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RNA-seq data analysis practical

[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)

This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.

We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:

For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).

The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:

DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.

Table of contents

  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:
      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data
    2. Exon-centric analyses:

Software requirements

Note: depending on the topics covered in the course some of these tools might not be used.

Other resources

Course data

Tutorials

Cheat sheets

Aknowledgments

This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.

About

RNA-seq data analysis practical

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2 stars

Watchers

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
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Stars
2 stars
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6 watching
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3 forks
Report repository
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, 'i');
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
RNA-seq data analysis practical
[Some slides to help with the practical] (https://drive.google.com/open?id=0B8OqXE2Rr7OHQVZQN1BXQ0RMOGM)
This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. We will also use one meta-pipeline [IRAP] (https://github.com/nunofonseca/irap). Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.
We will be working with a subset of a publicly available dataset from Drosophila melanogaster, which is available:
For more information about this dataset please refer to the original publication [(Brooks et al. (2010)] (http://genome.cshlp.org/content/early/2010/10/04/gr.108662.110).
The tools and R packages that we will be using during the practical are listed below (see Software requirements) and the necessary data files can be found here. After dowloading and uncompressing the tar.gz file, you should have the following directory structure in your computer:
DATA # data used for the practicals
|-- demultiplexing # multiplexed data !!! Not used in the project - FYI only
|-- eqtl # data used in the eqtl practical !!! Not used in the project - FYI only
|-- fastq # fastq files -> starting point
|-- mapped # mapped data: BAM files
|-- QCreports # precomputed QC report
|-- reference # reference from release 62 of Ensembl
`-- IRAP_example # Directory setup for IRAP (raw_data +reference) + its output
|-- data
| |-- contamination # E.coli reference
| |-- raw_data # fastq files
| |-- reference
| | |--drosophila_melanogaster # All the reference files are in this directory
`-- E-GEOD-18508 # output of IRAP
| ...

You can also browse the files online and download only the needed material from here
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.
Table of contents
  1. Dealing with raw data
    1. The FASTQ format
    2. Quality assessment (QA)
    3. Filtering FASTQ files
    4. Aligning reads to the genome (already processed - will not be run)
  2. Dealing with aligned data
    1. The SAM/BAM format
    2. Visualising aligned reads (optional)
    3. Filtering BAM files
    4. Gene-centric analyses:

      1. Counting reads overlapping annotated genes
        • With htseq-count
        • With R
        • Alternative approaches
      2. Normalising counts
        • With RPKMs
        • With DESeq2
      3. Differential gene expression
  3. Other topics - Not covered in the course
    1. Dealing with raw data

    2. Exon-centric analyses:

Software requirements
Note: depending on the topics covered in the course some of these tools might not be used.
Other resources
Course data
Tutorials
Cheat sheets
Aknowledgments
This tutorial has been inspired on material developed by Mar Gonzalez-Porta, Liliana Greger, Nuno Fonseca, Claudia Calabrese, Fatemeh Zamanzad, Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who we would like to thank and acknowledge.
About
RNA-seq data analysis practical
Resources
Readme
Activity
Custom properties
Stars
2 stars
Watchers
6 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors