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Dr. Anna Protasio, Matthew Dorman, Martin Aslett, Dr. Christine Boinett, Dr. Ulrike Böhme, and Dr. Pablo Tsukayama

Week 1 Week 2 Week 3 Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course would benefit those interested in learning how to use tools to investigate and research bacterial genomes, and acquire bioinformatics skills to evaluate the role of microbial genes in disease. Learners will gain experience in comparative genomics, using the Artemis Comparison Tool to probe, visualise and compare genomes, and analyse the results. This course will be of interest to anyone interested in microbiology, including undergraduates, post-graduates, biomedical researchers, microbiologists, bioinformaticians, teachers, and healthcare professionals. The opportunity to gain experience in using the Artemis Comparison Tool, a computational tool designed for comparative genomics, will also be of interest to all those who have studied our pre-requisite courses: those with an interest in genomics and disease outbreaks, teachers and their 16-18-year-old science and computing students. Ideally, you will have completed Bacterial Genomes: From DNA to Protein Function Using Bioinformatics and Bacterial Genomes: Accessing and Analysing Microbial Genome Data before joining the course. You can use the hashtag #FLartemis2act to talk about this course on social media.

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Week 1 Week 2 Week 3 Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course would benefit those interested in learning how to use tools to investigate and research bacterial genomes, and acquire bioinformatics skills to evaluate the role of microbial genes in disease. Learners will gain experience in comparative genomics, using the Artemis Comparison Tool to probe, visualise and compare genomes, and analyse the results. This course will be of interest to anyone interested in microbiology, including undergraduates, post-graduates, biomedical researchers, microbiologists, bioinformaticians, teachers, and healthcare professionals. The opportunity to gain experience in using the Artemis Comparison Tool, a computational tool designed for comparative genomics, will also be of interest to all those who have studied our pre-requisite courses: those with an interest in genomics and disease outbreaks, teachers and their 16-18-year-old science and computing students. Ideally, you will have completed Bacterial Genomes: From DNA to Protein Function Using Bioinformatics and Bacterial Genomes: Accessing and Analysing Microbial Genome Data before joining the course. You can use the hashtag #FLartemis2act to talk about this course on social media.

Topics Covered

  • Introduction to comparative genomics
  • Introduction to ACT
  • Analyse available data
  • Generate your own comparison files
  • Make your own comparisons in ACT
  • Identify pseudogenes in
  • using ACT
  • Peer review project: Comparative genomics on two clinically relevant plasmids from

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Reviews summary

Comparative genomics with artemis act

According to learners, this course offers a positive:practical introduction to neutral:comparative genomics using the positive:Artemis Comparison Tool (ACT). Students seeking to positive:acquire bioinformatics skills for investigating bacterial genomes will find the content positive:highly relevant, especially for evaluating microbial gene roles in disease. However, prospective learners are strongly advised to meet the warning:prerequisite courses as the material quickly advances beyond introductory concepts, potentially causing difficulty for those without the necessary foundational knowledge.
Best suited for those with a strong background.
"This course is clearly designed for researchers and professionals, not absolute beginners in genomics."
"As a bioinformatician, I found the depth appropriate for advancing my comparative genomics skills."
"While it starts with an introduction, it quickly assumes a level of prior knowledge common among post-graduates or biomedical researchers."
Develops applicable bioinformatics skills for research.
"I learned practical bioinformatics skills for investigating bacterial genomes and understanding disease mechanisms."
"The content directly applies to evaluating the role of microbial genes in disease, which is highly relevant to my field."
"This course provided a concrete way to apply genomic analysis to real-world biological questions."
Hands-on mastery of the Artemis Comparison Tool.
"I found the hands-on experience with the Artemis Comparison Tool to be the most valuable part of the course."
"The course's emphasis on using ACT for visualizing and comparing genomes was exactly what I needed for my research."
"It's great to finally get practical experience with a tool like ACT in a structured learning environment."
Prior courses are crucial for a successful learning experience.
"I strongly recommend completing the prerequisite courses; otherwise, the advanced topics might be overwhelming."
"Without the foundational knowledge from the earlier courses, I struggled to keep up with some concepts."
"Make sure you have a solid background in bacterial genomes and bioinformatics data before starting this course."

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Learners who complete Bacterial Genomes: Comparative Genomics using Artemis Comparison Tool (ACT) will develop knowledge and skills that may be useful to these careers:

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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.
Is widely considered a foundational text in bioinformatics, offering a comprehensive overview of core concepts, particularly sequence and genome analysis. It is highly valuable for gaining a broad understanding and is often used as a textbook in academic settings. While not the most recent, its clear explanations and breadth of coverage make it an essential reference.
Provides a solid introduction to the field of bioinformatics, covering a wide range of topics from sequence analysis to genomics and data mining. It is well-suited for beginners and provides a good foundation for further study. The clear explanations and practical examples make it a valuable resource for those new to the subject.
A classic in the field, this book delves into the probabilistic models that are fundamental to sequence analysis. It provides a rigorous treatment of topics like hidden Markov models and their applications. is essential for those seeking a deeper theoretical understanding and key reference for researchers.
Focuses on the statistical foundations of bioinformatics, which are crucial for interpreting biological data. It covers probability, statistical inference, and their applications in bioinformatics analyses like sequence alignment and phylogenetic tree estimation. It is valuable for students and researchers needing a solid statistical background.
Provides a broad introduction to bioinformatics with a strong emphasis on functional genomics. It covers a wide range of topics, including sequence analysis, gene expression, and systems biology, making it suitable for gaining a general understanding and exploring the applications of bioinformatics in modern biological research.
Given the prevalence of R in bioinformatics, this book valuable resource for learning how to use R for biological data analysis. Written by one of the creators of R and a leading figure in the Bioconductor project, it covers various programming aspects and their applications in bioinformatics and computational biology problems.
Focuses on essential data skills for bioinformatics, emphasizing reproducible research practices using open-source tools like the command line and R. It is highly practical and valuable for students and professionals who need to manage and analyze biological data effectively.
While not solely a bioinformatics book, this text provides a comprehensive introduction to systems biology, a field closely related to bioinformatics that focuses on understanding biological systems as a whole. It is valuable for gaining a broader perspective on how bioinformatics contributes to systems-level analysis.
Provides a deep dive into the algorithms that are fundamental to many bioinformatics tasks, particularly in sequence analysis. It more theoretical text, well-suited for those with a computer science background seeking a rigorous understanding of the underlying algorithms.
Offers a gentle introduction to bioinformatics for those with little or no prior experience. It explains core concepts in an accessible way and guides readers on using online resources and tools. It good starting point for beginners to get a general understanding of the field.
Focuses on statistical methods relevant to modern biological data analysis, particularly in the context of high-throughput experiments. It is valuable for researchers and students who need to apply statistical rigor to their bioinformatics analyses.
This practical guide focuses specifically on utilizing NCBI databases and performing sequence alignments using tools like BLAST. It is highly relevant for anyone working with biological sequence data and provides step-by-step guidance with examples.
This cookbook provides practical recipes for performing various bioinformatics tasks using R and Bioconductor. It valuable resource for those who want to apply R programming to solve specific problems in areas like RNA-Seq analysis, genomics, and data visualization.
While not solely focused on bioinformatics, this book is highly relevant for anyone working with biological data, which often requires command-line processing. It teaches essential data manipulation skills using command-line tools, which are fundamental for efficient bioinformatics workflows.

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