Skip to content

Open Source Biology & Genetics Interest Group

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

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

Open Source Biology & Genetics Interest Group

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

 Posted in Research

How to retrieve KO IDs for a list of genes?

 August 12, 2021

How to retrieve KO IDs for a list of genes?

2

Hi, community!!! I have downloaded a list of genes from the MetaCyc database for some bacterial species. I want to find out their respective KO IDs. Can anyone please tell me how can I do that?

Thanks


KEGG


database

• 27 views

Source link

Tagged kegg, KO, MetaCyc

Post navigation

how to use ESTIMATE to infer tumor purity and stromal score from RNA-seq data? →
← Chipseq visualization how to draw the figure

Categories

  • Abbott (1)
  • AbbVie (1)
  • Bayer (1)
  • Boehringer Ingelheim (1)
  • Business (63)
    • Amgen (1)
    • Roche (7)
    • Takeda (2)
  • Hiring (820)
    • Asia-Pacific (37)
    • Europe (43)
    • North America (36)
  • Merck & Co. (1)
  • Novartis (1)
  • Pfizer (1)
  • Research (15,004)
  • Russia (4)
  • Sanofi (1)

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 ADPribose AF AfterQC AGAT AI-sandbox ajax AJOU Alaskapox ALCL ALDEx2 Alevin ALK ALOT 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 APC apex apollo ARACNE-AP ArcGIS Arduino arm64 ARRmNormalization Ascalaph Designer ASM212806v1 ASM238634v1 ASM287662v1 ASM298219v1 ASM314399v1 ASM350094v1 ASM1227490v1 ASoC aspera ASVs ATAC ATAC-SEQ ATCC ATG ATM atpB-rbcL ATTR AutoDock AutoML Avogadro AZ BAC BALB BAM bamdst barplot baseml bash BATF3 BattleMetrics BBDuk BBMap BBMerge BBTools BCFtools bcl2fastq BCR bdgcmp beagle BEAST BED bedGraph BEDtools bfiles BGCs bgen 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 bsc BSgenome 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 CD-HIT CDC CDKN2A cDNA CDS cdx centos CentOS 7 CentriMo cfDNA cGAS-STING cgMLST ChEBI chi2 ChimeraX ChIPComp ChIPseeker ChiPseq CHO CHR chRDNA ChromHMM Chromium chromoMap chrX CIBERSORTx CircRNA CIRI-full CITE-seq CITEseq ClinVar CLIP cLOD CLUE ClustalO ClustalW cluster clusterProfiler clusters clustprofiler CMake CMT2E CNA cnetplot cnv Co-op COBRA codeml COG compareCluster ComplexHeatmap comp_chem conda config.txt ConsensusClusterPlus Control-FREEC CookHLA corr.test COSMIC CP033719 CP2K CpG Cr2O3 cram cran CRIMS CRISPOR crispr CRISPRa CRISPRko CRLMM CROP-seq crRNA CRyPTIC CSI ctDNA cto Cutadapt CWL CXCR4 cygwin CYP2D6 Cytochrome CyTOF cytoscape CyVerse DADA2 DAPARS2 darkRP 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 DoBISCUIT DOCK2 docker dplyr DRG DRIMseq DROP-seq DSB dsDNA dseq2 dsQTL dsRed dtu DuoClustering2018 EB Eclipse IDE EcoR1 EDTA EdU EggNOG egsea EGT2 eisa electrotransfection ELN EMBL EMBL-EBI EMBOSS ENA eNeuro enformer enhancedVolcano ENmix enrichGO enrichkegg enrichr ENSCAFG1 ensembl ENSEMBL ID ENSG entrezID EOF EpCAM EPN eQTL ERA ers ERSSA ESBL-EC eureKARE eureKAWARDS ex-psu exceRpt ExPASy FACS FAIR FAQs FAST5 FASTA fasterq-dump fastp fastq fastQC FASTY FBXW7 fcScan FDA fData featureSet feature_db FEBS fetchChromSize 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 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 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 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 GWAS GWASpower H3K9me3 H3K27ac H5AD H2122 haplogroup s HaplotypeCaller haplotypes HCC HDCytoData HDMI HDR heatmaps HEK293 HEK293T HeLa HepG2 hg19 hg38 HGNC HGT HGU133Plus2 HiFiBiO HilbertVis hiPSCs hisat2 hiSeq HiTC HitTable HMM hmmer Hmox1 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 indel InferCNV iNMF install.packages Integrated Genome Browser InterMine InterPro InterProScan intronsByTranscript IOError ION torrent IP-MS ipdsummary iPSC iPSCs ischemia-reperfusion ISD iseq iso-seq iTOL JASPAR Java javascript jbrowse jobRxiv Jquery json JSON Schema JunctionSeq junk DNA Jupyter jupyterhub K-mer k-smoothing K562 kaggle KaryoploteR kegg Kernel KeyError kissplice KO Kraken2 KRAS KRASG12C KSpace kubernetes L2FC L4D2 LABGeM LabKey Server LAMMPS LAMP LaTeX LD LDDT LentiCRISPR LentiCRISPRv2 leukocyt libgromacs5 Liftoff ligand-interaction LIMMA LIMS LiP-MS LIU LLC lme4 LMGene lncRNA locus_tag LoF log2FoldChange logCPM logFC Logic LwaCas13a Lynch LZMA m10kcod.db MACS macs2_bdgdiff MAFFT MAGeCK MAGs maizeprobe Makeblastdb 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 netbenchmark NetCoMi netprioR NewStem next-gen 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 ONT oocyte openAPI OpenKIM OpenMP OpenWrt orf OTU OVCAR3 p53 pA PacBio PACR pact_00210 PAIRADISE pairwise2 PARPis PASS PATCH v2 pathfindR PathVisio pax3 pbs PCA PCR pd.hg.focus PDAC pData pdb PDBE pdInfoBuilder PedPhase pegRNAs PERMANOVA perTargetCoverage Pfam pgdx PGGB PGS phase_trio.sh phastConsElements phd pheatmap phenix Phoenix PHRED phyloseq Phylum PI3 picard PICS PIK3CA PIM Pindel platypus plink plotMA POLG1 polyphen2 PopGenome popmax PostgreSQL PowerBI PPI ppinfer pre-IPO PredicineCARE primer3 PrognomiQ Promethease PROPER-seq ProteoDisco provean PRS PRSice PRSice-2 pseudotime psgendb psiblast PSMC 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 Quantseq query R r-bioc-basilisk r-bioc-deseq2 rabbitmq RAD-seq ragdolls ramr ranzcr raw_counts RBPs RCorrector RDAVIDWebService RDocumentation reactjs reactome.db ReactomePA ReadAffy readDGE readDNAStringSet recount2 recovery REditools2 RefSeq regex RegExp relatedness resonance RFLP RFP RfxCas13d RGBA rgsepd RIMS-seq RINalyzer rip-md RIPSeekerData rlogTransformation RMA RNA RNAmodR.Data RNAseq rnaturalearth rnaturalearthdata roblox ROH RPKM rpoB 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 segemehl selenium seq-lang Seqio.Parse SeqIO.pm Serum SEs Seurat 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 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 ssRNA Stackify Staden Package STAR stats4 Steam stem STP stref_00240 StringTie sv SV40 SwissProt Synapse Syntekabio systemPipeRdata T7RNAP TAIR tblastx TBSignatureProfiler TCC tcga tcga-brca TensorRT testthat textfield TFAM TFBSTools TFDP2 TIDE TIGRFAMs TK8912 TME TMM-normalization TnpB tnpR TOM topGO tophat2 TP53 TPM tracrRNA 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 useMart usr USU UTP utr VanillaICE Varscan VCF VCF.gz VCFtools vcfutils VDB VEP VG viennaRNA ViPR VirtualBox VMD vodosp.ru VOTCA VP VP64 VQSR VTx WASP WDNA WebSockets WES WGBS WGCNA WGS wilcoxon WMD WNN WT1 WW2 WXS xenografts xFire XGBoost xpehh XRCC1 XS Y-DNA Yandex Yogscast YPR193C z-score Zenodo zsh ZyCoV-D zygotes Zymo ~jobs

Videos

Visualization and analysis of highly multiplexed imaging data - Nils Eling
Visualization Analysis IMC Data - March 28, 2022
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLjhwZ2JxNWJRaHh3
Spatial Transcriptomics Technologies and Analysis Tools - Dario Righelli Analysing Spatially Resolved Transcriptomics Data with Bioconductor - Helena L. Crowell
Spatial Transcriptomics - January 31, 2022
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLmctUldCSHFLajZN
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
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLlhXamZHelhsRTVr
BioC Asia 2021 Opening Remark Koki Tsuyuzaki (RIKEN, Japan) 1:00 PM - 1:10 PM JST (Japan Standard Time) on Monday, Nov. 1st, 2021 OPENING REMARK
BioC Asia 2021 Opening Remark
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLnE0NHYwR0lFVW53
Dimension Reduction for Beginners: Hitchhiker’s Guide to Matrix Factorization and PCA Aedin Culhane (University of Limerick, Ireland) 4:00 PM - 5:00 PM JST (Japan Standard Time) on Tuesday, Nov. 2nd, 2021 ENGLISH WORKSHOP This workshop will provide a beginner’s guide to matrix factorization, principal component analysis (PCA), the difference between singular value decomposition, different forms of PCA and fast PCA for single-cell data as well as correspondence analysis and decomposition of the Pearson Residuals. We will describe how to detect artifacts and select the optimal number of components. It will focus on SVD, PCA, COA applied toy datasets and single-cell data. Principal component analysis (PCA) is a key step in many bioinformatics pipelines. In this interactive session we will take a deep dive into the various implementations of singular value decomposition (SVD) and principal component analysis (PCA) to clarify the relationship between these methods, and to demonstrate the equivalencies and contrasts between these methods. We will describe correspondence analysis (COA) and demonstrate how it differs from PCA. We will also discuss interpretation of outputs, as well as some common pitfalls and sources of confusion in utilizing these methods. Moderator: Peter Hickey (Walter and Eliza Hall Institute of Medical Research)
Dimension Reduction for Beginners: Hitchhiker's Guide to Matrix Factorization and PCA
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLlAwWGx4MXR4SmFz
Single-cell proteomics data analysis using QFeatures and scp Christophe Vanderaa (UCLouvain, Belgium) Laurent Gatto (UCLouvain, Belgium) 4:00 PM - 5:00 PM JST (Japan Standard Time) on Thursday, Nov. 4th, 2021 ENGLISH WORKSHOP Mass spectrometry (MS)-based single-cell proteomics (SCP) is an emerging field that requires a dedicated computational environment. QFeatures along with its extension scp allow for standardized analysis of SCP data. The workshop will start by introducing the QFeatures class and its functions to perform generic proteomics data analysis. We will then move to SCP and present how scp extends QFeatures to single-cell applications. The remainder of the workshop will be a hands-on session where attendees will be guided through the reproduction of a real-life analysis of published SCP data. Along the reproduction exercise, we will point out to current challenges that still need to be tackled computationally. This workshop is meant for inexperienced users that want to learn how to perform current state-of-the-art analysis of SCP data as well as experienced developers interested in contributing to an emerging and exciting single-cell technology. This workshop is provided as two vignettes. The first vignette provides a general introduction to the QFeatures class in the general context of MS-based proteomics data manipulation. The second vignette focuses on single-cell application and introduces the scp package as an extension of QFeatures. This second vignette also provides exercises that give the attendee the opportunity to apply the learned concepts to reproduce a published analysis on a subset of a real data set. Moderator: Peter Hickey (Walter and Eliza Hall Institute of Medical Research)
Single-cell proteomics data analysis using QFeatures and scp
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLkNMWnhqSUtpa0lV
Visualize Data on Spirals Zuguang Gu (German Cancer Research Center, Germany) 3:30 PM - 3:55 PM JST (Japan Standard Time) on Thursday, Nov. 4th, 2021 CONTRIBUTED TALK The spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The spiralize package is available at https://CRAN.R-project.org/package=spiralize. Moderator: Kozo Nishida (RIKEN)
Visualize Data on Spirals
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLm8tUHh2Qm5rWW9J
Visualization and exploration of MSAs and associated data with ggmsa 周烺 Lang Chau (Southern Medical University, China) 4:00 PM - 5:00 PM JST (Japan Standard Time) on Thursday, Nov. 4th, 2021 MANDARIN WORKSHOP 在多序列比对(Multiple Sequence Alignment, MSA)的可视化中，普遍采用堆叠可视化的形式，其以行表示每条序列，以列表示相同位置的不同残基字符。我们开发的R包ggmsa在可视化层面扩展了这种堆叠形式的可视化方法，使得MSA图形可以与各类数据进行整合。例如：允许将序列二级结构信息、基因座位点信息或表型数据等序列相关的数据集与MSA结合，以此来探索序列-结构-功能三者之间的关系。并且，我们还开发和整合多种特别的比对序列可视化方法，包核苷酸差异图、核苷酸相似性图 (鉴定序列重组信号)、序列集束(Sequence Bundles)和序列标识(Sequence Logos)，这允许用户从不同角度探索序列的特征。ggmsa可以在git:上找到 https://github.com/YuLab-SMU/ggmsa Workshop将分为三个部分: 使用图形语法对MSA进行可视化和注释。 整合MSA图形和其他数据。 通过其他序列可视化方法探索序列特征。 The stacked alignment graphic that represents all individual sequences as rows and homologous residue positions as columns is used almost universally in the visualization of multiple sequence alignment (MSA). We present the R package ggmsa to extend MSA visualization method via integrating stacked MSAs and associated data. Sequence-related data sets such as secondary structures, genes locus, or phenotype are allowed to combining with MSA for exploring the sequence-structure-function relationship. And we developed and integrated multiple alignment visualization methods such as nucleotide difference plots, nucleotide similarity plots, and sequence bundles to exploring multifaceted sequence features from multiple perspectives. The ggmsa package is available at https://github.com/YuLab-SMU/ggmsa. The workshop will be organized into three broad sections: Visualization of MSAs and annotations via Grammar of Graphics Integration of stacked MSAs plot and associated data. Exploration of sequence features via different sequence visualization methods Moderator: Xueyi Dong (Walter and Eliza Hall Institute of Medical Research)
Visualization and exploration of MSAs and associated data with ggmsa
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLmtRYXVQcXZ3czJr
FANTOM project and data resource for understanding the transcription in the mammalian genomes Takeya Kasukawa (RIKEN, Japan) 1:00 PM - 1:40 PM JST (Japan Standard Time) on Thursday, Nov. 4th, 2021 KEYNOTE Takeya Kasukawa is the team leader of RIKEN Center for Integrative Medical Sciences, Laboratory for Large-Scale Biomedical Data Technology. His major is the development of data management technology that will lead to new discoveries in the field of biomedical science. He has maintained the database of the international collaborative research “FANTOM (Functional ANnoTation of Mammalian Genome) Project”. This database is widely provided to the research community and is expected to contribute to the elucidation of biological phenomena. Moderator: Kozo Nishida (RIKEN)
FANTOM project and data resource for understanding the transcription in the mammalian genomes
YouTube Video VVVxYU1TUWRfaC0yRURHc1U2V0RpWDBRLkxMSVF3VmxHNzZF
Load More... Subscribe

Copyright © 2021 IRZU Inštitut za raziskovanje zvočnih umetnosti

Join us at the first CRISPR workshop in Europe in July 2022. Erasmus+ funds available!
Learn more
This is default text for notification bar
Learn more