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导致免疫基因组学在研究适应性免疫曲目中的广泛适用性。这些技术能够生成大规模数据集，这些数据集可在包括免疫学在内的广泛的生物领域中使用。该项目将提供系统计算资源，以了解适应性免疫系统的机制和演变。通过提供易于使用的数据库的形式，通过将建议的生物信息学方法应用于各种和大规模的基因组数据集合中，以易于使用的数据库的形式来实现这一点。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汇集在一起，从而导致这些社区之间的互动更强。此外，该项目将开发一个交互式教育平台，用于使用基于Python的交互式笔记本的大数据分析技术学习和培训。在线平台将专门针对先前接触计算科学的学生进行量身定制。该平台将在国家一级为教学机构入学的教职员工提供。该项目将开发有效且可扩展的生物信息学方法，以改善当前V（D）J参考数据库，并表征跨多种脊椎动物的T和B细胞受体库。具体而言，该项目将开发1）从下一代测序数据中组装V（d）J等位基因的强大而可扩展的方法，2）从下一代测序数据中组装B和T细胞受体库的准确和健壮的物种和菌株特异性方法。此外，该项目将通过在数十万个样品中应用开发的方法来丰富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集的易于使用的数据库共享。开发的数据库将允许生命科学研究人员系统地比较脊椎动物物种的受体变异的体细胞事件，并提供对适应性免疫演变的新见解。该项目的结果可以在https://github.com/mangul-lab-usc/immune-repertoires-vertebrate-species中找到。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查审查标准来通过评估来获得支持的。

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