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

基本信息

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

项目摘要

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得免疫基因组学在适应性免疫库研究中得到广泛应用。这些技术能够生成大规模数据集，可用于包括免疫学在内的广泛生物领域。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。这将通过提供强大且易于使用的开源软件以及通过在多样化和大规模基因组数据集上应用所提出的生物信息学方法来组装的易于使用的数据库形式的经验结果来实现。该项目将促进跨学科合作，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，从而加强这些社区之间的互动。此外，该项目还将开发一个交互式教育平台，使用基于Python的交互式笔记本来学习和培训大数据分析技术。该在线平台将专门为之前接触过计算科学的学生量身定制。该平台将在全国范围内向教学机构的教师和学生开放。该项目将开发高效且可扩展的生物信息学方法，以改进当前的 V(D)J 参考数据库并表征各种脊椎动物物种的 T 和 B 细胞受体库。具体来说，该项目将开发 1) 稳健且可扩展的方法，用于从下一代测序数据组装 V(D)J 等位基因，2) 准确且稳健的物种和菌株特异性方法，用于从下一代测序数据组装 B 和 T 细胞受体库。生成测序数据。此外，该项目还将通过在数十万个样本中应用开发的方法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数据库。为了促进所获得的结果的传播，组装的免疫受体序列将作为具有丰富功能的易于使用的数据库进行共享。开发的数据库将使生命科学研究人员能够系统地比较引起脊椎动物物种受体变异的体细胞事件，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 找到。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

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

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

A comprehensive benchmarking of WGS-based deletion structural variant callers