Difference between revisions of "Journal Club"
From Bioinformatics Lab
Dreadcupper (Talk | contribs) |
|||
(27 intermediate revisions by 5 users not shown) | |||
Line 1: | Line 1: | ||
+ | {|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 81: | ||
!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2017/02/21 | + | |style="padding:.4em;" rowspan=2|2017/06/28 |
− | |style="padding:.4em;"|2017- | + | |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;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"| | ||
+ | [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;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|17-18 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|17-16 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|17-14 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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;"|17-12 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-11 | ||
|style="padding:.4em;"|JE Shim | |style="padding:.4em;"|JE Shim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.] |
|- | |- | ||
− | |style="padding:.4em;" rowspan= | + | |style="padding:.4em;" rowspan=2|2017/02/21 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-10 |
|style="padding:.4em;"|EB Kim | |style="padding:.4em;"|EB Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.] |
|- | |- | ||
− | |style="padding:.4em;" rowspan= | + | |style="padding:.4em;"|17-09 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|EB Kim |
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/02/07 | ||
+ | |style="padding:.4em;"|17-08 | ||
|style="padding:.4em;"|DS Bae | |style="padding:.4em;"|DS Bae | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.] | [http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.] | ||
|- | |- | ||
− | |style="padding:.4em;" | + | |style="padding:.4em;"|17-07 |
− | + | ||
|style="padding:.4em;"|MY Lee | |style="padding:.4em;"|MY Lee | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.] | [http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2017/01/ | + | |style="padding:.4em;" rowspan=1|2017/01/31 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-06 |
|style="padding:.4em;"|CY Kim | |style="padding:.4em;"|CY Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2017/01/ | + | |style="padding:.4em;" rowspan=1|2017/01/24 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-05 |
|style="padding:.4em;"|JW Cho | |style="padding:.4em;"|JW Cho | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.] |
|- | |- | ||
|style="padding:.4em;" rowspan=2|2017/01/17 | |style="padding:.4em;" rowspan=2|2017/01/17 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-04 |
|style="padding:.4em;"|HJ Han | |style="padding:.4em;"|HJ Han | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C] |
|- | |- | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-03 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|EB Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2017/01/10 | |style="padding:.4em;" rowspan=1|2017/01/10 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-02 |
|style="padding:.4em;"|JE Shim | |style="padding:.4em;"|JE Shim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2017/01/03 | |style="padding:.4em;" rowspan=1|2017/01/03 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|17-01 |
|style="padding:.4em;"|SH Lee | |style="padding:.4em;"|SH Lee | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types] |
|- | |- | ||
|} | |} |
Revision as of 15:14, 7 May 2018
2012
2011
2010