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Open Source Biology & Genetics Interest Group

Open source scripts, reports, and preprints for in vitro biology, genetics, bioinformatics, crispr, and other biotech applications.

 Posted in Research

3 Questions on… Picking the Right Tumor Cell Models for Research

 August 11, 2021

Technologies that have enabled this include tumoroids, and genome engineering techniques, such as CRISPR/Cas9. Patient-derived xenografts …

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Tagged cas9, crispr, xenografts

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Error when trying to import GTF files using rtracklayer’s import function →
← is local ancestry inference typically always run w/ array genotypes instead of imputed genotypes?

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GOSemSim GOseq GPL Grain GraphQL GRCH37 GRCH38 GRCm38 GRCm39 grenits GREP GRM gRNA GROMACS grompp GRRDUser GSA GSE66099 GSE172016 GSEA gseGO gsekegg GSM-SAMPLES GSTAr,GSTA Gstreamer gsva GTA gtex gtf GtRNAdb GUI GUIDANCE GWAS GWASpower H3K9me3 H3K27ac H5AD H2122 haplogroup s HaplotypeCaller haplotypes HCC HDCytoData HDMI HDR HDR-CRISPR heatmaps HEK293 HEK293T HeLa HepG2 hg19 hg38 HGNC HGT HGU133Plus2 HiC-Pro HiFiBiO HilbertVis hiPSCs hisat2 hiSeq HiTC HitTable HMM hmmer Hmox1 HNC homeoboxB9 HOMER HPC HPC-reditools HPLC HPV HR HRS hs37d5 HT-Seq HTqPCR HTseq HTSeq-count HTSlib HuGene-1_0-st HVS HYBRID hypergravitropic I-TASSER IC50 ICA ICAR ICheckpointHelperClient iCOMIC IDbyDNA IDR IEEE IgG IGM ignoreTxVersion IGV IJMS IL-2R ILMN ImdSession Impl IMPUTE2 IncRNA indel InferCNV iNMF install.packages IntegraGen Integrated Genome Browser InterMine InterPro InterProScan intronsByTranscript IOError ION torrent IP-MS ipdsummary iPSC iPSCs IRI ischemia-reperfusion ISD iseq iso-seq iTOL IVT 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PGS phase_trio.sh phastConsElements phd pheatmap phenix PhiX Phoenix PHRED phyloseq Phylum PI3 picard PICS PIK3CA PIM Pindel platypus pLentiCRISPRv2 plink plotMA PLS-DA POLG1 polyphen2 PopGen PopGenome popmax PostgreSQL PowerBI PPI ppinfer PrCa pre-IPO PredicineCARE primer3 PrognomiQ Promethease PROPER-seq ProteoDisco provean PRS PRSice PRSice-2 pseudotime psgendb psiblast PSMC Psme1 pSpCas9 psyt PTM PubMed pUC19 pULA105E PuTTY px459 Pybedtools PyCharm pyHAM PYMC3 pymol pymolrc python PythonRepo pytorch QA QC qctool v2 Qiagen QIAseq QIIME2 qPCR qqplot QUAL qualimap quant.sf Quanta Quantseq query R r-bioc-basilisk r-bioc-deseq2 rabbitmq RACK1 RAD-seq ragdolls ramr ranzcr RASMOL raw_counts RBPs RCB RCorrector RDAVIDWebService RDocumentation reactjs reactome.db ReactomePA ReadAffy readDGE readDNAStringSet recount2 recovery REditools2 RefSeq regex RegExp relatedness resonance REVIGO RFLP RFM RFP RFS RfxCas13d RGBA rgsepd RIMS-seq RINalyzer rip-md RIPSeekerData rlogTransformation RMA RNA 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Videos

European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany (talks only) https://eurobioc2022.bioconductor.org/schedule/ 09:00 Keynote Dr. Björn Grüning: Developing interoperable frameworks for the scientific community 09:45 Flashtalks 10:30 Coffee break 11:00 Special session Birds-of-a-Feather, Three parallel sessions 12:00 Lunch 13:00 Special session Birds-of-a-Feather, Three parallel sessions 14:30 Coffee break 14:50 End of Day 3 Closing remarks
EuroBioC2022 Live Stream Day 3: morning
European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany (talks only) https://eurobioc2022.bioconductor.org/schedule/ 16:20 Short talk Short talk 19: DifferentialRegulation: a novel approach to identify differentially regulated genes. Simone Tiberi 16:30 Short talk Short talk 20: Analysis of transposable elements in R and Bioconductor with atena. Beatriz Calvo-Serra 16:40 Short talk Short talk 21: Inclusive instruction in cancer data science: Authoring and deployment with yes4cure. Alexandru Mahmoud
EuroBioC2022 Live Stream Day 2: afternoon
European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany (talks only) https://eurobioc2022.bioconductor.org/schedule/ 14:35 Keynote Dr. Michael Dorrity: Developmental robustness of zebrafish embryos at single-cell resolution
EuroBioC2022 Live Stream Day 2: noon
European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany (talks only) https://eurobioc2022.bioconductor.org/schedule/ 09:15 Keynote Dr. Judith Zaugg 10:00 Coffee break 10:10 Short talk Short talk 8: Smooth epigenomics data analysis and visualisation with Bioconductor (or: an ode to Rle). Pierre-Luc Germain 10:20 Short talk Short talk 9: MethQuant: A package providing entropy-based measures for quantifying patterns of DNA methylation heterogeneity in (single-cell) bisulfite sequencing data. Emanuel Sonder 10:30 Short talk Short talk 10: DeeDee - Evaluating, Visualizing, and Integrating Results from Differential Expression Analysis Workflows. Federico Marini 10:40 Short talk Short talk 11: Analysing multiplexed assays of variant effects with mutscan. Michael Stadler 10:50 Short talk Short talk 12: Joint modeling of rare variant genetic effects using deep learning and data-driven burden scores. Brian Clarke 11:00 Coffee break 11:30 Short talk Short talk 13: spillR: Causal Modelling of Spillover in Mass Cytometry. Marco Guazzini 11:40 Short talk Short talk 14: Multi-omic integration with cosmosR and ocEAn to study cross-talks between signalling and metabolism in diseases. Aurelien Dugourd 11:50 Short talk Short talk 15: A new principled approach for single-cell proteomics data analysis. Laurent Gatto 12:00 Short talk Short talk 16: Analyzing hydrogen-deuterium exchange mass-spectrometry data in R. Oliver Crook 12:10 Short talk Short talk 17: CTexploreR, a package to explore CT genes. Axelle Loriot
EuroBioC2022 Live Stream Day 2: morning
European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany (talks only) https://eurobioc2022.bioconductor.org/schedule/ 14:10 Keynote Dr. Julio Saez-Rodriguez: Combining multi-omics data and biological knowledge to extract disease mechanisms
EuroBioC2022 Live Stream Day 1: afternoon
European Bioconductor Conference 2022 Live Stream from Heidelberg, Germany QUESTIONS: www.slido.com #1680070 (talks only) https://eurobioc2022.bioconductor.org/schedule/ 09:15 Introduction Welcome, Bioconductor overview, and Code of Conduct 09:40 Keynote Dr. Shila Ghazanfar: Integrative analyses for spatial genomics data 10:25 Break 10:30 Short talk Short talk 1: Co-clustering of Spatially Resolved Transcriptomic Data. Andrea Sottosanti 10:40 Short talk Short talk 2: Simplified, strand aware and comprehensive splicing analysis using IntEREst (1.22.0). Ali Oghabian 10:50 Short talk Short talk 3: Differential embedding analysis of multi-condition single-cell datasets. Constantin Ahlmann-Eltze 11:00 Coffee break 11:30 Short talk Short talk 4: Clinical microbiome data science with MultiAssayExperiment. Tuomas Borman 11:40 Short talk Short talk 5: µSTASIS - Assessment of human microbiota stability across longitudinal samples. Pedro Sánchez-Sánchez 11:50 Short talk Short talk 6: OmicSHIELD: privacy-protected federated omic data analysis in multi-center studies with Bioconductor through DataSHIELD. Juan Ramon Gonzalez 12:00 Short talk Short talk 7: DepInfeR - A Bioconductor package for Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling. Junyan Lu
EuroBioC2022 Live Stream Day 1: morning
Visualization and analysis of highly multiplexed imaging data - Nils Eling
Visualization Analysis IMC Data - March 28, 2022
Spatial Transcriptomics Technologies and Analysis Tools - Dario Righelli Analysing Spatially Resolved Transcriptomics Data with Bioconductor - Helena L. Crowell
Spatial Transcriptomics - January 31, 2022
Rにおけるバイオ画像解析入門: Introduction to Bioimaging Analysis in R 久米 慧嗣 Satoshi Kume (大阪電気通信大学 Osaka Electro-Communication University, 日本 Japan) 4:00 PM - 5:00 PM JST (Japan Standard Time) on Thursday, Nov. 4th, 2021 JAPANESE WORKSHOP このワークショップでは、BioconductorパッケージであるEBImageを使って、Rでの画像処理・画像解析の基本的な方法を扱う。次に、BioImageDbsパッケージを用いて、ExperimentHubからの画像データの取得を行う。さらに、rMiWパッケージが提供する、Deep learningモデルの１つであるU-NETモデルを用いて、教師有り画像セグメンテーション（領域分割）を学び。このワークショップは、Orchestra環境にて実施する。 This workshop covers basic methods of the image processing and image analysis in R using the Bioconductor package “EBImage” and the Orchestra platform. In addition, the image dataset is obtained from ExperimentHub using the “BioImageDbs” package. Using this dataset, we perform a supervised image segmentation using the U-NET model, one of deep learning models, provided by the rMiW package. Moderator: 露崎 弘毅 Koki Tsuyuzaki (理化学研究所 RIKEN, 日本 Japan)
Rにおけるバイオ画像解析入門: Introduction to Bioimaging Analysis in R
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