Xiaochen Bo

Academy of Military Medical Sciences

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Mingxia Gao

Fudan University

Xu Li

Kennedy Krieger Institute

Wei Xie

Center for Life Sciences

Jianjun Yang

Air Force Medical University

Roger R. Reddel

University of Sydney

Charles Pineau

Musée de l'Homme

Zemin Zhang

Center for Life Sciences

Xiao Yang

Beijing Proteome Research Center

Xianwen Ren

Peking University

Chenxi Jia

Institute of Zoology

Cooperative Institutions

Chinese Academy of Sciences

Chinese PLA General Hospital

Chinese Academy of Medical Sciences & Peking Union Medical College

University of Chinese Academy of Sciences

Beijing Proteome Research Center

Academy of Military Medical Sciences

Peking University

Shanghai Jiao Tong University

Sun Yat-sen University

Fudan University

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π-HuB: the proteomic navigator of the human body

Nature (2024)

Fuchu He Ruedi Aebersold Mark S. Baker Xiu‐Wu Bian Xiaochen Bo

Human proteome project

Human body

10.1038/s41586-024-08280-5

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Citations (5)

Author Correction: High-resolution annotation of the mouse preimplantation embryo transcriptome using long-read sequencing

Nature Communications (2021)

Yunbo Qiao Chao Ren Shisheng Huang Jie Yuan Xingchen Liu

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22148-6

10.1038/s41467-021-22148-6

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Citations (1)

High-resolution annotation of the mouse preimplantation embryo transcriptome using long-read sequencing

Nature Communications (2020)

Yunbo Qiao Chao Ren Shisheng Huang Jie Yuan Xingchen Liu

Abstract The transcriptome of the preimplantation mouse embryo has been previously annotated by short-read sequencing, with limited coverage and accuracy. Here we utilize a low-cell number transcriptome based on the Smart-seq2 method to perform long-read sequencing. Our analysis describes additional novel transcripts and complexity of the preimplantation transcriptome, identifying 2280 potential novel transcripts from previously unannotated loci and 6289 novel splicing isoforms from previously annotated genes. Notably, these novel transcripts and isoforms with transcription start sites are enriched for an active promoter modification, H3K4me3. Moreover, we generate a more complete and precise transcriptome by combining long-read and short-read data during early embryogenesis. Based on this approach, we identify a previously undescribed isoform of Kdm4dl with a modified mRNA reading frame and a novel noncoding gene designated XLOC_004958 . Depletion of Kdm4dl or XLOC_004958 led to abnormal blastocyst development. Thus, our data provide a high-resolution and more precise transcriptome during preimplantation mouse embryogenesis.

10.1038/s41467-020-16444-w

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Citations (28)

Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing

Nature Communications (2023)

Jinlong Shi Zhilong Jia Jinxiu Sun Xiao-Reng Wang Xiaojing Zhao

Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Indel

Epigenomics

10.1038/s41467-023-44034-z

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Citations (2)

Author Correction: π-HuB: the proteomic navigator of the human body

Nature (2024)

Fuchu He Ruedi Aebersold Mark S. Baker Xiu‐Wu Bian Xiaochen Bo

Human body

10.1038/s41586-024-08555-x

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Citations (1)

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Nature Communications (2024)

Hao Li Zebei Han Yu Sun Fu Wang Pengzhen Hu

Abstract Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1 , PPM1A and DLG2 . We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

10.1038/s41467-024-50426-6

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Citations (5)

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Frontiers in Bioengineering and Biotechnology (2022)

Jijun Yu Luoxuan Wang Xiangya Kong Yang Cao Mengmeng Zhang

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

10.3389/fbioe.2022.819583

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Citations (6)

Continual integration of single-cell multimodal data with MIRACLE

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Jiahao Zhou 眞智子平賀 Jing Wang Shuofeng Hu Tongtong Kan

Single-cell sequencing technologies have revolutionized our understanding of cellular heterogeneity and facilitated the construction of multi-omics cell atlases via data integration. However, updating these atlases with new data conventionally requires reintegration of all data and is computationally intensive, hindering timely updates and dynamic adjustments in biological and medical research. To address this challenge, we present Multimodal Integration with Continual Learning (MIRACLE), a novel online learning framework for the adaptive and efficient integration of single-cell multimodal data. MIRACLE employs dynamic architectures and data rehearsal strategies to support continual learning, allowing diverse data to be integrated while minimizing information loss over time. Our evaluations demonstrate that MIRACLE achieves accurate online integration with reduced computational requirements, effectively updating and expanding atlases with new cross-tissue and cross-modal data, and precisely identifying novel cell types and transferring labels across datasets. MIRACLE provides an efficient and flexible tool for single-cell community to integrate, share and explore biological knowledge from single-cell multimodal data.

Miracle

10.1101/2024.09.24.613833

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