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Martin Aslett,
Dr. Anna Protasio,
Matthew Dorman,
Dr. Christine Boinett,
Dr. Pablo Tsukayama,
Professor Nicholas Thomson,
and
Dr. Ulrike Böhme
Topics Covered
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Topics Covered
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Topics Covered
- Multi-FASTA files
- Reference and draft bacterial genomes
- Introduction to Artemis, an open source genome browser
- Genome annotation
- Genomic regions defined by GC (guanine-cytosine) content
- Accessing and downloading whole genome sequences
- Pathogenicity islands
- Peer-reviewed project: Ecological niche adaptation between two
- species
- Pseudogenes in
- species
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Reviews summary
Bioinformatics and bacterial genomes
This course provides an in-depth look at bioinformatics as it is applied to analyzing microbial genome data. Through a series of interactive exercises, learners will learn to use a genome browser to identify essential genes, probable roles of genes, and even predict pathogenicity islands based on GC% content. The course materials are comprehensive and easy to follow regardless of a student's level of experience with bioinformatics. This course has received an overwhelming amount of praise from students who have taken it, and is highly recommended for anyone interested in learning more about this field.
Microbial bioinformatics in web browser.
"The course made microbial bioinformatics using a web browser a piece of cake!"
Focused on bioinformatics.
"This course provides an in-depth look at bioinformatics as it is applied to analyzing microbial genome data."
"great video aids and highly lucid way of describing complex subject matter like bioinformatics."
Uses the Artemis genome browser.
"I had never heard of Artemis before the course but at the end I was very happy with what I had learnt."
"The course was centered on bacteria genomics and comparative genomics using Artemis, the EMBL and NCBI."
Offers in depth learning opportunity.
"I started off as a novice but now believe I can mentor someone to some extent. It is so resourceful and demystified."
Activities
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Bacterial Genomes: Accessing and Analysing Microbial Genome Data.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Bacterial Genomes: Accessing and Analysing Microbial Genome Data will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/cxb80s/bacterial
Reading list
We haven't picked any books for this reading list yet.
Although not exclusively focused on bacterial genomes, this book provides a comprehensive overview of bacterial physiology and metabolism, including discussions on bacterial genome structure and function.
Offers a fundamental understanding of bacterial molecular biology, covering topics such as bacterial genome structure and gene expression. It is suitable for students or researchers seeking a foundational knowledge of this field.
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Provides a comprehensive overview of bioinformatics in German. It covers topics such as DNA sequencing, gene expression analysis, and protein structure prediction. It good resource for students and researchers who are interested in learning more about the field in German.
Provides a broad overview of bioinformatics, covering topics such as molecular biology, computer science, and statistics. It good resource for students who are interested in learning more about the field.
Provides a detailed overview of bioinformatics algorithms. It covers topics such as sequence alignment, gene finding, and protein structure prediction. It good resource for students and researchers who are interested in developing new bioinformatics algorithms.
Provides a detailed overview of statistical methods used in bioinformatics. It covers topics such as data analysis, machine learning, and statistical modeling. It good resource for students and researchers who are interested in using statistical methods to analyze biological data.
Provides a detailed overview of machine learning methods used in bioinformatics. It covers topics such as supervised learning, unsupervised learning, and feature selection. It good resource for students and researchers who are interested in using machine learning methods to analyze biological data.
Provides a detailed overview of deep learning methods used in bioinformatics. It covers topics such as convolutional neural networks, recurrent neural networks, and autoencoders. It good resource for students and researchers who are interested in using deep learning methods to analyze biological data.
Provides a detailed overview of big data in bioinformatics. It covers topics such as data management, data analysis, and data visualization. It good resource for students and researchers who are interested in working with big data in bioinformatics.
Provides a detailed overview of programming for bioinformatics using Python. It covers topics such as data structures, algorithms, and machine learning. It good resource for students and researchers who are interested in developing bioinformatics software.
Provides a detailed overview of data analysis for bioinformatics using R. It covers topics such as data wrangling, data visualization, and statistical modeling. It good resource for students and researchers who are interested in using R to analyze biological data.
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A comprehensive guide to genome analysis, covering a wide range of topics from DNA sequencing to statistical methods. Notable for its clear and concise explanations, making it accessible to readers with diverse backgrounds.
Teaches the essential data science skills for analyzing large-scale biological data. Provides hands-on guidance on using open-source tools and best practices for reproducible and robust research.
Provides a comprehensive overview of statistical methods used in bioinformatics. Covers topics such as hypothesis testing, multiple testing, and supervised learning, making it a valuable reference for researchers using statistical methods in genome analysis.
An accessible introduction to algorithms and data structures used in bioinformatics. Covers topics such as string alignment, phylogenetic tree construction, and sequence assembly. Useful for understanding the computational foundations of genome analysis.
Focuses on the bioinformatics approaches used in comparative genomics, covering topics such as sequence alignment, phylogenetic analysis, and comparative genomics databases.
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Provides a comprehensive overview of the field of genomics, including comparative genomics. It is written in a clear and concise style, and it is suitable for students and researchers alike.
Provides a comprehensive overview of the computational methods used in comparative genomics. It is written in a clear and concise style, and it is suitable for students and researchers alike.
Provides a comprehensive overview of evolutionary genomics, including comparative genomics. It is written in a clear and concise style, and it is suitable for students and researchers alike.
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Provides a detailed overview of the evolution of the human body. It is written by a leading expert in the field and is essential reading for anyone interested in learning more about our evolutionary history.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cxb80s/bacterial
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Time
15 hours
Via
FutureLearn
Institution
Wellcome Genome Campus Advanced Courses and Scientific Conferences
Instructors
Martin Aslett
Dr. Anna Protasio
Matthew Dorman
Dr. Christine Boinett
Dr. Pablo Tsukayama
Professor Nicholas Thomson
Dr. Ulrike Böhme
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