Skip to content

Open Source Biology & Genetics Interest Group

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

  • About
  • XML/JSON
  • Filtering
  • Suggestions
  • Internships
  • Postdocs

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

Tabix position and alternative allele

 October 12, 2021

Tabix position and alternative allele

0

Hi everyone!
I need to extract the variants from a vcf file.
I’m using the tabix command but I have some position with multiple alternative alleles (splitted in my vcf), and I need to extract also the exact alternative allele.
How can I do that?
Thanks.


alternative


allele


tabix


variants

• 17 views

Read more here: Source link

Tagged VCF

Post navigation

Plot Blastn.table file on R →
← S. cerevisiae in snpEff databases

Tags

.bbs .bim .csv .evec .faa .fam .Gbk .gmt .NET Bio .PDBQT .tar.gz 23andMe A375 ABEs ABL-21058B ACADVL AccuraDX ACE2 aCGH ACLAME ACTB ACTREC addgene ADMIXTURE Adobe Audition adonis ADPribose Advantech AF AfterQC AGAT AI-sandbox Airbnb ajax AJOU Alaskapox ALCL ALDEx2 Alevin ALK ALOT AlphaDesign AlphaFold ALS AML AMOS AMP AMPHORA Ampure XP Amyloidosis Anaconda ancestryDNA ANCOM-BC ANGeS ANGPTL8 ANGSD anitaokoh annotateMyIDs AnnotationDbi annotationHub annovar ANSYS anti-aliasing anti-BCMA Anti-PD-1 antiSMASH Apache Kafka APC apex apollo ARACNE-AP ArcGIS Arduino arm64 ARRmNormalization Ascalaph Designer ascat ASM212806v1 ASM238634v1 ASM287662v1 ASM298219v1 ASM314399v1 ASM350094v1 ASM1227490v1 ASoC aspera ASVs ATAC ATAC-SEQ ATCC ATG ATM atpB-rbcL ATTR AutoDock AutoML AUTOR AVENGER Avogadro AZ Azure SQL BAC BALB BAM bamdst barplot Barracuda baseml bash BATF3 BattleMetrics BBDuk BBMap BBMerge BBTools BCFtools bcl2fastq BCR bdgcmp beagle BEAST BED bedGraph BEDtools bfiles BGCs bgen BGmix BiGG bigWig bin Bing Bio-DB-HTS BiocGenerics Bioclipse bioconda Bioconductor biohaskell BioImageDbs Bioinformatic Bioinformatics BioJava BioJS BioLabDonkey biomaRt BioMOBY BIONET BioPerl Biopython biorender BioRevolution BioRuby bioRxiv Biosample BioSpace BioViz biowasm birchhomedir Bismark Bitnami BL21 blaCTX-M-55 blaNDM-5 blaNDM-7 BLAST BOLT-LMM Bombyx bowtie2-align Brand BRCA Brunello bsc BSgenome BsmBI BUB1B BUBR1 Bug#991859 bulkRNAseq BUSCO BWA c++ C1QBP C57BL/6J cageminer CAIX-NFS1 CalculateContamination CalculateHsMetrics Car-T CareDx CAR T-cell CAS Cas7-11 cas9 Cas12a Cas13 CAS13A Cas13d CasMINI CASP14 CBEs CCA CcTop CD-HIT CDC CDKN2A cDNA CDS cdx centos CentOS 7 CentriMo cfDNA cGAS-STING cgMLST ChEBI chi2 ChimeraX ChIPComp ChIPpeakAnno ChIPseeker ChiPseq CHO CHR chRDNA ChromHMM Chromium chromoMap chrX CIBERSORTx Cinnamon CircRNA CIRI-full CITE-seq CITEseq ClinGen ClinVar CLIP CLISH cLOD CLUE ClustalO ClustalW cluster clusterProfiler clusters clustprofiler CMake CMT2E CNA cnetplot cnv Co-op COBRA codeml COG compareCluster ComplexHeatmap comp_chem Comsol conda config.txt ConsensusClusterPlus Control-FREEC CookHLA corr.test COSMIC CP033719 CP2K CPEC CPF CpG Cr2O3 cram cran Cre-driver CRIMS CRISPOR crispr CRISPRa CRISPRko CRLMM cRNA CROP-seq crRNA CRyPTIC CSI ctDNA cto Cubase Cutadapt CWL CXCR4 cygwin CYP2D6 Cytochrome CyTOF cytoscape CyVerse DADA2 DAPARS2 darkRP DAW dba.plotProfile DBHI DBMR dbNSFP dCas9 dCas9-vp64 dCas9-VPR ddATP DdCBE-mediated ddCTP ddGTP ddH2O ddTTP DE-analysis DE-kupl decay DECIPHER deepTools DEG degradome-seq DEL Demovir DESC DESE2 DESeq deseq2 DGE dgelist diagnostic DiffBind directx Diva Django DLBCL DMR DMRs DMSO DNA DNA-seq DNAseq dnasp dNdScv DoBISCUIT DOCK2 docker dplyr DRG DRIMseq DROP-seq DSB dsDNA dseq2 dsQTL dsRed dtu DuoClustering2018 EB eb-eye Eclipse IDE EcoR1 EDTA EdU eGFP-Puro EggNOG egsea EGT2 EHMT2 eisa electrotransfection ELN EMBL EMBL-EBI EMBOSS ENA EndeavourOS eNeuro enformer enhancedVolcano ENmix enrichGO enrichkegg enrichr ENSCAFG1 ensembl ENSEMBL ID ENSG entrezID EOF EpCAM EPN eQTL ER-lysosome ERA ers ERSSA ESBL-EC ESGCT eureKARE eureKAWARDS ex-psu exceRpt ExPASy fabfilter FACS FAIR FAP FAQs FAST5 FASTA fasterq-dump fastp fastq fastQC FASTY FBXW7 fcScan FDA fData featureSet feature_db FEBS fetchChromSize ffmpeg FGFR2 FGFR3 Ficoll file.ht2 fimo findMotifsGenome.pl Finite Element Method flagstat Flavivirus-GLUE flowStats flowTrans FoldGO FPKM fq.gz FreeBayes FreeBSD FRiP FSHD FunciSNP G2Net Gag/Pol GAM GAPDH gaschYHS GATK gBlock GBM GBR GBS GC GC-content GCA gcta GDAC gDNA GeCKO GEDmatch geecc GeForce GTX gemBS GEMMA GenBank genblastA Gene-Ontology GeneDX GeneGA geneID geneMANIA GenePattern GeneRfold GenGIS genome-wide genomeGenerate GenomeInfoDb genomelink genomeLoad Genomespace GenomeStudio genomic genomica GenomicsDBImport GENtle Gentoo GenXys GEO GEO2R GEOexplorer GEOquery GEO_OPT getEAWP getfasta getGEO Geworkbench GEX GFF gff3 gffread GFP ggalt GGBase GGG gGmbH ggplot ggplot2 GGtools ggtree GIAB GISTIC GISTIC2.0 github gitlab gja5 glmLRT global25 GM12891 GM12892 GMOD gnomAD gnomADc gnomix GNU GOChord GOI golubEsets GOPATH GOrilla 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 guideRNA GWAS GWASpower H3K9me3 H3K27ac H5AD H358 H1299 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 hipSYCL 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 HTTP API HuGene-1_0-st HVS HYBRID hypergravitropic Hyphy 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 JASPAR Java javascript jbrowse JETPEI jobRxiv journalctl Jquery json jsonpath JSON Schema JunctionSeq junk DNA Jupyter jupyterhub K-mer k-smoothing K562 kaggle KaryoploteR kegg Kernel KeyError Kibana kissplice KO Kraken2 KRAS KRASG12C kselftest KSpace kubectl kubernetes L2FC L4D2 label_format LABGeM LabKey Server Lambdas LAMMPS LAMP LaTeX LD LD50 LDDT LEfse LentiCRISPR LentiCRISPRv2 leukocyt libgromacs5 Liftoff ligand-interaction LIMMA LIMS LiP-MS LIU LLC lme4 LMGene lncRNA locus_tag LoF log2FoldChange logCPM logFC Logic LRRK2 lumiHumanIDMapping LwaCas13a Lynch LZMA m10kcod.db MACS macs2_bdgdiff MAFFT MAGeCK MAGs maizeprobe Makeblastdb MALT MAplot MAPQ MarkduplicatesSpark marr MARS-seq MashupMD matplotlib MBEDTLS MCF10A mcr-1 mCRC mCRPC MCScanX MD MDS MECP2 medulloblastoma mega-x Megahit melanoma MemVerge MergeSamFiles mESC mESCs MeSH.Pto.eg.db MetaCyc Metagenome MetaGxPancreas MetaPhlAn2 MetaProdigal metawrap METHPED MethylationEPIC methylclockData methylkit MetID MG-rast mg1655 MGEs MHC-I MIC-Drop MicroGenDX mig minimap2 MinION MINSEQE miQC MiRBase mirdeep2 miRNAseq MISeq mixcr mkref MLE MMBIR MMR mnp modelr MOFA MoGene-1_0-st-v1 MOI mol MongoDB monocle2 monocle3 monoclle3 montana Monterey mothur motIV Mpeg MPH mphil mpileup mps MQTT MRC MRD mRNA MRSA msa msc MSGFgui MSI MSigDB msk MSMC MTB mtDNA MTHFD1 multi-omic MULTI-seq MUMmer mummerplot MuTect MUTECT2 MXene MYC mydb MYO10 MySQL NA NAMESPACE NAR NBAMSeq nc1700sJORDAN ncbi NCC NCFamilyGenetic ncRNA NEB neofart NestJs netbenchmark NetCoMi NetMHCpan netprioR NewStem next-gen Next.js NextDenovo nextflow Nextseq Nexus nf-core NFDI4Microbiota NfL NGG nginx NGS NGS-Lib-Fwd NHEJ NHS NIH NLS Node.js NovaSeq NovelStem NPC NPSR1 nr NTLA nu Nucleosome NUMTS NuProbe NVIDIA odseq OMA omic OMIM OncoSimulR ONT oocyte openAPI OpenKIM OpenMP openSUSE OpenWrt orf OTU OVCAR3 p53 pA PacBio PACR pact_00210 PAIRADISE pairwise2 PARPis PASS PATCH v2 pathfindR PathVisio pax3 pbs pc016 PCA PCR pd.hg.focus PDAC pData pdb PDBE PDBep pdInfoBuilder PedPhase pegRNAs PER1 PERMANOVA perTargetCoverage Pfam pgdx PGGB 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 Pulseaudio PuTTY PWMEnrich 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 React JS 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 RISC-V rlogTransformation RMA RNA RNAmodR.Data RNAseP RNAseq rnaturalearth rnaturalearthdata roblox ROH RPKM rpoB RPPA RQN RRBS RRHO2 rRNA RSEM RSeQC rsIDs RSQLite Rstudio Rsubread RT-qPCR RTMP rtracklayer rTRMui Ruby RUnit RUNX2 rust-bio S4Vectors SageMath sagenome SAIGE Salmon SAM sambamba samblaster SAMD9 sample samtool SAMtools SBS SBT ScarHRD scATAC-SEQ SCF SCID ScienceDaily SCIRP SCO-012 scRNA scRNAseq scVelo SCVI SED seer-medicare segemehl selenium seq-lang Seqio.Parse SeqIO.pm SERS Serum SEs Seurat sEVs SFS sgRNA Shapeit shell Shiny shRNA sift SILVA Single.mTEC.Transcriptomes SingleR SingscoreAMLMutations sjdbOverhang SKAT-O SKCM SLURM SMAD4 smallRNA smallRNAseq SMAP smartPCA SmartSeq SMRT SNAKEMAKE snap snoRNP snp SnpEff SNPRelate snpsift snRNA-seq snsplot SNV SOAP SOAP Suite SOS SPAdes SpCas9 Spike-Ins SPSS SQL SQLite SRA sra-toolkit SRC SRF SRP SRR ssDNA SSL ssODNs ssRNA Stackdriver Stackify Staden Package STAR stats4 Steam stem STF STP StrainPhlAn stref_00240 StringTie SunTag sv SV40 SwissProt Synapse Syntekabio Synths systemPipeRdata T7RNAP TAIR tblastx TBSignatureProfiler TCC tcga tcga-brca TensorRT testthat TET2 textfield TFAM TFBSTools TFDP2 TIDE TIGRFAMs TK8912 TME TMM-normalization TnpB tnpR TOM topGO tophat2 TP53 TPM tracrRNA traefik TRAIT TransposonPSI treat_pileup trimAL tRNA tRNAdbImport tRNAleu-cox2 TruSeq tSNE TSS Ttc30a ttgsea typescript Ublast Ubuntu UCD UCSC UCSF UGENE UH UMAP UMItools Unipept UniProtKB UniRef90 unity3d uORF useMart usr USU UTP utr ValueWalk VanillaICE Varscan VCF VCF.gz VCFtools vcfutils VDB VEP Vertex VG viennaRNA ViPR VirtualBox VMD vodosp.ru VOTCA VP VP64 VQSR VSCode VTx WASP Waveform WDNA WebRTC WebSockets WES WGBS WGCNA WGS whg wilcoxon WMD WNN WT1 WW2 WXS xCT XDR xenografts Xfce xFire XGBoost xGEN xpehh Xpert XRCC1 XS Y-DNA Yandex Yogscast YPR193C z-score Zenodo zsh ZyCoV-D zygotes Zymo ~jobs

Categories

  • Business (2,081)
    • Abbott (44)
    • AbbVie (43)
    • Amgen (49)
    • Bayer (60)
    • Boehringer Ingelheim (17)
    • Gilead (37)
    • GlaxoSmithKline (17)
    • Johnson & Johnson (2)
    • Merck & Co. (3)
    • Novartis (87)
    • Pfizer (147)
    • Roche (219)
    • Sanofi (50)
    • Takeda (41)
    • Teva (12)
  • Hiring (4,814)
    • Asia-Pacific (1,651)
    • Europe (1,772)
    • North America (1,670)
    • Russia (214)
  • Research (25,685)

Videos

CRISPR Web Analyser Göknur Giner (WEHI, Australia) 4:30 PM - 4:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK CRISPR/Cas-based genome editing systems have revolutionised the field of genome engineering with applications in a plethora of research fields, including medicine, biology, and biotechnology in only a few years. Here we present a newly developed CRISPR web analyser that offers a few modules to understand the count data obtained from next-generation sequencing (NGS) technology of a pooled CRISPR experiment. These modules include Data Quality Check, Data Preprocessing, and Fitting a Statistical Model. Moderator: Peter Hickey (WEHI, Australia)
CRISPR Web Analyser
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples Ramyar Molania (WEHI, Australia) 4:15 PM - 4:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can signifi- cantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms. Moderator: Peter Hickey (WEHI, Australia)
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples
Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver Ignatius Pang (Childrens Medical Research Institute, Australia) 4:00 PM - 4:15 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Identifying phosphorylation sites and how they change in abundance under different environmental conditions are important for elucidating the role of signal regulations in cellular processes. ProteomeRiver is a novel pipeline that facilitates the analysis of differential abundance of proteins and their phosphorylation events. The pipeline enables the batch analysis of many pairwise treatment versus control comparisons and subsequent pathways overrepresentation analysis. To enable differential abundance analysis of mono- and multi-phosphorylation events, ProteomeRiver incorporates missing values imputation (PhosR*, Kim et al. Cell Rep., 34(8), 108771), removal of unwanted variation (ruv*, Molania et al. 2019 Nucleic Acids Res. 47:6073 - 6083), linear models (limma*, Ritchie et al. 2015 Nucleic Acids Res. 43(7), e47), kinase-substrate enrichment (KinSwingR*), and pathways analysis (clusterProfiler*, Wu et al. The Innovation, 2(3), 100141). This pipeline uses modular components, which allows the modules to be substituted and/or extended with novel tools as they become available. This pipeline also uses a small set of configuration files and scripts to store all instructions necessary for data analysis, which could be shared publicly on code repositories to support reproducibility. The impact of applying remove unwanted variation and normalising the changes in abundance of phosphorylation events by the changes in host protein abundance was demonstrated through the re-analysis of a published dataset of synapses proteome and phosphoproteome during homeostatic up- and down-scaling (Desch et al. 2021 Cell Rep. 36:109583). Compared with the results from the original publication, the use of ProteomeRiver resulted in the identification of novel pathways and upstream kinases associated with homeostatic up- and down-scaling. The pipeline is currently under development and will be available as a R package via https://bitbucket.org/cmri-bioinformatics/proteomeriver. (*) denotes R or Bioconductor packages. Moderator: Peter Hickey (WEHI, Australia)
Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver
Notes: The audio for the first 6 minutes of this talk was not recorded due to an error with the hybrid meeting. Extension of scPipe Bioconductor Package for scATAC-seq Data Shanika Amarasinghe (ARMI/WEHI, Australia) 3:30 PM - 3:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enables genome-wide profiling of chromatin accessibility that can be used to improve gene expression modelling at single-cell resolution. scATAC-seq data are intrinsically sparse, since the signals detected at a genomic position are limited by DNA copy number, thereby making it difficult to discriminate signals from noise. Therefore, thorough and clear pre-processing steps are needed prior to downstream analyses. Furthermore, these pre-processing steps need to be sufficiently adaptable to user requirements. Currently available scATAC-seq pre-processing tools are either not fully R-based or have limited flexibility. We have extended scPipe, our current pre-processing tool for single-cell RNA data, to be able to process scATAC-seq data from a variety of different protocols. We take the power and flexibility of the R environment and create a workflow that combines multiple Bioconductor packages to achieve this. We also evaluated the performance of scPipe’s scATAC-Seq module on an in-house benchmarking dataset to assess its speed and ability to recover known population structure. Moderator: Peter Hickey (WEHI, Australia)
Extension of scPipe Bioconductor Package for scATAC-seq Data
Matilda for Single-cell Multi-omics Data Integration Chunlei Liu (Childrens Medical Research Institute, Australia) 2:55 PM - 3:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 PACKAGE DEMO Single-cell multimodal omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of single-cell multimodal omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of single-cell multimodal omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular single-cell multimodal omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative single-cell multimodal omics data analysis. Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
Matilda for Single-cell Multi-omics Data Integration
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data Melody Jin (WEHI, Australia) 2:15 PM - 2:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Single cell RNA-seq is a revolutionary technology that has enabled the discovery of novel cell types and has facilitated deeper insight into normal tissue development. Single cell data is highly noisy, and extensive research focuses on quality control strategies. We develop a package cellXY (https://github.com/phipsonlab/cellXY) with pre-trained machine learning models to qualify the data from the cell sex perspective. We define super genes (superX and superY) to capture the expression pattern of male and female cells. Several models are tuned to predict the sex label for human and mouse cells, including logistic regression, support vector machines, random forest and gradient boosting machine models. The performance on a pooled human PBMC dataset with available ground truth reaches an overall sensitivity of 0.9016 and a precision of 0.9053. Besides, we test the developed model on a public mouse heart dataset without true labels, and successfully predict 37% more cells compared to a simple thresholding method originally adopted by the researchers. Additionally, we rely on sex-related features to train models identifying Male/Female doublets with specially generated doublet-singlet training sets. We achieve optimal performances in the training phase, and the identified Male/Female doublets in public datasets exhibit clear evidence based on feature distributions. The package is publically available and can contribute to the quality control process in single-cell data analysis. It can be used in conjunction with the demultiplexing techniques to assist the cell-to-sample allocation. Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data Taylor Tian (BGI, Australia) 2:45 PM - 2:55 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 PACKAGE DEMO Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data
Spectre Toolkit for Rapid Analysis of Cytometry Data Givanna Putri (WEHI, Australia) 2:00 PM - 2:15 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Cytometry is a technology which simultaneously, rapidly, and economically quantifies the expression of up to 50 proteins of millions of cells at single-cell resolution. It is routinely used in studies to investigate immune system, infectious diseases, and cancer. While cytometry and scRNAseq share few common data analysis steps, computational approaches specific for cytometry data are not as well established as those for scRNAseq data, and that it is non-trivial for scRNAseq tools to process cytometry data due to the sheer volume of the data on hand (millions vs thousands of cells) requiring excessive processing time and computing resources. Importantly, it is often difficult to integrate existing computational tools in an analysis workflow, as they frequently operate on custom complex data formats. Existing toolkits are inflexible in that they also use custom overly complicated data structures and often mandate a strict order of analysis operations, some of which are not required for cytometry data. To address this, we developed Spectre, a flexible R toolkit for rapid analysis of cytometry data. Spectre facilitates the creation of a highly modular analysis workflow by employing data.table (an extension of R’s native data.frame format) for its data management and operations, and by implementing wrapper functions for select essential computational tools to perform data pre-processing, batch alignment, clustering, dimensionality reduction, plotting, and simple statistical analyses, such that they can be easily chained together in any given order in a workflow. Further, Spectre also facilitates cell type annotation label transfer across datasets using classical machine learning classification algorithms. Since its inception, Spectre has been used in various biological studies, including one which studied the immune response to COVID-19 infection. Here, we illustrate the use of Spectre by analysing cytometry data elucidating the immune response to the West Nile virus infection in mice. In summary, Spectre facilitates the analysis of complex high-dimensional cytometry data by enabling seamless integration of computational methods through strategic use of data.table and wrapper functions. Further work is currently underway to develop the infrastructure and wrapper functions to analyse Imaging Mass Cytometry data and to integrate cytometry with CITEseq or AbSeq data. Spectre is freely available on GitHub https://github.com/immunedynamics/spectre. Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Spectre Toolkit for Rapid Analysis of Cytometry Data
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis Ellis Patrick (The University of Sydney, Australia) 1:45 PM - 2:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 LONG TALK Understanding the interplay between different types of cells and their immediate environment is critical for understanding the mechanisms of cells themselves and their function in the context of human diseases. Recent advances in high dimensional in situ cytometry technologies have fundamentally revolutionized our ability to observe these complex cellular relationships providing an unprecedented characterisation of cellular heterogeneity in a tissue environment. We have developed an analytical framework for analysing data from high dimensional in situ cytometry assays including CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. Implemented in R, this framework makes use of functionality from our Bioconductor packages spicyR, lisaClust, scFeatures, treekoR, FuseSOM, simpleSeg and ClassifyR. We will provide an overview of key steps which are needed to interrogate the comprehensive spatial information generated by these exciting new technologies including cell segmentation, feature normalisation, cell type identification, microenvironment characterisation, spatial hypothesis testing and patient classification. Ultimately, our modular analysis framework provides a cohesive and accessible entry point into spatially resolved single cell data analysis for any R-based bioinformaticians. Moderator: Anna Quaglieri (Mass Dynamics, Australia)
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis
Load More... Subscribe

Copyright © 2021 IRZU Inštitut za raziskovanje zvočnih umetnosti

Cleantalk Pixel
Join us at CRISPR workshops in Koper, Slovenia in 2023. Erasmus+ funds available!
Learn more
This is default text for notification bar
Learn more