数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (4): 179-190.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.04.013

doi: 10.11871/jfdc.issn.2096-742X.2026.04.013

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FaaS在生物信息领域应用综述

王晓光1(),曹荣强1,*(),万萌1,李凯1,王彦棡1,2,王珏1,2   

  1. 1 中国科学院计算机网络信息中心， 北京 100083
    2 中国科学院大学， 北京 100049
  • 收稿日期:2025-08-23 出版日期:2026-08-20 发布日期:2026-08-21
  • 通讯作者: 曹荣强（E-mail：caorq@cnic.cn）
  • 作者简介:王晓光，中国科学院计算机网络信息中心，高级工程师，主要研究方向为人工智能平台。
    本文承担主要工作为整理数据和论文撰写。
    WANG Xiaoguang is a Senior Engineer at the Computer Network Information Center, Chinese Academy of Sciences. His research interests include artificial intelligence platforms.
    In this paper, he is responsible for data organization and manuscript writing.
    E-mail: wangxg@cnic.cn|曹荣强，中国科学院计算机网络信息中心，副研究员，主要研究方向为人工智能平台。
    本文中负责整体规划、论文指导。
    CAO Rongqiang is an associate researcher at the Computer Network Information Center, Chinese Academy of Sciences. His main research direction is artificial intelligence platforms.
    In this paper, he is responsible for overall planning and guidance on the manuscript.
    E-mail: caorq@cnic.cn
  • 基金资助:
    国家重点研发计划“创新药物发现技术体系的集成/优化与示范应用”(2022YFF1203004)

FaaS-Enabled Computing for Bioinformatics: a Comprehensive Review

WANG Xiaoguang1(),CAO Rongqiang1,*(),WAN Meng1,LI Kai1,WANG Yangang1,2,WANG Jue1,2   

  1. 1 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2025-08-23 Online:2026-08-20 Published:2026-08-21

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摘要：

【目的】 系统化地梳理生物信息领域计算模式从传统高性能计算（High Performance Computing，HPC）到函数即服务（Function as a Service，FaaS）的演进路径，结合生物信息领域应用场景对FaaS的概念以及平台特征、HPC与FaaS融合模式进行分析。【文献范围】 本文调研了2003-2024年间来自主流会议、期刊以及主流云厂商平台文档的70余篇文献。【方法】 采用文献调研与架构对比相结合的方法，按照计算模式演变脉络梳理生物信息学应用，界定FaaS及主流平台特性；并选取基因组学、测序、遗传学与数据挖掘等代表性场景进行应用案例归纳，结合其局限探讨与HPC结合的三种模式。【结果】 FaaS在多类生信任务中可简化部署、提升弹性并加速研究迭代，同时逐渐以“HPC加云、云中HPC、HPC即服务”三种模式不断融合，适用不同场景的需求。【结论】 FaaS应用已在基因组学、测序、遗传学与数据挖掘等生信领域取得了成功，但仍存在资源受限等一系列问题。相信未来会有更多将FaaS与传统HPC相融合的解决方案。

关键词: FaaS, 无服务器计算, 生物信息学计算, HPC

Abstract:

[Objective] This study systematically traces the evolution of computing models in bioinformatics from traditional high-performance computing (HPC) to Function as a Service (FaaS). In light of bioinformatics application scenarios, it analyzes the concept of FaaS, its platform characteristics, and integration models between HPC and FaaS. [Literature Scope] This paper reviews more than 70 publications from mainstream conferences and journals, as well as.documentation from major cloud platforms, published between 2003 and 2024. [Methods] This study combines a literature review and architectural comparison to computation models evolution in bioinformatics, characterize FaaS and leading platforms, synthesize use cases in genomics, sequencing, genetics, and data mining, and evaluate three FaaS-HPC integration modes. [Results] FaaS simplifies deployment, enhances elasticity, and accelerates research cycles across bioinformatics tasks, while increasingly integrating via three models—HPC+cloud, HPC in the cloud, and HPC-as-a-Service—to meet diverse scenario needs. [Conclusions] FaaS has been successfully appllied in genomics, sequencing, genetics, and data mining, but challenges such as resource limitations remain. In the future, more solution integrating FaaS with traditional HPC are expected.

Key words: FaaS, serverless computing, bioinformatics, HPC