Learning path topics
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Module: AMR gene detection
AMR gene content can be assessed from the contigs to detect known resistance mechanisms and potentially identify novel mechanisms.
Time estimation: 2 hours
Learning objectives:
- Run a series of tool to assess the presence of antimicrobial resistance genes (ARG)
- Get information about...
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Module: Assembly
Assembly is a major step in the process of detecting AMR genes as it combines sequenced reads into contigs, longer sequences where it will be easier to identify genes and in particular AMR genes
Time estimation: 4 hours
Learning objectives:
- Run tools to evaluate sequencing data on...
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Module: Genome annotation
The generated contigs can be annotated to detect genes, potential plasmids, etc. This will help the AMR gene detection process, especially the verification and visualization
Time estimation: 3 hours
Learning objectives:
- Run a series of tool to annotate a draft bacterial genome for...
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Module: Species and contamination checking
Quality control and taxonomic assignation is useful in AMR detection to verify the quality of the data but also to check contamination and confirm species
Time estimation: 2 hours
Learning objectives:
- Run tools to evaluate sequencing data on quality and quantity
- Evaluate the output...
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R introduction
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Recommended follow-up tutorials
Time estimation: 4 hours
Learning objectives:
- Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
- Preprocess the sequencing data to remove adapters, poor quality base content and host/contaminating reads
- Perform taxonomy profiling indicating and...
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Reusing Biodiversity Data: Access, Integration, and Application
This module explores how biodiversity data processed through Plazi’s workflow can be accessed, integrated, and reused across multiple platforms. Participants will learn how to locate and retrieve data from open-access repositories such as GBIF, TreatmentBank, the Biodiversity Literature...
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Week 1: Python is a Calculator
Python can work a lot like a desktop calculator! A lot of mathematical expressions one is used to from maths classes are the same or very similar in Python. Functional notation is also introduced.
Time estimation: 1 hour
Learning objectives:
- Understand the fundamentals of object...
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Week 1: SQL Basics
The lesson is to be followed in class, and the game given as homework.
Time estimation: 5 hours
Learning Objectives
- Explain the difference between a table, a record, and a field.
- Explain the difference between a database and a database manager.
- Write a query to select all values...
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Week 2: Advanced SQL
Today we introduce complex operations like Joins.
Time estimation: 3 hours
Learning Objectives
- Define aggregation and give examples of its use.
- Write queries that compute aggregated values.
- Trace the execution of a query that performs aggregation.
- Explain how missing data is...