CAREER: Developing efficient and scalable bioinformatics methods and databases to analyze the adaptive immune repertoires of vertebrate species

职业：开发高效且可扩展的生物信息学方法和数据库来分析脊椎动物的适应性免疫库

• 批准号: 2041984
• 负责人: Serghei Mangul
• 金额: $ 74.43万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041984&HistoricalAwards=false
• 关键词: CAREER; Developing; efficient; scalable; bioinformatics

Recent advances in high-throughput technologies have led to the broad applicability of immunogenomics in studying the adaptive immune repertoire. These technologies are capable of generating large-scale datasets that can be used across a wide range of biological domains, including immunology. The project will provide systematics computational resources for understanding the mechanisms and evolution of adaptive immune systems. This will be achieved by delivering robust and easy-to-use open-source software as well as empirical results in form of easy-to-use databases assembled by applying proposed bioinformatics methods on diverse and large-scale genomic datasets. The project will facilitate collaborations across disciplines and will bring together researchers and students from computer science, life science, and bioinformatics, leading to stronger interactions across these communities. Additionally, the project will develop an interactive educational platform for learning and training in big data analytic techniques using python-based interactive notebooks. The online platform will be specifically tailored towards students with limited prior exposure to computational sciences. The platform will be made available at the national level for faculty and students enrolled at teaching-focused institutions. The project will develop efficient and scalable bioinformatics methods for improving current V(D)J reference databases and characterizing T and B cell receptor repertoire across a variety of vertebrate species. Specifically, the project will develop 1) robust and scalable methods to assemble V(D)J alleles from next-generation sequencing data, 2) accurate and robust species- and strain-specific methods to assemble B and T cell receptor repertoire from next-generation sequencing data. Additionally, the project will enrich existing immunogenomics databases of V(D)J alleles and receptor sequences across various vertebrate species by applying the developed methods across hundreds of thousands of samples. To promote the dissemination of obtained results, the assembled immune receptor sequences will be shared as an easy-to-use database with a rich set of functionalities. The developed database will allow life science researchers to systematically compare somatic events that give rise to receptor variation in vertebrate species and provide novel insight into the evolution of adaptive immunity. Results of the project can be found at https://github.com/Mangul-Lab-USC/immune-repertoires-vertebrate-species.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

高通量技术的最新进展使免疫基因组学在研究获得性免疫谱系方面具有广泛的适用性。这些技术能够生成可用于包括免疫学在内的广泛生物领域的大规模数据集。该项目将为理解适应性免疫系统的机制和进化提供系统学计算资源。这将通过提供强大和易于使用的开源软件以及以易于使用的数据库的形式提供经验结果来实现，这些数据库是通过对各种和大规模的基因组数据集应用拟议的生物信息学方法汇编而成的。该项目将促进跨学科的合作，并将计算机科学、生命科学和生物信息学的研究人员和学生聚集在一起，导致这些社区之间更强的互动。此外，该项目将开发一个互动教育平台，使用基于Python的互动笔记本学习和培训大数据分析技术。这个在线平台将专门为以前接触计算科学的学生量身定做。该平台将在国家层面上提供给在以教学为重点的机构注册的教师和学生。该项目将开发高效和可扩展的生物信息学方法，以改进目前的V(D)J参考数据库，并表征各种脊椎动物的T和B细胞受体谱系。具体地说，该项目将开发1)稳健和可扩展的方法，从下一代测序数据组装V(D)J等位基因；2)准确和稳健的方法，根据下一代测序数据组装B和T细胞受体谱系。此外，该项目将通过将开发的方法应用于数十万个样本来丰富各种脊椎动物物种的V(D)J等位基因和受体序列的现有免疫基因组学数据库。为了促进已有成果的传播，组装的免疫受体序列将作为一个易于使用的数据库共享，具有丰富的功能集。开发的数据库将使生命科学研究人员能够系统地比较导致脊椎动物物种受体变异的躯体事件，并为适应性免疫的进化提供新的见解。该项目的结果可以在https://github.com/Mangul-Lab-USC/immune-repertoires-vertebrate-species.This上找到，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research.

• DOI: 10.3389/fimmu.2022.954078
• 发表时间: 2022
• 期刊: FRONTIERS IN IMMUNOLOGY
• 影响因子: 7.3
• 作者: Peng, Kerui;Moore, Jaden;Vahed, Mohammad;Brito, Jaqueline;Kao, Guoyun;Burkhardt, Amanda M.;Alachkar, Houda;Mangul, Serghei
• 通讯作者: Mangul, Serghei

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

• DOI: 10.1093/bib/bbab265
• 发表时间: 2021-08-04
• 期刊: BRIEFINGS IN BIOINFORMATICS
• 影响因子: 9.5
• 作者: Nadel, Brian B.;Oliva, Meritxell;Mangul, Serghei
• 通讯作者: Mangul, Serghei

Unlocking capacities of genomics for the COVID-19 response and future pandemics.

• DOI: 10.1038/s41592-022-01444-z
• 发表时间: 2022-04
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Knyazev, Sergey;Chhugani, Karishma;Sarwal, Varuni;Ayyala, Ram;Singh, Harman;Karthikeyan, Smruthi;Deshpande, Dhrithi;Baykal, Pelin Icer;Comarova, Zoia;Lu, Angela;Porozov, Yuri;Vasylyeva, Tetyana, I;Wertheim, Joel O.;Tierney, Braden T.;Chiu, Charles Y.;Sun, Ren;Wu, Aiping;Abedalthagafi, Malak S.;Pak, Victoria M.;Nagaraj, Shivashankar H.;Smith, Adam L.;Skums, Pavel;Pasaniuc, Bogdan;Komissarov, Andrey;Mason, Christopher E.;Bortz, Eric;Lemey, Philippe;Kondrashov, Fyodor;Beerenwinkel, Niko;Lam, Tommy Tsan-Yuk;Wu, Nicholas C.;Zelikovsky, Alex;Knight, Rob;Crandall, Keith A.;Mangul, Serghei
• 通讯作者: Mangul, Serghei

RNA-seq data science: From raw data to effective interpretation.

• DOI: 10.3389/fgene.2023.997383
• 发表时间: 2023
• 期刊: FRONTIERS IN GENETICS
• 影响因子: 3.7
• 作者: Deshpande, Dhrithi;Chhugani, Karishma;Chang, Yutong;Karlsberg, Aaron;Loeffler, Caitlin;Zhang, Jinyang;Muszynska, Agata;Munteanu, Viorel;Yang, Harry;Rotman, Jeremy;Tao, Laura;Balliu, Brunilda;Tseng, Elizabeth;Eskin, Eleazar;Zhao, Fangqing;Mohammadi, Pejman;Labaj, Pawel P.;Mangul, Serghei
• 通讯作者: Mangul, Serghei

Data availability of open T-cell receptor repertoire data, a systematic assessment

• DOI: 10.3389/fsysb.2022.918792
• 发表时间: 2022-04
• 期刊: bioRxiv
• 作者: Yu-Ning Huang;Naresh Amrat Patel;Jay Himanshu Mehta;Srishti Ginjala;P. Brodin;C. Gray;Yesha M Patel;L. Cowell;A. Burkhardt;S. Mangul