diff --git a/presentations/modules/RCy3_primer/module.html b/presentations/modules/RCy3_primer/module.html index 22d2e267..1b51be94 100644 --- a/presentations/modules/RCy3_primer/module.html +++ b/presentations/modules/RCy3_primer/module.html @@ -1,5 +1,5 @@
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Translating biological data into Cytoscape using RCy3

+

Translating biological data into Cytoscape using RCy3 or py4cytoscape

Networks offer us a useful way to represent our biological data. But how do we seamlessly translate our data from R into Cytoscape?

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- - 3.6.0 -
diff --git a/presentations/modules/proteomics/module.html b/presentations/modules/proteomics/module.html index bb7ce52b..c51909a6 100644 --- a/presentations/modules/proteomics/module.html +++ b/presentations/modules/proteomics/module.html @@ -3,7 +3,25 @@

Proteomics

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(Very) Brief Intro to Mass Spectrometry

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Why Study Proteins?

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+ +
+

(Very) Brief Intro to Mass Spectrometry

Caveats:

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

Tandem Mass Spectrometry (MS/MS)

When dealing with proteins or lots of peptides, the m/z ratios might be too close to distinguish. Tandem Mass Spectrometry addresses that by adding an additional fragmentation step

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Tandem Mass Spectrometry (MS/MS)

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

Proteomics

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Proteomics

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(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

Proteomics

How do we get from spectra to peptide?

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Possible problem: peptides might not uniquely identify a single protein.

-

(Very) Brief Intro to Mass Spectrometry

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(Very) Brief Intro to Mass Spectrometry

Proteomics

Protein groups are proteins that share all their peptides with other proteins cannot be unambiguously identified.

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(Very) Brief Intro to Mass Spectrometry

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Resources for learning more: -

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- -
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Understanding Proteomics Results

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Understanding Proteomics Results

MaxQuant proteinGroups.txt

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MaxQuant proteinGroups.txt

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Understanding Proteomics Results

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Understanding Proteomics Results

MaxQuant Columns

The most important columns that every proteinGroups.txt file contains are: @@ -119,19 +123,71 @@

MaxQuant Columns

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Analyzing Proteomics Data

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Understanding Proteomics Results

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Overview of analytical pipeline

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+ +
+ https://doi.org/10.3389/fmed.2021.747333 +
+ +
+

Understanding Proteomics Results

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Example dataset after identification and quantitation

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+ +
-

Analyzing Abundance Data

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Analyzing Proteomics Data

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R packages for proteomics analysis

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Analyzing PTM Data

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+

Peptide identification must account for the masses of potential post-translational modifications (PTMs)

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+ +
+ +

Analyzing AP/MS or BioID Data

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AP/MS and BioID results are a list of interactions

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+ +
+ https://doi.org/10.1038/nature10719 +
+ +
+

Proteomics

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Resources for learning more: +

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+ + +
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Questions?

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Functional Enrichment Analysis

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Example Data

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This tour can be used with a set of example data, including a dataset originally from TCGA, and a published dataset from Voineagu et al., 2011.

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For the TCGA data, it was processed as follows:

+ + +
+

Example Data - TCGA lung cancer study

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This tour can be used with your own data, or with an example dataset. Three example datasets are provided in the following slides.

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The first dataset is from the TCGA lung cancer study, and includes a comparison of the expression of transcripts in lung cancer biopses versus normal tissue. The data was processed to produce the example files:

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    +
  1. Calculate log2 fold change and adjusted pvalue for cancer vs normal per gene from group average expression values.
    2. +
  2. Filter for significantly up-regulated genes: log2 fold change => 2 AND adjusted pvalue <= 0.05; list of 367 up-regulated genes (NCBI gene symbol). Download data.
    3. +
  3. Filter for significantly down-regulated genes: log2 fold change <= -2 AND adjusted pvalue <= 0.05; list of 516 down-regulated genes (NCBI gene symbol). Download data.
    4. +
  4. Gene ranking calculated for all genes using sign(log2 fold change) * -log10(pvalue); file with Ensembl gene id and rank value for all genes. Download data.
    5. +
+

These input files can be used for ORA and GSEA analysis in Enrichr and WebGestalt.

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+ + + +
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Example Data - Pinto et al. SARS-CoV-2 study

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The Pinto et al study is a multi-OMICs study of SARS-CoV-2 host responses in lung epithelial cells. The data files below were adapted from the supplementary data files provided with the publication, which were already pre-filtered.

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    +
  1. Differentially regulated transcripts identified in cells infected with SARS-CoV2; list of 2656 genes (NCBI gene symbol). Download data.
    2. +
  2. Differentially expressed proteins identified in cells infected with SARS-CoV2; list of 225 proteins. Download data with Uniprot identifiers / Download data with NCBI gene symbols.
    3. +
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These input files can be used for ORA analysis in Enrichr and WebGestalt.

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Example Data

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Finally, the Voineagu et al. data was downloaded from Expression Atlas and contains data for all genes measured with the following comma-separated columns:

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Example Data - Voineagu et al. autism study

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The Voineagu et al. study compares the transcriptome between autistic and normal brain, and was downloaded from Expression Atlas. It contains data for all genes measured with the following comma-separated columns:

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    +
  • NCBI gene symbol
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  • fold change
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  • p value
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Download data.

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This input is used for ORA and GSEA analysis in the Interactive Enrichment Analysis tool.

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Enrichr: Gene Set Libraries

Enrichr: Start Analysis

Analysis is started by simply copying the list of genes from an input text file into the input box on the right of the Analyze tab and clicking Submit.

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Note that this corresponds to the example data file containing up-regulated genes.

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Note that this corresponds to the example TCGA example data file containing up-regulated genes.

+ @@ -223,7 +299,7 @@

WebGestalt: Start Analysis

WebGestalt: Start Analysis

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The input data is defined in the Gene List section, and the dataset or list of genes can either be pasted into the input field or uploaded as a text file. Select Gene ID Type should match the identifier type in the input data. The list used here corresponds to the example input file containing the full dataset with associated gene rank.

+

The input data is defined in the Gene List section, and the dataset or list of genes can either be pasted into the input field or uploaded as a text file. Select Gene ID Type should match the identifier type in the input data. The list used here corresponds to the TCGA example input file containing the full dataset with associated gene rank.

Clicking Submit at the bottom left starts the analysis.