Gigascience_Indels_SAV_non-normal_demonstration_STS26T-Gent_Workflow

proteomics-neoantigen-2-non-normal-database-generation/main-workflow

Author(s)
GalaxyP
version Version
1
last_modification Last updated
Jan 14, 2025
license License
CC-BY-4.0
galaxy-tags Tags
name:neoantigen

Features
Tutorial
hands_on Neoantigen 2: Non-normal-Database-Generation

Workflow Testing
Tests: ❌
Results: Not yet automated
FAIRness purl PURL
https://gxy.io/GTN:
RO-Crate logo with flask Download Workflow RO-Crate
Launch in Tutorial Mode question
galaxy-download Download
flowchart TD
  0["ℹ️ Input Dataset\nHomo_sapiens.GRCh38_canon.106.gtf"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nRNA-Seq_Reads_2.fastq"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nRNA-Seq_Reads_1.fastq"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["ℹ️ Input Dataset\nHUMAN-Uniprot-and-isoforms_and_cRAP-FASTA-Database"];
  style 3 stroke:#2c3143,stroke-width:4px;
  4["Uncompressed_RNA_Seq_Reads_2"];
  1 -->|output| 4;
  5983317a-db25-4f0f-8a0b-290d15aafd14["Output\nUncompressed_RNA_Seq_Reads_2"];
  4 --> 5983317a-db25-4f0f-8a0b-290d15aafd14;
  style 5983317a-db25-4f0f-8a0b-290d15aafd14 stroke:#2c3143,stroke-width:4px;
  5["Uncompressed_RNA_Seq_Reads_1"];
  2 -->|output| 5;
  d6f2044c-cd3b-4fde-901a-2c6c7db5db4f["Output\nUncompressed_RNA_Seq_Reads_1"];
  5 --> d6f2044c-cd3b-4fde-901a-2c6c7db5db4f;
  style d6f2044c-cd3b-4fde-901a-2c6c7db5db4f stroke:#2c3143,stroke-width:4px;
  6["Convert_HUMAN_Uniprot_and_CRAP_FASTA_to_tabular"];
  3 -->|output| 6;
  067e8f48-59d2-4f9c-bce9-13d1dca9d74e["Output\nHUMAN_Uniprot_and_CRAP.tabular"];
  6 --> 067e8f48-59d2-4f9c-bce9-13d1dca9d74e;
  style 067e8f48-59d2-4f9c-bce9-13d1dca9d74e stroke:#2c3143,stroke-width:4px;
  7["HISAT2_Alignment_BAM"];
  5 -->|output1| 7;
  4 -->|output1| 7;
  0137dcb5-9d02-427f-82da-995723a93c85["Output\nHISAT2_Alignment_BAM"];
  7 --> 0137dcb5-9d02-427f-82da-995723a93c85;
  style 0137dcb5-9d02-427f-82da-995723a93c85 stroke:#2c3143,stroke-width:4px;
  8["Filtering_HUMAN_Uniprot_and_cRAP_Accessions_tabular"];
  6 -->|output| 8;
  4a2dbdd0-8e12-418c-a8f9-38256a760be8["Output\nFiltering_HUMAN_Uniprot_and_cRAP_Accessions_tabular"];
  8 --> 4a2dbdd0-8e12-418c-a8f9-38256a760be8;
  style 4a2dbdd0-8e12-418c-a8f9-38256a760be8 stroke:#2c3143,stroke-width:4px;
  9["StringTie_Alignment_GTF"];
  0 -->|output| 9;
  7 -->|output_alignments| 9;
  a4ff5eba-c73a-424b-8a9e-cfe76a0fe77f["Output\nStringTie_Alignment_GTF"];
  9 --> a4ff5eba-c73a-424b-8a9e-cfe76a0fe77f;
  style a4ff5eba-c73a-424b-8a9e-cfe76a0fe77f stroke:#2c3143,stroke-width:4px;
  10["FreeBayes_variants_VCF"];
  7 -->|output_alignments| 10;
  b4ebb058-327d-4284-b96e-f7491262912e["Output\nFreeBayes_variants_VCF"];
  10 --> b4ebb058-327d-4284-b96e-f7491262912e;
  style b4ebb058-327d-4284-b96e-f7491262912e stroke:#2c3143,stroke-width:4px;
  11["GffCompare_Annotated_Transcripts_GTF"];
  0 -->|output| 11;
  9 -->|output_gtf| 11;
  72c73a0b-6677-4701-ab52-4d296c553df9["Output\nGffCompare_Annotated_Transcripts_GTF"];
  11 --> 72c73a0b-6677-4701-ab52-4d296c553df9;
  style 72c73a0b-6677-4701-ab52-4d296c553df9 stroke:#2c3143,stroke-width:4px;
  12["CustomProDB_protein_FASTAs_from_BAM_and_VCF"];
  7 -->|output_alignments| 12;
  10 -->|output_vcf| 12;
  8ce883fd-1961-47f6-b7b0-8153b7d1b4be["Output\nCustomProDB_INDEL_FASTA"];
  12 --> 8ce883fd-1961-47f6-b7b0-8153b7d1b4be;
  style 8ce883fd-1961-47f6-b7b0-8153b7d1b4be stroke:#2c3143,stroke-width:4px;
  b31fbd26-fec0-42d9-a079-a28ae64ec009["Output\nCustomProDB_VARIANT_ANNOTATION_RDATA"];
  12 --> b31fbd26-fec0-42d9-a079-a28ae64ec009;
  style b31fbd26-fec0-42d9-a079-a28ae64ec009 stroke:#2c3143,stroke-width:4px;
  b7944440-cbca-4fb5-96fd-03e952854728["Output\nCustomProDB_Genomic_SQLlite"];
  12 --> b7944440-cbca-4fb5-96fd-03e952854728;
  style b7944440-cbca-4fb5-96fd-03e952854728 stroke:#2c3143,stroke-width:4px;
  9cee39b1-2766-43b0-b002-697e496a11d7["Output\nCustomProDB_VARIANT_ANNOTATION_SQLite"];
  12 --> 9cee39b1-2766-43b0-b002-697e496a11d7;
  style 9cee39b1-2766-43b0-b002-697e496a11d7 stroke:#2c3143,stroke-width:4px;
  8258d64c-c755-4b49-b417-32ea722773bd["Output\nCustomProDB_RPKM_FASTA"];
  12 --> 8258d64c-c755-4b49-b417-32ea722773bd;
  style 8258d64c-c755-4b49-b417-32ea722773bd stroke:#2c3143,stroke-width:4px;
  9ce1b014-b001-4ed8-9144-5890bb8cdbd5["Output\nCustomProDB_SNV_FASTA"];
  12 --> 9ce1b014-b001-4ed8-9144-5890bb8cdbd5;
  style 9ce1b014-b001-4ed8-9144-5890bb8cdbd5 stroke:#2c3143,stroke-width:4px;
  13["GffCompare_Annotated_GTF_to_BED"];
  11 -->|transcripts_annotated| 13;
  751c3898-45a0-4836-bd33-44affbe9748a["Output\nGffCompare_Annotated_GTF_to_BED"];
  13 --> 751c3898-45a0-4836-bd33-44affbe9748a;
  style 751c3898-45a0-4836-bd33-44affbe9748a stroke:#2c3143,stroke-width:4px;
  14["Convert_INDEL_FASTA_to_tabular"];
  12 -->|output_indel| 14;
  dc1bb9c3-0f77-4f30-a0ea-d164563269d8["Output\nCustomProDB_INDEL.tabular"];
  14 --> dc1bb9c3-0f77-4f30-a0ea-d164563269d8;
  style dc1bb9c3-0f77-4f30-a0ea-d164563269d8 stroke:#2c3143,stroke-width:4px;
  15["Convert-SNV_FASTA_to_tabular"];
  12 -->|output_snv| 15;
  0d8682f5-66fa-40db-b280-d6f1bb4a5717["Output\nCustomProDB_SNV.tabular"];
  15 --> 0d8682f5-66fa-40db-b280-d6f1bb4a5717;
  style 0d8682f5-66fa-40db-b280-d6f1bb4a5717 stroke:#2c3143,stroke-width:4px;
  16["Convert-RPKM_FASTA_to_tabular"];
  12 -->|output_rpkm| 16;
  88cfd137-3b8e-4c69-b38a-0b9ea8fea7fc["Output\nCustomProDB_RPKM.tabular"];
  16 --> 88cfd137-3b8e-4c69-b38a-0b9ea8fea7fc;
  style 88cfd137-3b8e-4c69-b38a-0b9ea8fea7fc stroke:#2c3143,stroke-width:4px;
  17["Converting_Genomic_SQLite_to_database_mode"];
  12 -->|output_genomic_mapping_sqlite| 17;
  91eaaad0-e93e-434b-b87c-2de0f9c232ef["Output\nConvert_Genomic_SQLite_to_tabular"];
  17 --> 91eaaad0-e93e-434b-b87c-2de0f9c232ef;
  style 91eaaad0-e93e-434b-b87c-2de0f9c232ef stroke:#2c3143,stroke-width:4px;
  18["Converting_CustomProDB_FASTA_to_tabular"];
  12 -->|output_rpkm| 18;
  6bcf6e52-85a8-439e-83cc-50de2cc8749d["Output\nCustomProDB_FASTA_to_tabular"];
  18 --> 6bcf6e52-85a8-439e-83cc-50de2cc8749d;
  style 6bcf6e52-85a8-439e-83cc-50de2cc8749d stroke:#2c3143,stroke-width:4px;
  19["Converting_Variant_SQLite_to_database_mode"];
  12 -->|output_variant_annotation_sqlite| 19;
  7698d1f1-7979-4deb-9ac2-0f1541d905ce["Output\nConvert_Variant_SQLite_to_tabular"];
  19 --> 7698d1f1-7979-4deb-9ac2-0f1541d905ce;
  style 7698d1f1-7979-4deb-9ac2-0f1541d905ce stroke:#2c3143,stroke-width:4px;
  20["Translate_BED_Transcripts"];
  13 -->|output| 20;
  9510c804-d13e-4cd6-b08e-1c670623e296["Output\nTranslation_FASTA"];
  20 --> 9510c804-d13e-4cd6-b08e-1c670623e296;
  style 9510c804-d13e-4cd6-b08e-1c670623e296 stroke:#2c3143,stroke-width:4px;
  4ebcd87a-1526-4c05-914d-6051b996201d["Output\nTranslate_BED_Transcripts"];
  20 --> 4ebcd87a-1526-4c05-914d-6051b996201d;
  style 4ebcd87a-1526-4c05-914d-6051b996201d stroke:#2c3143,stroke-width:4px;
  21["Annotating-INDEL"];
  14 -->|output| 21;
  aff83bc0-c7af-4257-a51c-93dfe90ae6c0["Output\nAnnotating-INDEL"];
  21 --> aff83bc0-c7af-4257-a51c-93dfe90ae6c0;
  style aff83bc0-c7af-4257-a51c-93dfe90ae6c0 stroke:#2c3143,stroke-width:4px;
  22["Annotating-SNV"];
  15 -->|output| 22;
  960e7c54-1a91-4b92-b75c-0da23fe1650a["Output\nAnnotating-SNV"];
  22 --> 960e7c54-1a91-4b92-b75c-0da23fe1650a;
  style 960e7c54-1a91-4b92-b75c-0da23fe1650a stroke:#2c3143,stroke-width:4px;
  23["Annotating-RPKM"];
  16 -->|output| 23;
  ef203c53-9e5b-4120-a508-a0dcbbfaec3b["Output\nAnnotating-RPKM"];
  23 --> ef203c53-9e5b-4120-a508-a0dcbbfaec3b;
  style ef203c53-9e5b-4120-a508-a0dcbbfaec3b stroke:#2c3143,stroke-width:4px;
  24["Annotating_Genomic_SQLite"];
  17 -->|query_results| 24;
  045776fe-4fc9-4da1-b808-4ab674dcced4["Output\nAnnotating_Genomic_SQLite"];
  24 --> 045776fe-4fc9-4da1-b808-4ab674dcced4;
  style 045776fe-4fc9-4da1-b808-4ab674dcced4 stroke:#2c3143,stroke-width:4px;
  25["Filtering_RPKM_accessions"];
  18 -->|output| 25;
  d23ae9b0-374f-4668-831c-c08217161834["Output\nFiltering_RPKM_accessions"];
  25 --> d23ae9b0-374f-4668-831c-c08217161834;
  style d23ae9b0-374f-4668-831c-c08217161834 stroke:#2c3143,stroke-width:4px;
  26["Annotating_Variant_SQLite"];
  19 -->|query_results| 26;
  0238c405-dbcb-40a0-b9ce-6bc091cc01c5["Output\nAnnotating_Variant_SQLite"];
  26 --> 0238c405-dbcb-40a0-b9ce-6bc091cc01c5;
  style 0238c405-dbcb-40a0-b9ce-6bc091cc01c5 stroke:#2c3143,stroke-width:4px;
  27["Convert_Translation_BED_to_tabular_for_protein_map"];
  20 -->|translation_bed| 27;
  873fc597-ac53-4dfa-8f5a-d5532af8886c["Output\nTranslation_tabular_for_protein_map"];
  27 --> 873fc597-ac53-4dfa-8f5a-d5532af8886c;
  style 873fc597-ac53-4dfa-8f5a-d5532af8886c stroke:#2c3143,stroke-width:4px;
  28["Converting_Annotated_Indel_to_FASTA"];
  21 -->|out_file1| 28;
  ae642819-14c2-46d9-a31f-0b142c074a8c["Output\nConverting_Annotated_Indel_to_FASTA"];
  28 --> ae642819-14c2-46d9-a31f-0b142c074a8c;
  style ae642819-14c2-46d9-a31f-0b142c074a8c stroke:#2c3143,stroke-width:4px;
  29["Converting_Annotated_SNV_to_FASTA"];
  22 -->|out_file1| 29;
  49e24e6a-ae3e-463c-915b-65f895a471e4["Output\nConverting_Annotated_SNV_to_FASTA"];
  29 --> 49e24e6a-ae3e-463c-915b-65f895a471e4;
  style 49e24e6a-ae3e-463c-915b-65f895a471e4 stroke:#2c3143,stroke-width:4px;
  30["Converting_Annotated_RPKM_to_FASTA"];
  23 -->|out_file1| 30;
  0969ba6f-4bd8-4464-94cc-c990cf395507["Output\nConverting_Annotated_RPKM_to_FASTA"];
  30 --> 0969ba6f-4bd8-4464-94cc-c990cf395507;
  style 0969ba6f-4bd8-4464-94cc-c990cf395507 stroke:#2c3143,stroke-width:4px;
  31["Concatenate_HUMAN_Crap_protein-accessions"];
  8 -->|output| 31;
  25 -->|output| 31;
  e3f2a023-888a-4d80-b16c-ea87adbc6588["Output\nConcatenate_HUMAN_Crap_protein-accessions"];
  31 --> e3f2a023-888a-4d80-b16c-ea87adbc6588;
  style e3f2a023-888a-4d80-b16c-ea87adbc6588 stroke:#2c3143,stroke-width:4px;
  32["Variant_input_for_MVP"];
  26 -->|out_file1| 32;
  ef638bf1-2cd7-4c4f-acc7-0fc5ce771756["Output\nVariant_input_for_MVP"];
  32 --> ef638bf1-2cd7-4c4f-acc7-0fc5ce771756;
  style ef638bf1-2cd7-4c4f-acc7-0fc5ce771756 stroke:#2c3143,stroke-width:4px;
  33["Concatenate_databases_from_Genomic_SQlite_and_translation_BED_file"];
  24 -->|out_file1| 33;
  27 -->|output| 33;
  77671f49-26c7-4eeb-b38c-62f09933e99b["Output\nConcatenate_databases_from_Genomic_SQlite_and_translation_BED_file"];
  33 --> 77671f49-26c7-4eeb-b38c-62f09933e99b;
  style 77671f49-26c7-4eeb-b38c-62f09933e99b stroke:#2c3143,stroke-width:4px;
  34["Merge_Indel_SNV_RPKM_to_make_Non_normal_CustomProDB_FASTA"];
  30 -->|output| 34;
  29 -->|output| 34;
  28 -->|output| 34;
  81e3ae85-7111-4517-a9fe-64f245654ad4["Output\nNon-normal_CustomProDB_FASTA"];
  34 --> 81e3ae85-7111-4517-a9fe-64f245654ad4;
  style 81e3ae85-7111-4517-a9fe-64f245654ad4 stroke:#2c3143,stroke-width:4px;
  35["Genomic_input_for_MVP"];
  33 -->|out_file1| 35;
  d72ce75b-6ee3-4bcb-b829-f3c85b3c5a65["Output\nGenomic_input_for_MVP"];
  35 --> d72ce75b-6ee3-4bcb-b829-f3c85b3c5a65;
  style d72ce75b-6ee3-4bcb-b829-f3c85b3c5a65 stroke:#2c3143,stroke-width:4px;
  36["Human_cRAP_Non_normal_transcripts_dB generation"];
  3 -->|output| 36;
  34 -->|output| 36;
  20 -->|translation_fasta| 36;
  400380de-2047-4c30-a9e4-320a19489be4["Output\nHuman_cRAP_Non_normal_transcripts_dB generation"];
  36 --> 400380de-2047-4c30-a9e4-320a19489be4;
  style 400380de-2047-4c30-a9e4-320a19489be4 stroke:#2c3143,stroke-width:4px;

Inputs

Input Label
Input dataset Homo_sapiens.GRCh38_canon.106.gtf
Input dataset RNA-Seq_Reads_2.fastq
Input dataset RNA-Seq_Reads_1.fastq
Input dataset HUMAN-Uniprot-and-isoforms_and_cRAP-FASTA-Database

Outputs

From Output Label
CONVERTER_gz_to_uncompressed Convert compressed file to uncompressed. Uncompressed_RNA_Seq_Reads_2
CONVERTER_gz_to_uncompressed Convert compressed file to uncompressed. Uncompressed_RNA_Seq_Reads_1
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 FASTA-to-Tabular Convert_HUMAN_Uniprot_and_CRAP_FASTA_to_tabular
toolshed.g2.bx.psu.edu/repos/iuc/hisat2/hisat2/2.2.1+galaxy1 HISAT2 HISAT2_Alignment_BAM
toolshed.g2.bx.psu.edu/repos/iuc/filter_tabular/filter_tabular/3.3.1 Filter Tabular Filtering_HUMAN_Uniprot_and_cRAP_Accessions_tabular
toolshed.g2.bx.psu.edu/repos/iuc/stringtie/stringtie/2.2.3+galaxy0 StringTie StringTie_Alignment_GTF
toolshed.g2.bx.psu.edu/repos/devteam/freebayes/freebayes/1.3.6+galaxy0 FreeBayes FreeBayes_variants_VCF
toolshed.g2.bx.psu.edu/repos/iuc/gffcompare/gffcompare/0.12.6+galaxy0 GffCompare GffCompare_Annotated_Transcripts_GTF
toolshed.g2.bx.psu.edu/repos/galaxyp/custom_pro_db/custom_pro_db/1.22.0 CustomProDB CustomProDB_protein_FASTAs_from_BAM_and_VCF
toolshed.g2.bx.psu.edu/repos/galaxyp/gffcompare_to_bed/gffcompare_to_bed/0.2.1 Convert gffCompare annotated GTF to BED GffCompare_Annotated_GTF_to_BED
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 FASTA-to-Tabular Convert_INDEL_FASTA_to_tabular
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 FASTA-to-Tabular Convert-SNV_FASTA_to_tabular
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 FASTA-to-Tabular Convert-RPKM_FASTA_to_tabular
toolshed.g2.bx.psu.edu/repos/iuc/sqlite_to_tabular/sqlite_to_tabular/2.0.0 SQLite to tabular Converting_Genomic_SQLite_to_database_mode
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 FASTA-to-Tabular Converting_CustomProDB_FASTA_to_tabular
toolshed.g2.bx.psu.edu/repos/iuc/sqlite_to_tabular/sqlite_to_tabular/2.0.0 SQLite to tabular Converting_Variant_SQLite_to_database_mode
toolshed.g2.bx.psu.edu/repos/galaxyp/translate_bed/translate_bed/0.1.0 Translate BED transcripts Translate_BED_Transcripts
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace Annotating-INDEL
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace Annotating-SNV
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace Annotating-RPKM
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace Annotating_Genomic_SQLite
toolshed.g2.bx.psu.edu/repos/iuc/filter_tabular/filter_tabular/3.3.1 Filter Tabular Filtering_RPKM_accessions
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace Annotating_Variant_SQLite
toolshed.g2.bx.psu.edu/repos/galaxyp/bed_to_protein_map/bed_to_protein_map/0.2.0 bed to protein map Convert_Translation_BED_to_tabular_for_protein_map
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 Tabular-to-FASTA Converting_Annotated_Indel_to_FASTA
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 Tabular-to-FASTA Converting_Annotated_SNV_to_FASTA
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 Tabular-to-FASTA Converting_Annotated_RPKM_to_FASTA
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/9.3+galaxy1 Concatenate datasets Concatenate_HUMAN_Crap_protein-accessions
toolshed.g2.bx.psu.edu/repos/iuc/query_tabular/query_tabular/3.3.2 Query Tabular Variant_input_for_MVP
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/9.3+galaxy1 Concatenate datasets Concatenate_databases_from_Genomic_SQlite_and_translation_BED_file
toolshed.g2.bx.psu.edu/repos/galaxyp/fasta_merge_files_and_filter_unique_sequences/fasta_merge_files_and_filter_unique_sequences/1.2.0 FASTA Merge Files and Filter Unique Sequences Merge_Indel_SNV_RPKM_to_make_Non_normal_CustomProDB_FASTA
toolshed.g2.bx.psu.edu/repos/iuc/query_tabular/query_tabular/3.3.2 Query Tabular Genomic_input_for_MVP
toolshed.g2.bx.psu.edu/repos/galaxyp/fasta_merge_files_and_filter_unique_sequences/fasta_merge_files_and_filter_unique_sequences/1.2.0 FASTA Merge Files and Filter Unique Sequences Human_cRAP_Non_normal_transcripts_dB generation

Tools

Tool Links
CONVERTER_gz_to_uncompressed
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/freebayes/freebayes/1.3.6+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/bed_to_protein_map/bed_to_protein_map/0.2.0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/custom_pro_db/custom_pro_db/1.22.0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/fasta_merge_files_and_filter_unique_sequences/fasta_merge_files_and_filter_unique_sequences/1.2.0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/gffcompare_to_bed/gffcompare_to_bed/0.2.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/translate_bed/translate_bed/0.1.0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/filter_tabular/filter_tabular/3.3.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/gffcompare/gffcompare/0.12.6+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/hisat2/hisat2/2.2.1+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/query_tabular/query_tabular/3.3.2 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/sqlite_to_tabular/sqlite_to_tabular/2.0.0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/stringtie/stringtie/2.2.3+galaxy0 View in ToolShed

To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.

Importing into Galaxy

Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!
Hands-on: Importing a workflow
  • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
  • Click on galaxy-upload Import at the top-right of the screen
  • Provide your workflow
    • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
    • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
  • Click the Import workflow button

Below is a short video demonstrating how to import a workflow from GitHub using this procedure:

Video: Importing a workflow from URL

Version History

Version Commit Time Comments
1 6e4ce4916 2024-11-14 15:41:21 Made sure the order is right

For Admins

Installing the workflow tools

wget https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/neoantigen-2-non-normal-database-generation/workflows/main_workflow.ga -O workflow.ga
workflow-to-tools -w workflow.ga -o tools.yaml
shed-tools install -g GALAXY -a API_KEY -t tools.yaml
workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows