Register training material
5 materials found

Related resources: ARROWSMITH  or Proteomics dataset 


Text-mining exercises

Hands-on exercises using a variety of text-mining tools and databases based on text mining, to interpret the results from microbiome studies.

Scientific topics: Data mining, Natural language processing, Metagenomics, Microbial ecology

Text-mining exercises https://tess.elixir-europe.org/materials/text-mining-exercises Hands-on exercises using a variety of text-mining tools and databases based on text mining, to interpret the results from microbiome studies. Manimozhiyan Arumugam Data mining Natural language processing Metagenomics Microbial ecology Bioinformaticians Biologists
Proteomics - Peptide and Protein ID using OpenMS tools

Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to convert LC-MS/MS raw files? - How to identify peptides? - How to identify proteins? - How to evaluate the results? Objectives of the tutorial: - Protein identification from LC-MS/MS raw files.

Resource type: Tutorial

Proteomics - Peptide and Protein ID using OpenMS tools https://tess.elixir-europe.org/materials/proteomics-peptide-and-protein-id-using-openms-tools Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to convert LC-MS/MS raw files? - How to identify peptides? - How to identify proteins? - How to evaluate the results? Objectives of the tutorial: - Protein identification from LC-MS/MS raw files.
Proteomics - Peptide and Protein Quantification via Stable Isotope Labelling (SIL)

Training material for proteomics workflows in Galaxy Questions of the tutorial: - What are MS1 features? - How to quantify based on MS1 features? - How to map MS1 features to MS2 identifications? - How to evaluate and optimize the results? Objectives of the tutorial: - MS1 feature...

Resource type: Tutorial

Proteomics - Peptide and Protein Quantification via Stable Isotope Labelling (SIL) https://tess.elixir-europe.org/materials/proteomics-peptide-and-protein-quantification-via-stable-isotope-labelling-sil Training material for proteomics workflows in Galaxy Questions of the tutorial: - What are MS1 features? - How to quantify based on MS1 features? - How to map MS1 features to MS2 identifications? - How to evaluate and optimize the results? Objectives of the tutorial: - MS1 feature quantitation and mapping of quantitations to peptide and protein IDs.
Proteomics - Peptide and Protein ID using SearchGUI and PeptideShaker

Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to convert LC-MS/MS raw files? - How to identify peptides? - How to identify proteins? - How to evaluate the results? Objectives of the tutorial: - Protein identification from LC-MS/MS raw files.

Resource type: Tutorial

Proteomics - Peptide and Protein ID using SearchGUI and PeptideShaker https://tess.elixir-europe.org/materials/proteomics-peptide-and-protein-id Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to convert LC-MS/MS raw files? - How to identify peptides? - How to identify proteins? - How to evaluate the results? Objectives of the tutorial: - Protein identification from LC-MS/MS raw files.
Proteomics - Secretome Prediction

Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to predict cellular protein localization based upon GO-terms? - How to combine multiple localization predictions? Objectives of the tutorial: - Predict proteins in the cellular secretome by using...

Resource type: Tutorial

Proteomics - Secretome Prediction https://tess.elixir-europe.org/materials/proteomics-secretome-prediction Training material for proteomics workflows in Galaxy Questions of the tutorial: - How to predict cellular protein localization based upon GO-terms? - How to combine multiple localization predictions? Objectives of the tutorial: - Predict proteins in the cellular secretome by using GO-terms. - Predict proteins in the cellular secretome by using WolfPSORT. - Combine the results of both predictions.