Further learning

On-demand learning, including online tutorials and recorded webinars covering many topics in bioinformatics and EMBL-EBI resources.

Resources shared by our trainers:

Useful applications:

• Cytoscape https://cytoscape.org/  Network visualization
• DyNet: network comparison https://apps.cytoscape.org/apps/dynet
• ClusterMaker2: Clustering the network using various clustering algorithms including Girvan-Newman algorithm. https://apps.cytoscape.org/apps/clustermaker2
• BinGO: Gene Ontology and other enrichment application https://apps.cytoscape.org/apps/bingoNetwork databases:
• OmniPath protein-protein interaction database: https://omnipathdb.org/ Large secondary protein interaction network database with inter and intracellular networks
• Reactome: https://reactome.org/ Reaction based pathway and interaction network
• SignaLink3: http://signalink.org/ Signaling pathway database with common model organisms such as Drosophila, Caenorhabditis elegans, Danio rario and human
• STRING: https://string-db.org/ Large protein interaction and association database Lots of homology based interaction predictions for many different species
• DoRothEA database: https://saezlab.github.io/dorothea/ Transcription factor target database. It works ell the tools mentioned above by Aurelien
• TFlink database: https://tflink.net/ Large transcription factor target databaseGene expression databases for your work to download public data:
• Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ Here you can find most of the gene expression data, but not completely curated
• Array Express: https://www.ebi.ac.uk/biostudies/arrayexpress  GEO’s European counterpart, curated data
• Broad Institute single cell portal: https://singlecell.broadinstitute.org/single_cell Go to for single cell data
• Single cell RNA -seq atlas: https://www.ebi.ac.uk/gxa/sc/homeUseful data visualization packages and tutorials for enrichment:
• Clusterprofiler: https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html You can change the background easier then in EnrichR and it gives easy costum enrichmnet analysis
• Revigo: https://bioconductor.org/packages/release/bioc/html/rrvgo.html https://pubmed.ncbi.nlm.nih.gov/21789182/ It makes tree and semantic similarity based gene ontology enrichment figures.
• ID mapping:  Uniprot.org https://www.ensembl.org/info/data/biomart/index.html. In R use the AnnotationDbi package with your species specific package (https://bioconductor.org/packages/release/bioc/html/AnnotationDbi.html ). In human this is: org.Hs.eg.db https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html.
• A few reading materials:
  • Stastics from descriptive statistics till machine learning and linear regression https://www.nature.com/collections/qghhqm/pointsofsignificance
  • Points of view data visualization: https://protocolsmethods.springernature.com/posts/43650-data-visualization-a-view-of-every-points-of-view-column
  • Barabási: Network Science Check chapter 9 for community detection for description of the algorithms. http://networksciencebook.com/

• decoupleR (in R: https://saezlab.github.io/decoupleR/ and python: https://decoupler-py.readthedocs.io/en/latest/) for TF, kinase and pathway enrichment analysis in general. For single cell, spatial and bulk RNA data, and phosphoproteomic data.
• LIANA (https://saezlab.github.io/liana/) for cell-cell communication and ligand receptor interactions. Pulls together in one place many different cell cell communication methods such as cellphoneDB and cellChat, compare them and allow to generate consensus results across multiple methods. For single cell RNA data.
  • Also check this chapter in the single cell best practice book: https://www.sc-best-practices.org/mechanisms/cell_cell_communication.html
• COSMOS (https://saezlab.github.io/cosmosR/) for multi-omic integration with mechanistic prior knowledge networks, to generate mechanistic hypotheses connecting multiple types of omic data. For RNA, phospho, proteomic and metabolomic data mainly.
• Misty (https://saezlab.github.io/mistyR/) for analysis of spatial multi-omic data. For RNA, proteomic and metabolomic SPATIAL data.
• MOFA2 (https://biofam.github.io/MOFA2/) for multi-omic factor analysis. To integrate and reduce dimensionality of multi-omic datasets, as well as exploring correlation between multi-omic factors with metadata (clinical,etc…).

Few recommended on-demand training materials:

• Wenbinars –
  • Methods in building and analysing biological networks
  • Identifying tumor cells at the single cell level through machine learning
• Online tutorial –
  • Network analysis of protein interaction data
• Course materials –
  • Systems biology: From large datasets to biological insight

Live training, including other virtual courses and live webinars.

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