Please note: This instance is for testing/development, and any content submitted may be changed or deleted without warning.
Training eSupport System
  • Log In
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Trainers
    • Providers
    • Nodes

TeSS makes use of some necessary cookies to provide its core functionality.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Scientific topic
    • Bayesian methods2
    • Biostatistics2
    • Bottom-up proteomics2
    • Descriptive statistics2
    • Discovery proteomics2
    • Gaussian processes2
    • Inferential statistics2
    • MS-based targeted proteomics2
    • MS-based untargeted proteomics2
    • Markov processes2
    • Metaproteomics2
    • Multivariate statistics2
    • Peptide identification2
    • Probabilistic graphical model2
    • Probability2
    • Protein and peptide identification2
    • Proteomics2
    • Quantitative proteomics2
    • Statistics2
    • Statistics and probability2
    • Targeted proteomics2
    • Top-down proteomics2
    • DNA variation1
    • Genetic variation1
    • Genomic variation1
    • Mutation1
    • Polymorphism1
    • Somatic mutations1
    • Show N_FILTERS more
    • Tool
    • Galaxy7
    • BWA3
    • MultiQC2
    • SAMtools2
    • SRA Software Toolkit2
    • fastp2
    • lofreq2
    • snpEff2
    • Bwa-mem21
    • DropletUtils1
    • PubMed1
    • STAR1
    • Show N_FILTERS more
    • Content provider
    • Galaxy Training9
    • Show N_FILTERS more
    • Keyword
    • Galaxy Server administration2
    • Proteomics2
    • Statistics and machine learning2
    • 10x1
    • Introduction to Galaxy Analyses1
    • ML1
    • Machine learning1
    • Pan-cancer1
    • Single Cell1
    • Variant Analysis1
    • cancer1
    • cancer biomarkers1
    • covid191
    • interactive-tools1
    • oncogenes and tumor suppressor genes1
    • one-health1
    • virology1
    • Show N_FILTERS more
    • Difficulty level
    • Beginner7
    • Intermediate2
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International9
    • Show N_FILTERS more
    • Target audience
    • Students7
    • Galaxy Administrators2
    • Show N_FILTERS more
    • Author
    • Daniel Blankenberg
    • Helena Rasche102
    • Bérénice Batut67
    • Saskia Hiltemann48
    • Björn Grüning34
    • Anthony Bretaudeau23
    • Anton Nekrutenko22
    • The Carpentries22
    • Simon Gladman20
    • Nicola Soranzo19
    • Bazante Sanders18
    • Nate Coraor18
    • Wendi Bacon17
    • Yvan Le Bras17
    • Fotis E. Psomopoulos16
    • Katarzyna Kamieniecka16
    • Krzysztof Poterlowicz16
    • Mehmet Tekman15
    • Anne Fouilloux14
    • Marie Josse14
    • Subina Mehta14
    • Donny Vrins13
    • Julia Jakiela12
    • Maria Doyle12
    • Simon Bray12
    • Wolfgang Maier11
    • Cristóbal Gallardo10
    • Dave Clements10
    • Khaled Jum'ah9
    • Marius van den Beek9
    • Pavankumar Videm9
    • Pratik Jagtap9
    • Anika Erxleben8
    • Avans Hogeschool8
    • Delphine Lariviere8
    • John Chilton8
    • Lucille Delisle8
    • Alex Ostrovsky7
    • Alexandre Cormier7
    • Anne Pajon7
    • Florian Christoph Sigloch7
    • Joachim Wolff7
    • Laura Cooper7
    • Laura Leroi7
    • Martin Čech7
    • Robert Andrews7
    • Stéphanie Robin7
    • Andrew Mason6
    • Anna Syme6
    • Anup Kumar6
    • Branka Franicevic6
    • Christopher Barnett6
    • Coline Royaux6
    • Erwan Corre6
    • Kaivan Kamali6
    • Melanie Föll6
    • Michael Charleston6
    • Paul Zierep6
    • Philippe Rocca-Serra6
    • Sara Morsy6
    • Timothy J. Griffin6
    • Xenia Perez Sitja6
    • Allegra Via5
    • Clemens Blank5
    • Dechen Bhuming5
    • ELIXIR Goblet Train the Trainers5
    • James Johnson5
    • John Davis5
    • Katherine Do5
    • Kellie Snow5
    • Korneel Hens5
    • Mira Kuntz5
    • Munazah Andrabi5
    • Nick Juty5
    • Patricia Palagi5
    • Praveen Kumar5
    • Ray Sajulga5
    • Saskia Lawson-Tovey5
    • Beatriz Serrano-Solano4
    • Emma Leith4
    • Enis Afgan4
    • Florian Heyl4
    • Mallory Freeberg4
    • Mateo Boudet4
    • Morgan Howells4
    • Nadia Goué4
    • Vivek Bhardwaj4
    • Belinda Phipson3
    • Cyril Monjeaud3
    • Engy Nasr3
    • Fidel Ramirez3
    • Gianmauro Cuccuru3
    • Gildas Le Corguillé3
    • Hans-Rudolf Hotz3
    • Laila Los3
    • Leonid Kostrykin3
    • Maria Christina Maniou3
    • Matthias Fahrner3
    • Mo Heydarian3
    • Mélanie Petera3
    • Show N_FILTERS more
    • Contributor
    • Helena Rasche
    • Saskia Hiltemann9
    • Björn Grüning8
    • Nicola Soranzo4
    • Bérénice Batut3
    • Martin Čech3
    • Anton Nekrutenko2
    • Armin Dadras2
    • Jayadev Joshi2
    • Mehmet Tekman2
    • Melanie Föll2
    • Nate Coraor2
    • Subina Mehta2
    • Anup Kumar1
    • Cristóbal Gallardo1
    • Donny Vrins1
    • Gianmauro Cuccuru1
    • Hans-Rudolf Hotz1
    • John Davis1
    • Mélanie Petera1
    • Niall Beard1
    • Pavankumar Videm1
    • Teresa Müller1
    • Vijay1
    • Wendi Bacon1
    • William Durand1
    • Wolfgang Maier1
    • dlal-group1
    • Show N_FILTERS more
    • Resource type
    • e-learning7
    • slides2
    • Show N_FILTERS more
    • Related resource
    • Associated Workflows7
    • Associated Training Datasets5
    • Quarto/RMarkdown Notebook1
    • Show N_FILTERS more
  • Show disabled materials
  • Show archived materials
    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month

Training materials

  • Subscribe via email

Email Subscription

Register training material

Authors: Daniel Blankenberg

and Contributors: Helena Rasche

9 materials found
  • e-learning

    PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis

    • beginner
    Statistics and probability Machine learning Pan-cancer Statistics and machine learning cancer biomarkers oncogenes and tumor suppressor genes
  • e-learning

    Text-mining with the SimText toolset

    • beginner
    Statistics and probability Statistics and machine learning interactive-tools
  • e-learning

    Pre-processing of 10X Single-Cell RNA Datasets

    • beginner
    10x Single Cell
  • e-learning

    Machine Learning Modeling of Anticancer Peptides

    •• intermediate
    Proteomics ML cancer
  • e-learning

    From NCBI's Sequence Read Archive (SRA) to Galaxy: SARS-CoV-2 variant analysis

    • beginner
    Genetic variation Variant Analysis covid19 one-health virology
  • e-learning

    NGS data logistics

    • beginner
    Introduction to Galaxy Analyses
  • slides

    Reference Genomes in Galaxy

    • beginner
    Galaxy Server administration
  • slides

    Gearing towards production

    • beginner
    Galaxy Server administration
  • e-learning

    Peptide Library Data Analysis

    •• intermediate
    Proteomics
Training eSupport System
contact@example.com
Contribute
About TeSS
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.0
Source code
API documentation
Bioschemas testing tool

TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.