Analyzing Genomic Data in R

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Learn Path Description

Are you interested in analyzing next-generation sequencing data but lacking in strong computational skills? In this skills track, geared towards non-computational biologists, you will learn to use Bioconductor, the specialized repository for bioinformatics software, along with essential Bioconductor packages. Then, you'll learn about current best-practice workflows for RNA sequencing differential expression analysis, as well as Chip-sequencing data.

Skills You Will Gain

Courses In This Learning Path

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Total Duration

4 hours

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Level

Beginner

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Learn Type

Certifications

Introduction to Bioconductor in R

Many biological research, including medicine, biotech and biotech, focuses now on sequence analysis. We are now generating big data from the whole genome that can be used for biological research. To help you get started, the Bioconductor project is presented. Bioconductor is the platform for sharing software tools (packages and workflows) that can be used to analyze and understand genomic data. Bioconductor is an open-source software resource that has been developed by the community. It's a great platform. The essential Bioconductor software packages will be taught, along with some of the built in datasets. It will be a wonderful experience to use BSgenome or Biostrings with actual datasets of different species.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

RNA-Seq with Bioconductor in R

RNA-Seq is a next-generation sequencing technique which identifies genes or pathways that are responsible for certain diseases and conditions. It's exciting. As high-throughput sequencing data becomes more affordable and easier to access, the ability to analyze it is becoming a valuable skill. You will learn about the RNA sequencing process and how to identify genes or biological processes that might be relevant for you. This course will give you a brief overview of the RNA sequencing process, with a special focus on differential expression (DE). The course will start with gene counts. The course will then discuss how to prepare data for DE analysis. The DESeq2 package is used to model the count data using a negative binary model, and test for differentially expressed genes. You can visualize the results with heatmaps and volcano plots. You can save the genes that are differentially expressed.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Differential Expression Analysis with limma in R

Functional genomic technologies, such as sequencing and microarrays, can be used by scientists to get unbiased measurements of gene expression at large scale. No matter if you're creating your data from publicly available data sets or creating it, you will need to be able to analyze it. This course will show you how to use R/Bioconductor's flexible package limma to perform differential expression analysis on the most popular experimental designs. Additionally, you will learn how to prepare data, correct batch effects and visual assess results. You can also conduct enrichment testing. After completing this course, you will be able use these strategies for insight into any functional genomes study.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

ChIP-seq with Bioconductor in R

Bioinformatics has become a crucial branch. It allows us to see the inner workings of our cells. All of them share the same genome. This is true regardless of whether they're brain cells that help you read this webpage or immune cells that check your body for microorganisms. The active genes are what distinguish them. Complex proteins activate and deactivate various genes to determine these genes. If this regulatory machinery becomes too complex, it can lead to cancer or other serious diseases. The function and causes behind disease can be analyzed using ChIP-seq. It can provide insights into how we can intervene to prevent cells from spinning out of control. This course will show you how to analyze ChIP–seq data with R.

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