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1. Next-Gen Sequence Analysis Workshop (2018)
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¶
1. Next-Gen Sequence Analysis Workshop (2018)
1.1. Rooms and lead instructors!
1.2. Schedule, in brief:
1.3. Second week, July 9-July 14.
2. Workshop Code of Conduct
2.1. Need Help?
2.2. The Quick Version
2.3. The Less Quick Version
3. Booting a Jetstream Computer Instance for your use!
3.1. Request to log in to the Jetstream Portal
3.2. Use “XSEDE”
3.3. Fill in the username and password and click “Sign in”
3.4. Select Projects and “Create New Project”
3.5. Name the project for yourself, click “create”
3.6. Boot an instance with a pre-built image
3.7. Find the “DIBSI 2018 workshop image” image, click on it
3.8. Name it something simple
3.9. Wait for it to become active
3.10. Click on your new instance to get more information!
3.11. Miscellany
3.12. Suspend your instance
3.13. Shutting down your instance
3.14. Deleting your instance
4. Logging in to jetstream from your local terminal with a key file
4.1. Concerning Keys
4.2. Getting the Private Key
4.3. Getting your instance IP address
4.4. On MacOS/Linux
4.5. On Windows
5. Running command-line BLAST
5.1. Getting started
5.2. What is BLAST?
5.3. Why use the command line?
5.4. Running BLAST
6. Visualizing BLAST score distributions in RStudio
6.1. Getting started
6.2. Where are we?
6.3. Enter some R commands
6.4. Some questions for discussion/points to make:
7. Short read quality and trimming
7.1. Getting started
7.2. Data source
8. Mapping and variant calling on yeast transcriptome
8.1. Boot up a Jetstream
8.2. Install software
8.3. Change to a new working directory and map data
8.4. Map data
8.5. Visualize mapping
8.6. Call variants!
8.7. Discussion points / extra things to cover
9. RNAseq
9.1. Boot up a Jetstream
9.2. Install software
9.3. Make a new working directory and link the original data
9.4. Download the yeast reference transcriptome:
9.5. Index the yeast transcriptome:
9.6. Run salmon on all the samples:
9.7. Collect all of the sample counts using this Python script:
9.8. Run edgeR (in R) using this script and take a look at the output:
9.9. Questions to ask/address
9.10. More reading
10. Transcriptome assembly - some basics
10.1. Boot up a Jetstream
10.2. Install the TRINITY assembler
10.3. Change to a new working directory and link the original data
10.4. Applying Digital Normalization
10.5. Trim off likely erroneous k-mers
11. Genome assembly - challenge exercise.
12. Jetstream: install bioconda on a blank Linux machine.
12.1. Booting a “blank” machine
12.2. What is bioconda?
12.3. What problems does conda (and therefore bioconda) solve?
12.4. Installing conda and enabling bioconda
12.5. Using conda
12.6. Using bioconda
12.7. Bonus: installing RStudio Web on your running Linux box.
13. Exploratory RNAseq data analysis using RMarkdown, GitHub and Binder
13.1. Getting started on Jetstream
13.2. Download the data for today’s tutorial
13.3. Make sure R & RStudio are installed and connect
13.4. Introduction to RMarkdown
13.5. A few step workflow
13.6. Creating a
.Rmd
File
13.7. Anatomy of Rmarkdown file
13.8. Publishing on RPubs
13.9. Amazing Resources for learning Rmarkdown
13.10. Exploratory data analysis with Yeast RNAseq data
13.11. Version Control with Git and GitHub
13.12. Making it all work with Binder
14. ChIP-seq dibsi2018 tutorial
14.1. Story line and background
14.2. What is ChIP-seq?
14.3. Our goal
14.4. Get some sample data
14.5. Setting up the tools
14.6. Let’s do mapping!
14.7. Manipulate SAM output
14.8. Visualization
14.9. Aligning the control sample
14.10. macs2 peak calling
14.11. Use bedtools to to intersect bed files
14.12. Alternatively you can use HOMER
15. Annotating and evaluating a
de novo
transcriptome assembly
15.1. Annotation with dammit
15.2. Evaluation
16. Where to put your code and data for publication
17. The modern paper
18. Raw Data
19. Getting raw data from the SRA
20. Data products
20.1. Public websites for sharing data products
21. Code - GitHub / Zenodo
22. Binder
23. More information
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