Udemy - Learn Amr Detection - From Raw Genomic Reads To Ml Predic...
- Category Other
- Type Tutorials
- Language English
- Total size 3.4 GB
- Uploaded By freecoursewb
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Infohash : A721A9BF668EFAB437CF2DF7A086BDE5574798EA
Learn Amr Detection: From Raw Genomic Reads To Ml Prediction
https://WebToolTip.com
Published 10/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.44 GB | Duration: 7h 23m
Build end-to-end AMR analysis pipelines on Linux identify resistance genes, and prepare data for advanced ML predictions
What you'll learn
Understand the fundamentals of Antimicrobial Resistance (AMR) and its biological significance.
Learn how bioinformatics tools and databases are applied in AMR research and genomic analysis.
Set up a Linux-based bioinformatics environment and efficiently navigate the Linux file system.
Perform data preprocessing and quality control using tools like FastQC and Fastp.
Conduct de novo bacterial genome assembly using SPAdes and assess assembly quality with Quast.
Annotate genomes using Prokka and interpret gene annotation results in the context of AMR research.
Detect antimicrobial resistance genes from multiple databases using ABRicate.
Integrate all steps into a complete AMR analysis pipeline from raw data to gene detection.
Generate an AMR gene presence–absence matrix and prepare data for downstream analysis using Python.
Build and interpret machine learning models to predict antimicrobial resistance patterns based on genomic data.
Requirements
No prior experience in bioinformatics or Linux is required, this course is designed to start from the basics.
A computer (Windows, macOS, or Linux) with stable internet access for software installation and dataset downloads.
Basic understanding of biology or genetics will be helpful but not mandatory.
Interest in genomic data analysis, antimicrobial resistance, or computational biology.
Willingness to learn through hands-on practice using real-world data and pipelines.
Familiarity with Python programming will be an advantage but is not required, all steps are explained in detail.
Files:
[ WebToolTip.com ] Udemy - Learn Amr Detection - From Raw Genomic Reads To Ml Prediction- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction to Antimicrobial Resistance and Bioinformatics
- 1 - What is Antimicrobial Resistance AMR.mp4 (42.2 MB)
- 2 - Bioinformatics in AMR Research.mp4 (53.7 MB)
- 3 - Overview of the Course Pipelines.mp4 (164.6 MB)
- 4 - Setting Up Your Environment and Downloading Raw Data.mp4 (287.0 MB)
- 4 - download-raw-data.sh (0.3 KB)
- 10 - Setting Up Conda and Environments.mp4 (33.4 MB)
- 5 - Introduction to Linux.mp4 (49.2 MB)
- 6 - Environment Setup On Windows.mp4 (156.3 MB)
- 7 - Navigating The Linux File System.mp4 (58.9 MB)
- 8 - Basic Linux Commands For Bioinformatics.mp4 (81.9 MB)
- 9 - Working with Files Viewing Editing and Processing Genomic Data.mp4 (149.0 MB)
- 11 - Understanding Sequencing Data Formats.mp4 (213.6 MB)
- 12 - Initial Quality Assessment with FastQC.mp4 (335.0 MB)
- 13 - Read Trimming and Filtering with Fastp.mp4 (146.4 MB)
- 14 - Automation in Quality Control.mp4 (149.1 MB)
- 14 - Qc-pipeline.sh (3.8 KB)
- 15 - Principles of De Novo Genome Assembly.mp4 (51.8 MB)
- 16 - Using SPAdes for Bacterial Assembly.mp4 (146.9 MB)
- 17 - Quality Assessment with Quast.mp4 (79.3 MB)
- 18 - Pipeline Integration and Assembly Output.mp4 (39.6 MB)
- 18 - assembly-pipeline.sh (4.4 KB)
- 19 - What is Genome Annotation.mp4 (76.0 MB)
- 20 - Using Prokka for Prokaryotic Annotation.mp4 (159.7 MB)
- 21 - Integrating Pipeline and Interpreting Annotation Results.mp4 (134.1 MB)
- 21 - annotation.sh (2.4 KB)
- 22 - AMR Gene Databases Overview.mp4 (32.2 MB)
- 23 - Setting Up and Running ABRicate.mp4 (212.4 MB)
- 24 - MultiDatabase Analysis Pipeline.mp4 (104.2 MB)
- 24 - amr-detection.sh (3.5 KB)
- 25 - Creating Amr Gene PresenceAbsence Matrix Using Python.mp4 (292.9 MB)
- 25 - create-amr-matrix.py (4.6 KB)
- 25 - ml-matrix.csv (30.4 KB)
- 26 - AMR Predictions With Machine Learning Models.mp4 (274.1 MB)
- 26 - model.py (25.9 KB)
- Bonus Resources.txt (0.1 KB)
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