Bioinformatics: Introduction to Bioconductor

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Learning Experience6.5

The course on introduction to Bioconductor focuses on biostructure, annotation, normalization, and interpretation of genome-scale assays.

Last updated on July 22, 2021 1:22 pm

Introduction

Considering two main measurement technologies, the Next-generation sequencing and microarrays, the introduction to Bioconductor focuses on biostructure, annotation, normalization, and interpretation of genome-scale assays.

About this course

In the introduction to the Bioconductor course, you’ll begin with an introduction to the relevant biology, explaining what we measure and why. Then your focus will be on the two main measurement technologies:

Next-generation sequencing and microarrays, then move on to describing how raw data and experimental information are imported into R and how can you 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 course 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 makeup 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.

What you will learn from Introduction to Bioconductor?

  • 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.

Prerequisites

  • PH525.3x, PH525.4x

Syllabus

1. Introduction and resources

2. Section 1: What we measure, why, and how

3. Section 2: Bioconductor basics with GRanges and Biostrings

4. Section 3: Management of genome-scale data with Bioconductor

5. Section 4: Genomic annotation with Bioconductor

6. Section 5: Inference for genomics with Bioconductor

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

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$149.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Free Course (Affordable Certificate)
  • English
  • Bioinformatics Genomics High-throughput techniques
Learning Experience
6.5
PROS: Decent introduction to Bioconductor and course provides real education. Packed with great material on par with the previous courses in the sequence. These courses make up two Professional Certificates and are self-paced. Official and verified, easily shareable course completion certificate available.
CONS: Need to take all 3 previous courses of series before taking this course. The difficulty level ramps up considerably towards the end. Some case studies need to be rectified.

Description

Introduction

Considering two main measurement technologies, the Next-generation sequencing and microarrays, the introduction to Bioconductor focuses on biostructure, annotation, normalization, and interpretation of genome-scale assays.

About this course

In the introduction to the Bioconductor course, you’ll begin with an introduction to the relevant biology, explaining what we measure and why. Then your focus will be on the two main measurement technologies:

Next-generation sequencing and microarrays, then move on to describing how raw data and experimental information are imported into R and how can you 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 course 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 makeup 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.

What you will learn from Introduction to Bioconductor?

  • 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.

Prerequisites

  • PH525.3x, PH525.4x

Syllabus

1. Introduction and resources

2. Section 1: What we measure, why, and how

3. Section 2: Bioconductor basics with GRanges and Biostrings

4. Section 3: Management of genome-scale data with Bioconductor

5. Section 4: Genomic annotation with Bioconductor

6. Section 5: Inference for genomics with Bioconductor

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Free Course (Affordable Certificate)
  • English
  • Bioinformatics Genomics High-throughput techniques

Videos: Bioinformatics: Introduction to Bioconductor

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Bioinformatics: Introduction to Bioconductor
Bioinformatics: Introduction to Bioconductor

$149.00

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