Introduction to Bioconductor from edX

We begin with an introduction to the relevant biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

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What's inside

Learning objectives

What we measure with high-throughput technologies and why
Introduction to high-throughput technologies
Next generation sequencing
Microarrays

Preprocessing and normalization
The bioconductor genomic ranges utilities
Genomic annotation

What we measure with high-throughput technologies and why
Introduction to high-throughput technologies
Next generation sequencing
Microarrays
Preprocessing and normalization
The bioconductor genomic ranges utilities
Genomic annotation

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Examines next-generation sequencing and microarrays, which are core technologies in bioinformatics

Focuses on real-world applications in biology

Introduces students to Bioconductor, a powerful open-source software platform for bioinformatics

Provides a strong foundation for advanced statistical concepts such as hierarchical models

Develops advanced software engineering skills, including parallel computing and reproducible research concepts

Taught by Rafael Irizarry, Vincent Carey, and Michael Love, who are recognized for their work in genomics and bioinformatics

Reviews summary

Bioconductor for genomics data analysis

According to learners, Introduction to Bioconductor provides a solid foundation for analyzing high-throughput biological data using R and the Bioconductor ecosystem. Students highlight that it effectively covers key concepts and packages such as GenomicRanges and limma. The practical labs and exercises are frequently mentioned as very helpful for applying the concepts. However, many reviewers caution about the steep learning curve, noting that the course assumes significant prerequisites in R programming and statistics, which can make the pacing feel fast and lead to a challenging experience for those without sufficient background. It is considered a crucial and essential course for researchers in genomics and life sciences.

Labs and exercises are very helpful.

"The labs are helpful."

"The exercises were practical."

"Labs are well-designed."

Essential for genomics data analysis.

"Essential for anyone in genomics."

"Crucial course for the Genomics Data Analysis certificate."

"Best course on Bioconductor I've found. Practical and theoretically sound."

Introduces essential Bioconductor tools.

"Covers key concepts and Bioconductor packages like `GenomicRanges` and `limma` effectively."

"Made learning Bioconductor accessible and covered essential tools."

"Helpful for getting started. `GenomicRanges` section was excellent."

Content moves quickly.

"Some parts felt rushed."

"Could use more depth on advanced topics."

"`limma` part felt a bit brief."

Needs solid R and stats foundation.

"Assumes more background in stats/R than expected from an 'Introduction'."

"Too difficult. Expected a gentler introduction to R/stats before diving into Bioconductor."

"Pacing is quick on the stats/programming side, as warned."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Bioconductor with these activities:

Find a mentor

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Finding a mentor can provide you with guidance and support throughout your learning journey.

Browse courses on Statistics

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Identify the skills or knowledge you want to develop.
Find someone who has those skills or knowledge and is willing to mentor you.
Meet with your mentor regularly and ask questions.

Review prerequisites

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Reviewing the prerequisites for this course will help you refresh your knowledge and make sure you have a solid foundation before starting the course.

Browse courses on Statistics

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Identify the prerequisites for the course.
Review your notes or textbooks from previous courses.
Complete practice problems or exercises.

Review the textbook

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Reading the textbook will help you reinforce the conceptos learned in class and gain a deeper understanding of the material.

View Data Analysis for the Life Sciences with R on Amazon

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Read the textbook chapters assigned for each module.
Take notes and highlight important passages
Complete the end of chapter exercises

Five other activities

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Follow online tutorials

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Following online tutorials will allow you to learn new skills and concepts at your own pace and reinforce what you have learned in class.

Browse courses on Next Generation Sequencing

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Identify the topics you need to learn more about.
Find online tutorials on these topics.
Follow the tutorials and complete the exercises.

Participate in study groups

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Participating in study groups will allow you to discuss the course material with your peers, ask questions, and get feedback on your work.

Browse courses on Genomics

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Find a study group to join.
Attend study group meetings regularly.
Participate in discussions and ask questions.

Solve practice problems

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Solving practice problems will help you reinforce the concepts learned in class and improve your problem-solving skills.

Browse courses on Linear Models

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Identify the topic you need to practice.
Find practice problems online or in textbooks.
Solve the problems and check your answers.

Create a cheat sheet

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Creating a cheat sheet will help you summarize the key concepts and formulas from the course, which can be useful for quick reference during exams or when working on projects.

Browse courses on Genomics

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Identify the key concepts and formulas you need to include.
Create a visually appealing and easy-to-read cheat sheet.
Review your cheat sheet regularly.

Develop a data analysis pipeline

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Developing a data analysis pipeline will give you hands-on experience with the entire data analysis process, from data preprocessing to statistical analysis.

Browse courses on Genomics

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Identify the data you need to analyze.
Preprocess the data.
Perform statistical analysis on the data.
Interpret the results of your analysis.

Career center

Learners who complete Introduction to Bioconductor will develop knowledge and skills that may be useful to these careers:

Laboratory Technician

Laboratory technicians perform experiments and tests in a laboratory setting. They use their knowledge of science and technology to operate and maintain laboratory equipment, and to collect and analyze data. This course provides a strong foundation in the laboratory techniques used in the life sciences, and it would be particularly useful for laboratory technicians who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Laboratory Technician

Computational Biologist

Computational biologists use their knowledge of computer science and biology to develop and apply computational methods to answer questions about the natural world. This course provides a strong foundation in the computational methods used in the life sciences, and it would be particularly useful for computational biologists who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Computational Biologist

Research Scientist

Research scientists conduct experiments and studies to investigate natural phenomena and develop new knowledge. They use their knowledge of science and technology to design and conduct experiments, and to analyze and interpret data. This course provides a strong foundation in the scientific principles used in research, and it would be particularly useful for research scientists who want to work with high-throughput data in the life sciences. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Research Scientist

Data Scientist

Data scientists use their knowledge of statistics, computer science, and business to solve problems in a variety of industries. This course provides a strong foundation in the statistical and computational methods used in data science, and it would be particularly useful for data scientists who want to work with high-throughput data in the life sciences. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Data Scientist

Scientific Programmer

Scientific programmers use their knowledge of computer science and science to develop and apply computational methods to solve problems in science and engineering. This course provides a strong foundation in the computational methods used in the life sciences, and it would be particularly useful for scientific programmers who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Scientific Programmer

Molecular Biologist

Molecular biologists study the structure and function of molecules in living organisms. They use their knowledge of molecular biology to understand how cells work and how they interact with each other. This course provides a strong foundation in the molecular principles used in the life sciences, and it would be particularly useful for molecular biologists who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Molecular Biologist

Biostatistician

Biostatisticians play a vital role in the design and analysis of experiments in the life sciences. They use their knowledge of statistics and biology to develop and apply statistical methods to answer questions about the natural world. This course provides a strong foundation in the statistical methods used in the life sciences, and it would be particularly useful for biostatisticians who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Biostatistician

Statistician

Statisticians collect, analyze, and interpret data. They use their knowledge of statistics to design and conduct studies, and to analyze and interpret data. This course provides a strong foundation in the statistical methods used in the life sciences, and it would be particularly useful for statisticians who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Statistician

Genomicist

Genomicists study the genomes of living organisms. They use their knowledge of genomics to understand how organisms function and how they evolve. This course provides a strong foundation in the genomic principles used in the life sciences, and it would be particularly useful for genomicists who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Genomicist

Geneticist

Geneticists study the genes and chromosomes of living organisms. They use their knowledge of genetics to understand how organisms inherit traits and how traits are passed down from one generation to the next. This course provides a strong foundation in the genetic principles used in the life sciences, and it would be particularly useful for geneticists who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Geneticist

Software Engineer

Software engineers design, develop, and maintain software applications. They use their knowledge of computer science and software engineering to create software that meets the needs of users. This course provides a strong foundation in the software engineering principles used in the life sciences, and it would be particularly useful for software engineers who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Software Engineer

Pharmacologist

Pharmacologists study the effects of drugs on living organisms. They use their knowledge of pharmacology to develop new drugs and treatments for diseases. This course provides a strong foundation in the pharmacological principles used in the life sciences, and it would be particularly useful for pharmacologists who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Pharmacologist

Healthcare Data Analyst

Healthcare data analysts use their knowledge of statistics, computer science, and healthcare to solve problems in the healthcare industry. This course provides a strong foundation in the statistical and computational methods used in healthcare data analysis, and it would be particularly useful for healthcare data analysts who want to work with high-throughput data in the life sciences. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Healthcare Data Analyst

Medical Scientist

Medical scientists use their knowledge of science and medicine to develop new drugs and treatments for diseases. This course provides a strong foundation in the scientific principles used in medical research, and it would be particularly useful for medical scientists who want to work with high-throughput data in the life sciences. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

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Physician

Physicians diagnose and treat diseases in patients. They use their knowledge of medicine to provide medical care to patients and to help them maintain their health. This course provides a strong foundation in the medical principles used in the life sciences, and it would be particularly useful for physicians who want to work with high-throughput data. The course covers topics such as preprocessing and normalization of data, the use of genomic ranges utilities, and genomic annotation.

See salaries and explore the career path for Physician