Difference between revisions of "Journal Club"
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment] | [https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment] | ||
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment] | [https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment] | ||
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy] | [https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy] | ||
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells] | [https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells] | ||
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | [https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | ||
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment] | [https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment] | ||
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases] | [https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases] | ||
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | [https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
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[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy] | [https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy] | ||
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | [https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | ||
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients] | [https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients] | ||
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | [https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | ||
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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] | ||
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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] | ||
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!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
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− | |style="padding:.4em;" rowspan=1|2022/09/ | + | |style="padding:.4em;" rowspan=1|2022/11/15 |
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-39 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/08 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-38 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/01 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-37 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/11/01 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-36 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/10/25 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-35 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/10/18 | ||
+ | |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|2022/10/11 | ||
+ | |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|2022/10/04 | ||
+ | |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|2022/09/27 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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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] | ||
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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] | ||
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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] | ||
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− | |style="padding:.4em;"|22- | + | |style="padding:.4em;"|22-28 |
|style="padding:.4em;"|JY Ma | |style="padding:.4em;"|JY Ma | ||
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|style="padding:.4em;" rowspan=1|2022/09/01 | |style="padding:.4em;" rowspan=1|2022/09/01 | ||
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− | |style="padding:.4em;"|22- | + | |style="padding:.4em;"|22-27 |
|style="padding:.4em;"|NY Kim | |style="padding:.4em;"|NY Kim | ||
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|} | |} | ||
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+ | {|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] | ||
+ | |} | ||
Revision as of 11:05, 7 September 2022
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 |