao
    Yi, Xinpei
    Jiang, Wen
    Lei, Jonathan T.
    Zhang, Bing
    [J]. CELL SYSTEMS, 2023, 14 (09) : 777 - +