Introduction to Bioconductor

About

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:

• PH525.1x: Statistics and R for the Life Sciences
• PH525.2x: Introduction to Linear Models and Matrix Algebra
• PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
• PH525.4x: High-Dimensional Data Analysis

Genomics Data Analysis:

• PH525.5x: Introduction to Bioconductor
• PH525.6x: Case Studies in Functional Genomics
• PH525.7x: Advanced Bioconductor

This class was supported in part by NIH grant R25GM114818.

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What you will learn

• 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

Skills you learn

• Matrix Algebra
• Microarrays
• Data Analysis
• Next-Generation Sequencing
• Biology
• Software Engineering
• Life Sciences
• Functional Genomics
• Linear Model
• Data Warehousing
• Statistics
• Statistical Inference
• Algorithms
• Bioconductor (Bioinformatics Software)
• Statistical Methods
• DNA Sequencing
• R (Programming Language)

Auto Summary

"Introduction to Bioconductor" is a comprehensive course offered under the domain of IT & Computer Science, focusing on the integration of biological data with statistical and computational tools using Bioconductor. The course is designed and instructed by experts at HarvardX and is available on edX. This self-paced course delves into the biology of what we measure and why, with an emphasis on next-generation sequencing and microarrays. Learners will be guided on importing raw data and experimental information into R, utilizing Bioconductor classes for data organization from various sources. The curriculum addresses the identification of genomic features using intervals in genomic coordinates and explores efficient algorithms for genomic interval computation. Additionally, statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are covered, featuring packages like limma. Structured into seven segments, this course is suitable for a diverse audience, including statisticians who may skip the initial parts and biologists who may bypass some introductory biology lectures. The content complexity escalates, introducing advanced statistical concepts and software engineering skills by the third and fourth segments, respectively. The course forms part of two Professional Certificates: "Data Analysis for Life Sciences" and "Genomics Data Analysis," comprising the following modules: **Data Analysis for Life Sciences:** 1. PH525.1x: [Statistics and R for the Life Sciences](https://www.edx.org/course/statistics-and-r) 2. PH525.2x: [Introduction to Linear Models and Matrix Algebra](https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra) 3. PH525.3x: [Statistical Inference and Modeling for High-throughput Experiments](https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug) 4. PH525.4x: [High-Dimensional Data Analysis](https://www.edx.org/course/high-dimensional-data-analysis) **Genomics Data Analysis:** 1. PH525.5x: [Introduction to Bioconductor](https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys) 2. PH525.6x: [Case Studies in Functional Genomics](https://www.edx.org/course/case-studies-in-functional-genomics) 3. PH525.7x: [Advanced Bioconductor](https://www.edx.org/course/advanced-bioconductor) The entire series spans approximately 12 weeks, and learners can choose to enroll in the full series or individual courses based on their interests. The course is ideal for both beginners and advanced learners aiming to enhance their skills in data analysis and genomics. HarvardX encourages adherence to the edX honor code and participation in learning research. The institution is committed to a nondiscriminatory and harassment-free educational environment. Embark on this educational journey to master the integration of biological data with statistical and computational techniques through Bioconductor, and elevate your expertise in the evolving field of genomics.