Difference between revisions of "Journal Club"
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+ | |+style="text-align:left;font-size:12pt" | 2024-2 scOmics | ||
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+ | !scope="col" style="padding:.4em" | Date | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1101/2024.04.04.24305313 Single-cell RNA sequencing of human tissue supports successful drug targets] | ||
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+ | [https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces] | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis] | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|HB Lee | ||
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+ | [https://doi.org/10.1101/2024.09.09.611960 Mouse-Geneformer: A Deep Learning Model for Mouse Single-Cell Transcriptome and Its Cross-Species Utility] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.17632/wdxwy8gmrz.1 Systematic Functional Annotation and Visualization of Biological Networks] | ||
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+ | [https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data] | ||
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+ | [https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model] | ||
+ | |} | ||
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+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2024-2 Microbiome | ||
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+ | !scope="col" style="padding:.4em" | Date | ||
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+ | |style="padding:.4em;"|YR Kim | ||
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+ | [https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1038/s41564-024-01728-4 Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1038/s41467-024-52561-6 Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants] | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
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+ | [https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes] | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-62 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling] | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-61 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function] | ||
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+ | |style="padding:.4em;"|24-60 | ||
+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics] | ||
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+ | |style="padding:.4em;"|YR Kim | ||
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+ | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses] | ||
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+ | [https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.06.27.600934 Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics | ||
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+ | !scope="col" style="padding:.4em" | Date | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | !scope="col" style="padding:.4em" | Paper title | ||
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+ | |style="padding:.4em;"|HB Lee | ||
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+ | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01314-7 scDrugPrio: a framework for the analysis of single‑cell transcriptomics to address multiple problems in precision medicine in immune‑mediated inflammatory diseases] | ||
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+ | [https://doi.org/10.1038/s41591-024-02856-4 A visual-language foundation model for computational pathology] | ||
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+ | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains] | ||
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+ | [https://doi.org/10.1016/j.ccell.2023.12.013 Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer] | ||
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+ | [https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data] | ||
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+ | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking] | ||
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+ | [https://doi.org/10.1016/j.xgen.2024.100553 Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1101/2024.04.15.589472 Nicheformer: a foundation model for single-cell and spatial omics] | ||
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+ | [https://doi.org/10.1101/2023.05.29.542705 Large Scale Foundation Model on Single-cell Transcriptomics] | ||
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+ | [https://doi.org/10.1038/s41592-024-02201-0 scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1158/0008-5472.CAN-23-2650 The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes] | ||
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+ | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity] | ||
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+ | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets] | ||
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+ | [https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells] | ||
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+ | [https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID] | ||
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+ | [https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes] | ||
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+ | [https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease] | ||
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+ | [https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors] | ||
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+ | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis] | ||
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+ | [https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
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+ | [https://doi.org/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
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+ | [https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions] | ||
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+ | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning] | ||
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+ | [https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3] | ||
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+ | [https://doi.org/10.1186/s40168-023-01737-1 Gut microbiome-metabolome interactions predict host condition] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning] | ||
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+ | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome] | ||
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+ | [https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes] | ||
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+ | [https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory] | ||
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+ | [https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses] | ||
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+ | |style="padding:.4em;"|SM Han | ||
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+ | [https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution] | ||
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+ | |style="padding:.4em;"|HJ Choi | ||
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+ | [https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy] | ||
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+ | [https://doi.org/10.1038/s41467-021-27464-5 Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids] | ||
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+ | |style="padding:.4em;"|HJ Cha | ||
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+ | [https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves] | ||
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+ | [https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer] | ||
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+ | [https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas] | ||
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+ | [https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development] | ||
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+ | [https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function] | ||
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+ | [https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex] | ||
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+ | [https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq] | ||
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+ | [https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection] | ||
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+ | |style="padding:.4em;"|EB Yu | ||
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+ | [https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection] | ||
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+ | |style="padding:.4em;"|DY Won | ||
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+ | [https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes] | ||
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+ | [https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses] | ||
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+ | [https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition] | ||
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+ | [https://doi.org/10.1038/s41593-023-01455-9 Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain] | ||
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+ | [https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma] | ||
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+ | [https://doi.org/10.1016/j.celrep.2024.113784 Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease] | ||
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+ | [https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity] | ||
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+ | [https://doi.org/10.1038/s41467-021-26271-2 Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions] | ||
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+ | [https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide] | ||
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+ | [https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism] | ||
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+ | [https://doi.org/10.1038/s41556-023-01316-4 Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation] | ||
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+ | [https://doi.org/10.1038/s41467-022-35319-w Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases] | ||
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+ | [https://doi.org/10.1016/j.cmet.2022.07.010 Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment] | ||
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+ | [https://doi.org/10.1038/s41593-023-01334-3 Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease] | ||
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+ | [https://doi.org/10.1136/gutjnl-2023-330243 Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis] | ||
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+ | [https://doi.org/10.1038/s41467-022-31519-6 Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis] | ||
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+ | [https://doi.org/10.1016/j.ccell.2023.09.011 Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma] | ||
+ | |} | ||
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+ | [https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples] | ||
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+ | [https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication] | ||
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+ | [https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells] | ||
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+ | [https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references] | ||
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+ | [https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data] | ||
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+ | |- | ||
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+ | [https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors] | ||
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+ | [https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours] | ||
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+ | [https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates] | ||
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+ | [https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution] | ||
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+ | [https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation] | ||
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+ | [https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer] | ||
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+ | [https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection] | ||
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+ | [https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis] | ||
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+ | [https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea] | ||
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+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data] | ||
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+ | [https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community] | ||
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+ | [https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules] | ||
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+ | [https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines] | ||
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+ | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani] | ||
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+ | [https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI] | ||
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+ | [https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts] | ||
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+ | [https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire] | ||
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+ | [https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment] | ||
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+ | [https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring] | ||
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+ | [https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases] | ||
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+ | [https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan] | ||
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+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS | ||
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+ | [https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
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+ | [https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension] | ||
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+ | [https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS] | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/14 | ||
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+ | |style="padding:.4em;"|23-19 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-18 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/30 | ||
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+ | |style="padding:.4em;"|23-17 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/23 | ||
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+ | |style="padding:.4em;"|23-16 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/06/16 | ||
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+ | |style="padding:.4em;"|23-15 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/02 | ||
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+ | |style="padding:.4em;"|23-14 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-13 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/05/19 | ||
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+ | |style="padding:.4em;"|23-12 | ||
+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;" rowspan=1|2023/05/12 | ||
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+ | |style="padding:.4em;"|23-11 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/04/28 | ||
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+ | |style="padding:.4em;"|23-10 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-9 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/03/10 | ||
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+ | |style="padding:.4em;"|23-7 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-6 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-5 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-4 | ||
+ | |style="padding:.4em;"|SH Lee | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;" rowspan=1|2022/11/29 | ||
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+ | |style="padding:.4em;"|JH Cha, SB Baek, IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2022/11/22 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|22-30 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2022/11/08 | ||
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+ | |style="padding:.4em;"|22-29 | ||
+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;" rowspan=1|2022/10/11 | ||
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+ | |style="padding:.4em;"|22-28 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|22-26 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;" rowspan=1|2022/09/01 | ||
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+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;"|IS Choi | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|22-19 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning] | ||
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+ | [https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease] | ||
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+ | [https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome] | ||
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+ | [https://www.cell.com/cell-reports/pdf/S2211-1247(22)00770-7.pdf Thousands of small, novel genes predicted in global phage genomes] | ||
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+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01231-0 MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations] | ||
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+ | [https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype] | ||
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+ | [https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease] | ||
+ | [https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges] | ||
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+ | |style="padding:.4em;"|SH Ann | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia] | ||
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+ | [https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs] | ||
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+ | [https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity] | ||
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+ | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01011-3 Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34880502/ Gut microbiota modulates weight gain in mice after discontinued smoke exposure] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34819672/ The human microbiome encodes resistance to the antidiabetic drug acarbose] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes] | ||
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+ | |} | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data] | ||
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+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation] | ||
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+ | [https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro] | ||
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+ | [https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains] | ||
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+ | [https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations] | ||
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+ | [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts] | ||
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+ | [https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract] | ||
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+ | [https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets] | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics] | ||
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+ | [https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer] | ||
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+ | [https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells] | ||
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+ | [https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing] | ||
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+ | [https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer] | ||
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+ | [https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma] | ||
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+ | [https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation] | ||
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+ | [https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome] | ||
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+ | [https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences] | ||
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+ | [https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease] | ||
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+ | [https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals] | ||
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+ | [https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases] | ||
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+ | [https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis] | ||
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+ | |style="padding:.4em;"|SR You | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
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+ | |style="padding:.4em;"|HH Eom | ||
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+ | |style="padding:.4em;"|JH Park | ||
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+ | [https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice] | ||
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+ | [https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk] | ||
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+ | [https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome] | ||
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+ | [https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms] | ||
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+ | [https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach] | ||
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+ | |} | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy] | ||
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+ | [https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases] | ||
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+ | [https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy] | ||
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+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2020-1st semester | ||
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+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-11 | ||
+ | |style="padding:.4em;"|JH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/06/02 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-10 | ||
+ | |style="padding:.4em;"|HY Seo | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-9 | ||
+ | |style="padding:.4em;"|KH Hong | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/26 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-8 | ||
+ | |style="padding:.4em;"|JY Seong | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-7 | ||
+ | |style="padding:.4em;"|OY Min | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/19 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-6 | ||
+ | |style="padding:.4em;"|SN Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-5 | ||
+ | |style="padding:.4em;"|DJ Park | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/12 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-4 | ||
+ | |style="padding:.4em;"|SY Park | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-3 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/04/28 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|20-2 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2019-2nd semester | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" stype="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/15 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-51 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-50 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/08 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-49 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-48 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/01 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-47 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-46 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/09/24 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-45 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-44 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/09/17 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-43 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-42 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/09/10 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-41 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-40 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/09/03 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-39 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-38 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2019 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/20 | ||
+ | |style="padding:.4em;"|19-37 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-36 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/08/14 | ||
+ | |style="padding:.4em;"|19-35 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-34 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-33 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/13 | ||
+ | |style="padding:.4em;"|19-32 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-31 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/08 | ||
+ | |style="padding:.4em;"|19-30 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-29 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/07 | ||
+ | |style="padding:.4em;"|19-28 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-27 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/07/30 | ||
+ | |style="padding:.4em;"|19-26 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-25 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/07/23 | ||
+ | |style="padding:.4em;"|19-24 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-23 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/05/30 | ||
+ | |style="padding:.4em;"|19-22 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-21 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-20 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/05/23 | ||
+ | |style="padding:.4em;"|19-19 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-18 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-17 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/16 | ||
+ | |style="padding:.4em;"|19-16 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-15 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/09 | ||
+ | |style="padding:.4em;"|19-14 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-13 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/02 | ||
+ | |style="padding:.4em;"|19-12 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-11 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/4/11 | ||
+ | |style="padding:.4em;"|19-10 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-9 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/4/4 | ||
+ | |style="padding:.4em;"|19-8 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-7 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/3/28 | ||
+ | |style="padding:.4em;"|19-6 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-5 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/3/21 | ||
+ | |style="padding:.4em;"|19-4 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-3-1 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-3 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/3/14 | ||
+ | |style="padding:.4em;"|19-2 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-1 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2018 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/06/14 | ||
+ | |style="padding:.4em;"|18-12 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-11 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/06/07 | ||
+ | |style="padding:.4em;"|18-10 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-9 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/31 | ||
+ | |style="padding:.4em;"|18-8 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-7 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/24 | ||
+ | |style="padding:.4em;"|18-6 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-5 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/17 | ||
+ | |style="padding:.4em;"|18-4 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-3 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/10 | ||
+ | |style="padding:.4em;"|18-2 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-1 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.] | ||
+ | |} | ||
{|class=wikitable style="text-align:center;" | {|class=wikitable style="text-align:center;" | ||
|+style="text-align:left;font-size:12pt" | 2017 | |+style="text-align:left;font-size:12pt" | 2017 | ||
Line 7: | Line 3,398: | ||
!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=2|2017/ | + | |style="padding:.4em;" rowspan=2|2017/06/28 |
− | |style="padding:.4em;"|17- | + | |style="padding:.4em;"|17-36 |
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-35 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/06/14 | ||
+ | |style="padding:.4em;"|17-34 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-33 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/06/07 | ||
+ | |style="padding:.4em;"|17-32 | ||
|style="padding:.4em;"| | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-31 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/31 | ||
+ | |style="padding:.4em;"|17-30 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-29 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/24 | ||
+ | |style="padding:.4em;"|17-28 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-27 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/17 | ||
+ | |style="padding:.4em;"|17-26 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-25 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/26 | ||
+ | |style="padding:.4em;"|17-24 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-23 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/12 | ||
+ | |style="padding:.4em;"|17-22 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-21 | ||
+ | |style="padding:.4em;"|CY Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.] | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/05 | ||
+ | |style="padding:.4em;"|17-20 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.] | ||
|- | |- | ||
|style="padding:.4em;"|17-19 | |style="padding:.4em;"|17-19 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|SH Lee |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10. | + | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.] |
|- | |- | ||
|style="padding:.4em;" rowspan=2|2017/03/29 | |style="padding:.4em;" rowspan=2|2017/03/29 | ||
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|style="padding:.4em;"| | |style="padding:.4em;"| | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10. | + | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.] |
|- | |- | ||
|style="padding:.4em;"|17-17 | |style="padding:.4em;"|17-17 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|DS Bae |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/ | + | [https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.] |
|- | |- | ||
|style="padding:.4em;" rowspan=2|2017/03/22 | |style="padding:.4em;" rowspan=2|2017/03/22 | ||
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|style="padding:.4em;"| | |style="padding:.4em;"| | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/ | + | [https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.] |
|- | |- | ||
|style="padding:.4em;"|17-15 | |style="padding:.4em;"|17-15 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|EB Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/ | + | [https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.] |
|- | |- | ||
|style="padding:.4em;" rowspan=2|2017/03/15 | |style="padding:.4em;" rowspan=2|2017/03/15 | ||
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|style="padding:.4em;"| | |style="padding:.4em;"| | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/ | + | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.] |
|- | |- | ||
|style="padding:.4em;"|17-13 | |style="padding:.4em;"|17-13 | ||
|style="padding:.4em;"| | |style="padding:.4em;"| | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.ncbi.nlm.nih.gov/pubmed/ | + | [https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.] |
|- | |- | ||
|style="padding:.4em;" rowspan=2|2017/02/28 | |style="padding:.4em;" rowspan=2|2017/02/28 |
Latest revision as of 16:15, 16 October 2024
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2024/06/18 | Single-cell | 24-32 | EB Hong |
Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory |
2024/06/18 | Single-cell | 24-31 | JJ Heo |
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses |
2024/06/18 | Single-cell | 24-30 | SM Han |
Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution |
2024/06/18 | Single-cell | 24-29 | HJ Choi | |
2024/06/11 | Single-cell | 24-28 | SA Choi | |
2024/06/11 | Single-cell | 24-27 | HJ Cha |
Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics |
2024/06/11 | Single-cell | 24-26 | YK Jung | |
2024/06/11 | Single-cell | 24-25 | HJ Lee | |
2024/06/04 | Single-cell | 24-24 | HK Lee |
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas |
2024/06/04 | Single-cell | 24-23 | JI Lee |
Multimodal spatiotemporal phenotyping of human retinal organoid development |
2024/06/04 | Single-cell | 24-22 | JH Lee | |
2024/06/04 | Single-cell | 24-21 | JH Lee |
A single-cell analysis of the Arabidopsis vegetative shoot apex |
2024/05/28 | Single-cell | 24-20 | JH Lee |
Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq |
2024/05/28 | Single-cell | 24-19 | YH Lee | |
2024/05/28 | Single-cell | 24-18 | EB Yu | |
2024/05/28 | Single-cell | 24-17 | DY Won |
Spatial metatranscriptomics resolves host–bacteria–fungi interactomes |
2024/05/21 | Single-cell | 24-16 | SG Oh | |
2024/05/21 | Single-cell | 24-15 | SY Park | |
2024/05/21 | Single-cell | 24-14 | HS Moon | |
2024/05/21 | Single-cell | 24-13 | JH Nam |
Spatial cellular architecture predicts prognosis in glioblastoma |
2024/05/14 | Single-cell | 24-12 | HS Na | |
2024/05/14 | Single-cell | 24-11 | PK Kim |
Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity |
2024/05/14 | Single-cell | 24-10 | SH Kwon | |
2024/05/14 | Single-cell | 24-9 | Q Zhen | |
2024/05/07 | Single-cell | 24-8 | CR Leenaars | |
2024/05/07 | Single-cell | 24-7 | YR Kim | |
2024/05/07 | Single-cell | 24-6 | JY Kim |
Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases |
2024/05/07 | Single-cell | 24-5 | WJ Kim |
Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment |
2024/04/23 | Single-cell | 24-4 | G Koh |
Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease |
2024/04/23 | Single-cell | 24-3 | SH Ahn | |
2024/04/23 | Single-cell | 24-2 | EJ Sung | |
2024/04/23 | Single-cell | 24-1 | HJ Kim |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2023/08/30 | Single-cell | 23-24 | JW Yu | |
2023/08/09 | Single-cell | 23-23 | IS Choi |
Major data analysis errors invalidate cancer microbiome findings |
2023/08/02 | Single-cell | 23-22 | EJ Sung | |
2023/07/26 | Single-cell | 23-21 | G Koh | |
2023/07/19 | Single-cell | 23-20 | JW Yu |
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
2023/07/12 | Single-cell | 23-19 | JH Cha |
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data |
2023/07/05 | Single-cell | 23-18 | SB Baek |
Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance |
2023/06/28 | Single-cell | 23-17 | EJ Sung | |
2023/06/21 | Single-cell | 23-16 | IS Choi | |
2023/06/14 | Single-cell | 23-15 | G Koh | |
2023/05/31 | Single-cell | 23-14 | JW Yu |
Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma |
2023/05/24 | Single-cell | 23-13 | JH Cha | |
2023/05/17 | Single-cell | 23-12 | SB Baek | |
2023/05/10 | Single-cell | 23-11 | EJ Sung |
Supervised discovery of interpretable gene programs from single-cell data |
2023/05/03 | Single-cell | 23-10 | IS Choi |
Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer |
2023/04/26 | Single-cell | 23-9 | G Koh | |
2023/03/22 | Single-cell | 23-8 | JW Yu |
MetaTiME: Meta-components of the Tumor Immune Microenvironment |
2023/03/08 | Single-cell | 23-7 | JH Cha | |
2023/02/21 | Single-cell | 23-6 | SB Baek | |
2023/02/14 | Single-cell | 23-5 | EJ Sung |
A T cell resilience model associated with response to immunotherapy in multiple tumor types |
2022/01/31 | Single-cell | 23-4 | IS Choi | |
2023/01/25 | Single-cell | 23-3 | G Koh | |
2023/01/17 | Single-cell | 23-2 | JW Yu |
Pan-cancer integrative histology-genomic analysis via multimodal deep learning |
2023/01/11 | Single-cell | 23-1 | JH Cha |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/11/23 | Single-cell | 21-39 | IS Choi | |
2021/11/16 | Single-cell | 21-38 | SB Back | |
2021/11/09 | Single-cell | 21-37 | JH Cha | |
2021/11/02 | Single-cell | 21-36 | SB Baek |
Functional Inference of Gene Regulation using Single-Cell Multi-Omics |
2021/10/26 | Single-cell | 21-35 | IS Choi | |
2021/10/19 | Single-cell | 21-34 | JH Cha | |
2021/10/05 | Single-cell | 21-33 | JH Cha |
Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma |
2021/09/28 | Single-cell | 21-32 | SB Baek | |
2021/09/14 | Single-cell | 21-31 | IS Choi | |
2021/09/07 | Single-cell | 21-30 | JH Cha |
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer |
2021/08/31 | Single-cell | 21-29 | IS Choi |
Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma |
2021/08/24 | Single-cell | 21-28 | SB Baek |
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/02/22 | Single-cell | 21-8 | IS Choi |
Functional CRISPR dissection of gene networks controlling human regulatory T cell identity |
21-7 | JH Cha |
Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy | ||
2021/02/15 | Single-cell | 21-6 | SB Baek | |
21-5 | IS Choi |
Trajectory-based differential expression analysis for single-cell sequencing data | ||
2021/02/08 | Single-cell | 21-4 | SB Baek |
Genetic determinants of co-accessible chromatin regions in activated T cells across humans |
21-3 | JH Cha |
Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer | ||
2021/02/01 | Single-cell | 21-2 | JW Cho | |
21-1 | JW Cho |