galaxy-training-izs3

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上传日期:2023-09-23 16:49:42
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说明:  银河系靶向和全基因组数据宏基因组分析培训班,
(Training course on Galaxy for metagenomic analysis on targeting and whole genome data,)

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extras/get_data.md (6920, 2023-09-25)
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hands-on/18.hands-on.md (5483, 2023-09-25)
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images/07.krona_pie-01.png (287588, 2023-09-25)
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images/16S-align_summary.png (21596, 2023-09-25)
images/16S-count_seq.png (100078, 2023-09-25)
images/16S_gene.png (820512, 2023-09-25)
images/16S_merge_contigs.png (66698, 2023-09-25)
images/16S_variableregions.jpg (67927, 2023-09-25)
images/CRS4-HPC-logo.png (62646, 2023-09-25)
images/CRS4-NEXT-logo.png (50324, 2023-09-25)
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# Training course on Galaxy for metagenomic analysis on targeting and whole genome data [ https://next-crs4.github.io/galaxy-training-izs3](https://github.com/next-crs4/galaxy-training-izs3/blob/master/ https://next-crs4.github.io/galaxy-training-izs3) ## Welcome! - Why are we here? - What are your expectations? - What do you need Galaxy for? - What would you like to take home from the course? ## Learning outcomes * Utilize Galaxy to upload data, employ tools, view project histories, and execute workflows. * Employ statistical and scatterplot analysis to explore datasets. * Proficiently manage data, including conversion, reformatting, filtering, and sorting of text-based information. * Determine the most suitable approach for metagenomic data analysis. * Discriminate between tools suited for amplicon and shotgun data analysis. * Effectively visualize community structures. * Execute 16S rRNA sequencing analysis within Galaxy using the mothur toolsuite. * Assess sequencing experiment error rates via mock community data. * Master the visualization of sample diversity through Krona and Phinch tools. * Execute rigorous quality control of NGS raw data. * Extract comprehensive community profiles from microbiome datasets. * Proficiently reconstruct microorganism genomes within sampled communities. * Evaluate the quality of metagenomic assemblies. * Skillfully annotate bacterial genomes. * Visualize and interpret annotated genomic features. * Identify variants between reference genomes and read datasets. * Interpret SNP data within the context of read alignments to the genome. * Analyze the impact of variants on genomic features. * Detect pathogens by identifying virulence factor gene products. * Characterize strains and identify antimicrobial resistance genes within your samples. ## Computing resources The computing resources for this training have been provided by the CRS4 datacenter You will receive all the details to connect to Galaxy Instance: [https://izs3.crs4.it](https://github.com/next-crs4/galaxy-training-izs3/blob/master/https://izs3.crs4.it) ## Agenda ### **April 18-20, 2023** Title | Lecture / Practical ------|------------------- **Galaxy at a Glance** | Lecture **Introduction to Galaxy Analyses** | Lecture / Practical **Exploring datasets with statistics and scatterplots** | Practical **Data Manipulation** | Practical **Introduction to Metagenomics** | Lecture **Analyses of Metagenomics Data** | Lecture / Pracctical **16S Microbial Analysis with mothur** | Practical ### **September 26-28, 2023** Title | Lecture / Practical ------|------------------- **Introduction to Visualisations in Galaxy** | Lecture **Genomic Data Visualisation with JBrowse** | Practical **From NCBI's Sequence Read Archive (SRA) to Galaxy** | Practical **Quality Control** | Practical **Taxonomic Profiling and Visualization of Metagenomic Data** | Lecture / Practical **Assembly of metagenomic sequencing data** | Lecture / Practical **Genome annotation with Prokka** | Lecture / Practical **Microbial Variant Calling** | Lecture / Practical **Pangenome Analysis** | Lecture / Practical **Pathogen Detection** | Lecture / Practical **Hands-On** | Practical

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