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Dr. Anna Protasio,
Matthew Dorman,
Martin Aslett,
Dr. Christine Boinett,
Dr. Ulrike Böhme,
and
Dr. Pablo Tsukayama
Topics Covered
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Topics Covered
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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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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.
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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Time
15 hours
Via
FutureLearn
Institution
Wellcome Genome Campus Advanced Courses and Scientific Conferences
Instructors
Dr. Anna Protasio
Matthew Dorman
Martin Aslett
Dr. Christine Boinett
Dr. Ulrike Böhme
Dr. Pablo Tsukayama
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