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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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+ | [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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+ | [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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+ | [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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+ | [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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+ | [https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology] | ||
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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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+ | [https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology] | ||
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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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+ | [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/10.1016/j.xgen.2023.100383 Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data] | ||
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− | [https:// | + | [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:// | + | [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:// | + | [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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+ | [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:// | + | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] |
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− | [https:// | + | [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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+ | [https://doi.org/10.48550/arXiv.2303.00915 BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs] | ||
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+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
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− | [https:// | + | [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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+ | [https://doi.org/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development] | ||
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+ | [https://doi.org/10.1038/s41564-024-01751-5 A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome] | ||
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+ | [https://doi.org/10.1101/2023.12.11.571168 Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression] | ||
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+ | [https://doi.org/10.1101/2024.06.04.596112 Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations] | ||
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|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
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|- | |- | ||
− | |style="padding:.4em;" rowspan=1| | + | |style="padding:.4em;" rowspan=1|2024/07/17 |
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+ | |style="padding:.4em;"|SA Choi | ||
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+ | |style="padding:.4em;"|HJ Cha | ||
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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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+ | |style="padding:.4em;"|G Koh | ||
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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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+ | |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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+ | |} | ||
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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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+ | |style="padding:.4em;"|SH Ahn | ||
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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/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis] | ||
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+ | [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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+ | [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/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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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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+ | |style="padding:.4em;"|G Koh | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-48 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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|style="padding:.4em;"|SH Lee | |style="padding:.4em;"|SH Lee | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https:// | + | [https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1| | + | |style="padding:.4em;" rowspan=1|2023/11/08 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|23-42 |
+ | |style="padding:.4em;"|WJ Kim | ||
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+ | |style="padding:.4em;"|G Koh | ||
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|style="padding:.4em;"|SH Ahn | |style="padding:.4em;"|SH Ahn | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https:// | + | [https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1| | + | |style="padding:.4em;" rowspan=1|2023/10/11 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|23-37 |
+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | |style="padding:.4em;"|G Koh | ||
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|style="padding:.4em;"|HJ Kim | |style="padding:.4em;"|HJ Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https:// | + | [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] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1| | + | |style="padding:.4em;" rowspan=1|2023/09/06 |
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− | |style="padding:.4em;"|22-4 | + | |style="padding:.4em;"|23-28 |
+ | |style="padding:.4em;"|JH Cha | ||
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+ | |} | ||
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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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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|SY Lim | ||
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+ | |} | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | |style="padding:.4em;"|G Koh | ||
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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;"|IS Choi | ||
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+ | |style="padding:.4em;"|23-3 | ||
+ | |style="padding:.4em;"|G Koh | ||
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− | [https:// | + | [https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome] |
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-25 | ||
+ | |style="padding:.4em;"|SH Lee | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-24 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
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+ | |style="padding:.4em;"|23-23 | ||
+ | |style="padding:.4em;"|JY Kim | ||
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+ | |style="padding:.4em;" rowspan=1|2023/08/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/04 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-21 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/07/28 | ||
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− | [https:// | + | [https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system] |
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− | [https:// | + | [https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species] |
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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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− | [https://www.nature.com/articles/ | + | [https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities] |
+ | |- | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/26 | ||
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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;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;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|2023/02/21 | ||
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+ | |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;"|22-32 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/29 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|22-31 | ||
+ | |style="padding:.4em;"|JH Cha, SB Baek, IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions] | ||
+ | [https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference] | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells] | ||
+ | |- | ||
+ | |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"| | ||
+ | [https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/08 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|22-29 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/10/11 | ||
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-28 | |style="padding:.4em;"|22-28 | ||
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation] | [https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/09/13 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-25 | |style="padding:.4em;"|22-25 | ||
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[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies] | [https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/09/06 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-26 | |style="padding:.4em;"|22-26 | ||
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!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/ | + | |style="padding:.4em;" rowspan=1|2022/12/28 |
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-34 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-33 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/22 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-32 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/10/11 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-31 | |style="padding:.4em;"|22-31 | ||
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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] | [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] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/09/27 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-30 | |style="padding:.4em;"|22-30 | ||
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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] | [https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/09/13 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-29 | |style="padding:.4em;"|22-29 | ||
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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] | [https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/09/06 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-28 | |style="padding:.4em;"|22-28 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JY Ma |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.nature.com/articles/ | + | [https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2022/09/01 | |style="padding:.4em;" rowspan=1|2022/09/01 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-27 | |style="padding:.4em;"|22-27 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|NY Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://www.nature.com/articles/ | + | [https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2022/08/25 | |style="padding:.4em;" rowspan=1|2022/08/25 | ||
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|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation] | [https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation] | ||
+ | |} | ||
+ | |||
+ | |||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2022 Microbiome Special JC | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="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=1|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-15 | ||
+ | |style="padding:.4em;"|HY Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-14 | ||
+ | |style="padding:.4em;"|YJ Roh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-13 | ||
+ | |style="padding:.4em;"|SC Yang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-12 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-11 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-10 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-9 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-8 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-7 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-6 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-5 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-4 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-3 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-2 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-1 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics] | ||
|} | |} | ||
Latest revision as of 19:06, 24 September 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 |