Galaxy | ProteoRE | Published Workflow | Tutorial_ProteoRE_Biomarkers_Candidate_Identification

Tutorial_ProteoRE_Biomarkers_Candidate_Identification

Step	Annotation
Step 1: Build tissue-specific expression dataset Experimental data source (antibody- or RNAseq-based) RNA levels based on RNA-seq experiments HPA source files version /home/proteore/galaxy/tool-data/protein_atlas/HPA_RNA_tissue_17-06-2021.tsv Select tissue (by clicking in the box) heart muscle
Step 2: Build tissue-specific expression dataset Experimental data source (antibody- or RNAseq-based) Expression profiles based on immunohistochemistry HPA source files version /home/proteore/galaxy/tool-data/protein_atlas/HPA_normal_tissue_17-06-2021.tsv Select tissue (by clicking in the box) heart muscle Expression level High Medium Reliability score Enhanced Supported
Step 3: Filter by keywords and/or numerical value Input file Output dataset 'output' from step 1 Does file contain header? True Operation Discard Select an operator to combine your filters (if more than one) OR Filter by keywords Filter by numerical values Filter by numerical value 1 Column number on which to apply the filter c4 Select operator <= Value 10.0 Filter by range of numerical values Sort by column ? False
Step 4: Venn diagram List to compares List to compare 1 Enter your list Input file containing your list Select your file Output dataset 'kept_lines' from step 3 Does file contain header? True Column number on which apply the comparison c1 Enter the name of this list Heart RNAseq List to compare 2 Enter your list Input file containing your list Select your file Output dataset 'output' from step 2 Does file contain header? True Column number on which apply the comparison c1 Enter the name of this list Heart IHC
Step 5: Cut File to cut Output dataset 'output_text' from step 4 Operation Keep Delimited by Tab Cut by fields List of Fields 3
Step 6: Filter by keywords and/or numerical value Input file Output dataset 'output' from step 5 Does file contain header? True Operation Discard Select an operator to combine your filters (if more than one) OR Filter by keywords Filter by keywords 1 Column number on which to apply the filter c1 Search for exact match? True Enter keywords copy/paste Copy/paste keywords to find (keep or discard) NA Filter by numerical values Filter by range of numerical values Sort by column ? False
Step 7: Add expression data HPA source file version /home/proteore/galaxy/tool-data/protein_atlas/HPA_full_atlas_17-06-2021.tsv Enter your IDs (Ensembl gene IDs only, e.g. ENSG00000064787) Input file containing your IDs Select your file Output dataset 'kept_lines' from step 6 Column IDs (e.g : Enter c1 if ENSG ID are in column n°1) c1 Does file contain header? True RNAseq/Ab-based expression data: Select information to add to your list Gene name Gene description RNA tissue category RNA non-specific tissue abundance in 'Transcript Per Million' (only for 23/10/2018 release)
Step 8: Filter by keywords and/or numerical value Input file Output dataset 'output' from step 7 Does file contain header? True Operation Keep Select an operator to combine your filters (if more than one) OR Filter by keywords Filter by keywords 1 Column number on which to apply the filter c4 Search for exact match? False Enter keywords copy/paste Copy/paste keywords to find (keep or discard) enriched enhanced Filter by numerical values Filter by range of numerical values Sort by column ? False
Step 9: ID Converter Enter IDs Input file containing IDs Select your file Output dataset 'kept_lines' from step 8 Does file contain header? True Column number of IDs to map c1 Species Human (Homo sapiens) Release /home/proteore/galaxy/tool-data/id_mapping/Human_id_mapping_16-06-2021.tsv Type/source of IDs Ensembl gene ID (e.g. ENSG00000166913) Target type: Target type of IDs you would like to map to UniProt accession number (e.g. P31946) - reviewed entries only
Step 10: Add protein features Enter your IDs (neXtProt or UniProt; e.g. NX_P05090 or P05090) Input file containing your IDs Select your file Output dataset 'output' from step 9 Column IDs (e.g : Enter c1 for column n°1) c5 Does input file have header? True Type of IDs UniProt accession number Features/Annotation Number of transmembrane domains Subcellular Location Disease information Release of neXtProt reference file to use tool-data/nextprot_ref_31-07-2020.tsv
Step 11: Filter by keywords and/or numerical value Input file Output dataset 'output' from step 10 Does file contain header? True Operation Keep Select an operator to combine your filters (if more than one) AND Filter by keywords Filter by keywords 1 Column number on which to apply the filter c7 Search for exact match? False Enter keywords copy/paste Copy/paste keywords to find (keep or discard) cytoplasm cytosol Filter by numerical values Filter by numerical value 1 Column number on which to apply the filter c6 Select operator = Value 0.0 Filter by range of numerical values Sort by column ? False
Step 12: Get MS/MS observations in tissue/fluid Enter your IDs (UniProt Accession number only) Input file containing your IDs Select your file Output dataset 'kept_lines' from step 11 Does file contain header? True Column of IDs c5 Proteomics dataset (biological sample) tool-data/Human_Heart_2014-08.tsv tool-data/Human_Plasma_non_glyco_2017-04.tsv
Step 13: Filter by keywords and/or numerical value Input file Output dataset 'output' from step 12 Does file contain header? True Operation Discard Select an operator to combine your filters (if more than one) OR Filter by keywords Filter by keywords 1 Column number on which to apply the filter c10 Search for exact match? True Enter keywords copy/paste Copy/paste keywords to find (keep or discard) NA Filter by numerical values Filter by range of numerical values Sort by column ? False

About this Workflow

Author

proteore

Related Workflows

All published workflows
Published workflows by proteore

Rating

Community
(1 rating, 5.0 average)

Tags

Community: