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NeoHunter:Flexible software for systematically detecting neoantigens from sequencing data

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Quantitative Biology 2024年第1期12卷 70-84页

作者： Tianxing Ma Zetong Zhao Haochen Li Lei Wei Xuegong Zhang moe key lab of bioinformatics Bioinformatics Division of BNRIST and Department of AutomationTsinghua UniversityBeijingChina School of Medicine Tsinghua UniversityBeijingChina School of Life Sciences Tsinghua UniversityBeijingChina

Complicated molecular alterations in tumors generate various mutant *** of these mutant peptides can be presented to the cell surface and then elicit immune responses,and such mutant peptides are called *** detection of neoantigens could help to design personalized cancer *** some computational frameworks for neoantigen detection have been proposed,most of them can only detect SNV-and indel-derived *** addition,current frameworks adopt oversimplified neoantigen prioritization *** factors hinder the comprehensive and effective detection of *** developed NeoHunter,flexible software to systematically detect and prioritize neoantigens from sequencing data in different *** can detect not only SNV-and indel-derived neoantigens but also gene fusion-and aberrant splicing-derived *** supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate *** strategies utilize binding characteristics,existing biological big data,and T-cell receptor specificity to ensure accurate detection and *** applied NeoHunter to the TESLA dataset,cohorts of melanoma and non-small cell lung cancer *** achieved high performance across the TESLA cancer patients and detected 79%(27 out of 34)of validated neoantigens in ***-and indel-derived neoantigens accounted for 90%of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%.Gene fusion-derived neoantigens were detected in one *** is a powerful tool to‘catch all’neoantigens and is available for free academic use on Github(Xuegonglab/NeoHunter).

关键词： cancer vaccine molecular alteration neoantigen neoantigen prioritization

Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data

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Journal of Genetics and Genomics 2022年第9期49卷 891-899页

作者： Yanhong Wu Qifan Hu Shicheng Wang Changyi Liu Yiran Shan Wenbo Guo Rui Jiang Xiaowo Wang Jin Gu moe key laboratory of bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing 100084China

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)*** with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity *** mathematically find the optimal expression patterns that can maximize the proposed scoring *** comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation ***,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

关键词： Single-cell RNA-sequencing Feature selection Spatially resolved transcriptomic data Regional patterns Graphical models

Toward a unified information framework for cell atlas assembly

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National Science Review 2022年第3期9卷 7-10页

作者： Sijie Chen Yanting Luo Haoxiang Gao Fanhong Li Jiaqi Li Yixin Chen Renke You Hairong Lv Kui Hua Rui Jiang Xuegong Zhang bioinformatics division of bnrist and Department of Automation MOE Key Lab of BioinformaticsTsinghua University Fuzhou Institute of Data Technology School of Medicine Tsinghua University School of Life Sciences Center for Synthetic and Systems Biology Tsinghua University

Cells are the basic structural and functional unit of the human body. Virtually all cells of a human body possess the same genome, but they exhibit colorful heterogeneities in phenotypes and *** heterogeneities includ...

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TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

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Genomics, Proteomics & bioinformatics 2022年第5期20卷 974-988页

作者： Yiran Shan Qian Zhang Wenbo Guo Yanhong Wu Yuxin Miao Hongyi Xin Qiuyu Lian Jin Gu moe key laboratory of bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing 100084China UM-SJTU Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome ***,ST data analysis is still complicated by high technical noises and low *** addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST *** matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST *** present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and *** devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed *** on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood *** benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art *** show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological *** is available at http://***/software/tist/and https://***/biocode/tools/BT007317.

关键词： Spatial transcriptomics Multimodal information integration Network-based analysis Spatial cluster identification Gene expression enhancement

Building digital life systems for future biology and medicine

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Quantitative Biology 2023年第3期11卷 207-213页

作者： Xuegong Zhang Lei Wei Rui Jiang Xiaowo Wang Jin Gu Zhen Xie Hairong Lv moe key lab of bioinformatics and bioinformatics division of bnrist Department of AutomationTsinghua UniversityBeijing 100084China Center of Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences and School of Medicine Tsinghua UniversityBeijing 100084China

The rapid development of biological technology (BT) and information technology (IT) especially of genomics and artificial intelligence (AI) is bringing great potential for revolutionizing future medicine. We propose the concept and framework of Digital Life Systems or dLife as a new paradigm to unleash this potential. It includes the multi-scale and multi-granule measure and representation of life in the digital space, the mathematical and/or computational modeling of the biology behind physiological and pathological processes, and ultimately cyber twins of healthy or diseased human body in the virtual space that can be used to simulate complex biological processes and deduce effects of medical treatments. We advocate that dLife is the route toward future AI precision medicine and should be the new paradigm for future biological and medical research.

关键词： digital life systems digital twin aritificial intelligence precision medicine

人工智能与多组学时代基于生物网络的肿瘤精准防治

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Science Bulletin 2023年第12期68卷 1219-1222页

作者：张鹏汪博洋李梢 Institute of TCM-X/moe key laboratory of bioinformatics Bioinformatics DivisionBNRist/Department of AutomationTsinghua UniversityBeijing 100084China

Cancer is recognized as a leading cause of deaths nowadays,accounting for 10 million annual deaths worldwide and resulting in heavy health and economic burdens[1].Reducing cancer incidence through precision prevention is thus of great scientific and clinical *** cancer(GC)is one of the most common cancers in ***,the onset of GC,especially the intestinal-type ones,would usually suffer a series of premalignant lesions,including atrophic gastritis,intestinal metaplasia,and low-grade dysplasia(LGD),which is known as the process of gastritis-induced tumorigenesis(GIGT)[2].Epidemiological studies have shown that GIGT has exhibited the characteristics of longterm and low-probability,that is,only a small number of gastritis patients would develop into GC after a long ***,it has become the key challenge for GC precision prevention that how to uncover the biological mechanism underlying GIGT,followed by accurately identifying the critical time point at which effective diagnosis and treatment strategies could be developed to prevent *** note,this is also a common challenge for the precision prevention of other cancer types,such as liver and colon cancers.

关键词： prevention gastritis diagnosis

Creating a semi-opened micro-cavity ovary through sacrificial microspheres as an in vitro model for discovering the potential effect of ovarian toxic agents

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Bioactive Materials 2023年第8期26卷 216-230页

作者： Min Ye Yiran Shan Bingchuan Lu Hao Luo Binhan Li Yanmei Zhang Zixuan Wang Yuzhi Guo Liliang Ouyang Jin Gu Zhuo Xiong Ting Zhang Biomanufacturing Center Department of Mechanical EngineeringTsinghua UniversityBeijing100084China Biomanufacturing and Rapid Forming Technology key laboratory of Beijing Beijing100084China Biomanufacturing and Engineering Living Systems Innovation International Talents Base(111 Base)Beijing100084China moe key laboratory of bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing100084China

The bio-engineered ovary is an essential technology for treating female *** the development of relevant in vitro models could be a critical step in a drug ***,we develop a semi-opened culturing system(SOCS)strategy that maintains a 3D structure of follicles during the *** on the SOCS,we further developed micro-cavity ovary(MCO)with mouse follicles by the microsphere-templated technique,where sacrificial gelatin microspheres were mixed with photo-crosslinkable gelatin methacryloyl(GelMA)to engineer a micro-cavity niche for follicle *** semi-opened MCO could support the follicle growing to the antral stage,secreting hormones,and ovulating cumulus-oocyte complex out of the MCO without extra *** MCO-ovulated oocyte exhibits a highly similar transcriptome to the in vivo counterpart(correlation of 0.97)and can be ***,we found that a high ROS level could affect the cumulus expansion,which may result in anovulation *** damage could be rescued by melatonin,but the end of cumulus expansion was 3h earlier than anticipation,validating that MCO has the potential for investigating ovarian toxic agents in *** provide a novel approach for building an in vitro ovarian model to recapitulate ovarian functions and test chemical toxicity,suggesting it has the potential for clinical research in the future.

关键词： Bio-engineered ovary SOCS Semi-opened MCO Ovarian model Drug study

VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

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Genomics, Proteomics & bioinformatics 2018年第5期16卷 320-331页

作者： Dongfang Wang Jin Gu moe key laboratory of bioinformatics BNRIST Bioinformatics Division & Center for Synthetic and Systems BiologyDepartment of Automation Tsinghua University

Single-cell RNA sequencing(scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell subpopulations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data(VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.

关键词： Single cell RNA sequencing Deep variational autoencoder Dimension reduction Visualization Dropout

Yanda Li:A never-stopping pioneer,investigator and founder

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Quantitative Biology 2022年第1期10卷 1-5页

作者： Xuegong Zhang Jin Gu moe key laboratory of bioinformatics Bioinformatics DivisionBNRIST and Department of AutomationTsinghua UniversityBeijing 100084China School of Life Sciences and School of Medicine Tsinghua UniversityBeijing 100084China

Yanda Li was bom in October 1936 in Dongguan,Guangdong,*** childhood was in war *** had to move with his parents across several counties and cities,but he managed to accomplish his high-school study in Guangzhou(Fig.1).He showed deep interest in physics in high school and was admitted to the Department of Electrical Engineering of Tsinghua University in *** of his excellent performance in college study,he joined the faculty in 1958 before he finished his college study in *** the 60 years from Figure *** Li in his high-school ***:Yanda Li,unknown photographer.

关键词： admitted Figure finished