Container image for paper: Computational challenges to reproducibility, robustness, extensibility and reuse in multi-omics: a meta-workflow-based case study
Creators: name: Adusumilli, Ravali
name: Ratnakar, Varun
Publisher: name: Zenodo
Year: 2021
Subjects: wings
omic
genomics
proteomics
multi-omics
workflows
reproducibility

description: This Docker image contains the WINGS framework, together with all the tools and workflows needed to execute the multi-omics analyses described in the paper "Computational challenges to reproducibility, robustness, extensibility and reuse in multi-omics: a meta-workflow-based case study" These include tools for peptide search, protein assembly, spectral search, filter, protein data conversion, etc. The image contains a mixture of data, workflows and software, ready for execution. List of installed tools (and version): bumbershoot 3_0_9870, MSGFPlus 9979, proteowizard 3_0_7692, rsem 1.3, bcftools1.3, bowtie2 2.2.6, cufflinks 2.2.1, TopHat2 2.1.0, Samtools 1.3.0, TPP, python2.7.5, R3.2.3,java1.8 1.8.0_71, sqlite3 3, zipdir 3.0, Samtools 0.1.6, VarScan 2.4.0, bftools 5.2.4, MuTecT 1.17, picard 2.6, cellprofiler 2.4.0rc1, java1.7 1.7 <strong>R packages</strong><br> Rsubread (https://bioconductor.org/packages/release/bioc/html/Rsubread.html)<br> easyGgplot2 (http://www.sthda.com/english/wiki/easyggplot2) - gets ggplot2 as well<br> reshape2<br> Vennerable<br> limma<br> PerformanceAnalytics<br> Psych<br> reshape<br> org.Hs.eg.db<br> gage<br> dplyr<br> biomaRt<br> ConsensusClusterPlus (https://www.bioconductor.org/packages/release/bioc/html/<br> corrplot<br> Hmisc<br> customProDB (https://bioconductor.org/packages/release/bioc/html/customProDB.html)<br> Rtsne<br> pamr<br> multiOmicsViz<br> UpSetR <strong>python 2.7 packages</strong><br> Tkinter<br> pandas<br> matplotlib<br> matplotlib_venn<br> seaborn sqlite3 extension<br> sqlite3-pcre Instructions: <strong>Download and run the Docker image:</strong> Download and install docker from: https://www.docker.com/ Start Docker Download the image: docker pull kcapd/wings-genomics:latest. If you use this archived image, you will have to execute: docker load -i $path where $path is the path to the downloaded image. Run the image: docker run -p 8080:8080 -d kcapd/wings-genomics-full:latest In a browser, go to: http://localhost:8080/wings-portal/ Username: admin, password: 4dm1n!23 On WINGS portal, go to “Analysis &gt; Run Workflows” <strong>Executing multiomic workflows on Wings:</strong> MultiProteomic_SpectralLibSearch - Stage3 reanalysis workflow SingleProteogenomicSearch - Proteogenomic reanalysis workflow SingleProteomicSearch_ITRAQ - Proteomic workflow for iTRAQ data SingleProteogenomicSearch_ITRAQ - Proteogenomic workflow for iTRAQ data Note: Refer to Tables 1 and 2 below from the “DockerWorkflowFilesDescription” document for input and output files descriptions. MultiProteomic_SpectralLibSearch: This workflow closely resembles the steps from the original proteomic analysis and was used for stage 3 reanalysis. Uses Myrimatch and MSGPlus for peptide searches and Pepitome for spectral library based peptide searches. Results from the three mentioned search engines are merged together to assemble proteins. Input files: <em>ProtDataZip: SampleData1_mzML.zip</em> <em>sampleIDs: SampleIDs.txt</em> <em>ref_proteome_fasta: humanRefSeq_Version54_with_tryp_DECOY.fasta</em> <em>spectralLibFile: human_2011_05_26_it_decoy.sptxt</em> <em>pepSearchParams: ZhangMM_Exact.params, ZhangMSGFPlus_Exact.params (shift select both)</em> <em>pepSpectralSearchParams: ZhangPepitome_Exact.params</em> <em>pepConvertParams: toPepXML.params</em> <em>idpQonvertParams: idpqonvert.params</em> <em>idpQonvertGeneParams: idpqonvert_gene.params</em> <em>condFile: NOplex.condition.xml</em> <em>protAssemblyParams: idpassemble_0.001.params</em> <em>protAssemblyParams_redone: idpassemble_0.01.params</em> <em>protConvertParams: idpQueryProt.Params</em> <em>protConvertParams_withGene: idpQueryGene.Params</em> Output files (Required): <em>ref_protConvertSpectra: Protein groups results file</em> <em>ref_protConvertSpectraWithGene: Gene groups results file</em> SingleProteogenomicSearch: This workflow replicates the steps of the proteogenomics analysis from the original analysis and was generates single amino acid variants (SAAVs) and variant peptides. Input files: <em>ProtData: SampleData1_mzML.zip</em> <em>RNAData: SampleData1_fastq.zip</em> <em>sampleIDs: SampleIDs.txt</em> <em>RefFastaAnnot: hg19_cpdb_bundle.zip</em> <em>cufflinksParams: cufflinks.params</em> <em>decoyParams: decoyFASTA.params</em> <em>fastqToBamParams: tophat2.params</em> <em>genesAnnotFile: hg19_genes.gtf</em> <em>genomicsFASTA: hg19.fa (or test.fa)</em> <em>refFastaBundle: hg19_tux_bundle.zip</em> <em>refFastaBundleRSEM: hg19_RSEM_bundle.zip</em> <em>rsemParams: rsem_SE.params</em> <em>pepSearchParams: ZhangMM_Exact.params</em> <em>pepConvertParams: toPepXML.params</em> <em>idpQonvertParams: idpqonvert.params</em> <em>idpQonvertGeneParams: idpqonvert_gene.params</em> <em>condFile: NOplex.condition.xml</em> <em>protAssemblyParams: idpassemble_0.001.params</em> <em>protAssemblyParams_redone: idpassemble_0.01.params</em> <em>protConvertParams: idpQueryProt.Params</em> <em>protConvertParams_withGene: idpQueryGene.Params</em> <em>protConvertParams_pepLevel: idpQueryPep.Params</em> Output files (Required): <em>custom_protConvertSpectra_redone: Protein groups results file</em> <em>custom_protConvertSpectraWithGene_redone: Gene groups results file</em> <em>custom_protConvertSpectra_pepData: Peptide results file</em> SingleProteomicSearch_ITRAQ: This generic workflow processes proteomics iTRAQ data using MSGFPlus search engine. Input files: <em>ProtDataZip: SampleData_iTRAQ_mzML.zip</em> <em>sampleIDs: SampleIDs_iTRAQ.txt</em> <em>ref_proteome_fasta: humanRefSeq_Version54_with_tryp_DECOY.fasta</em> <em>pepSearchParams: MSGFPlus_iTRAQ4plex.params</em> <em>pepConvertParams: toPepXML.params</em> <em>idpQonvertParams: idpqonvert.params</em> <em>idpQonvertGeneParams: idpqonvert_gene.params</em> <em>idpQonvertParams_Quant: idpqonvert_iTRAQ4plex.params</em> <em>condFile: NOplex.condition.xml</em> <em>condFile_withQuantData: iTRAQ4plex_idpicker.params</em> <em>protAssemblyParams: idpassemble_0.01.params</em> <em>protAssemblyParams_redone: idpassemble_0.01.params</em> <em>protAssemblyParams_withQuantData: idpassemble_0.01_iTRAQ4plex.params</em> <em>protConvertParams: iTRAQ4plex_idpQueryProt.params</em> <em>protConvertParams_withGene: iTRAQ4plex_idpQueryGene.params</em> Output files (Required): <em>ref_protConvertSpectra: Protein groups results file</em> <em>ref_protConvertSpectraWithGene: Gene groups results file</em> SingleProteogenomicSearch_ITRAQ: This generic workflow processes proteogenomics iTRAQ data using MSGFPlus search engine. Input files: <em>ProtData: SampleData_iTRAQ_mzML.zip</em> <em>RNAData: SampleData1_fastq.zip</em> <em>sampleIDs: SampleIDs_iTRAQ.txt</em> <em>RefFastaAnnot: hg19_cpdb_bundle.zip</em> <em>cufflinksParams: cufflinks.params</em> <em>decoyParams: decoyFASTA.params</em> <em>fastqToBamParams: tophat2.params</em> <em>genesAnnotFile: hg19_genes.gtf</em> <em>genomicsFASTA: hg19.fa (or test.fa)</em> <em>refFastaBundle: hg19_tux_bundle.zip</em> <em>refFastaBundleRSEM: hg19_RSEM_bundle.zip</em> <em>rsemParams: rsem_SE.params</em> <em>pepSearchParams: ZhangMM_Exact.params</em> <em>pepConvertParams: toPepXML.params</em> <em>idpQonvertParams: idpqonvert.params</em> <em>idpQonvertGeneParams: idpqonvert_gene.params</em> <em>idpQonvertParams_Quant: idpqonvert_iTRAQ4plex.params</em> <em>condFile: NOplex.condition.xml</em> <em>condFile_withQuantData: iTRAQ4plex_idpicker.params</em> <em>protAssemblyParams: idpassemble_0.01.params</em> <em>protAssemblyParams_redone: idpassemble_0.01.params</em> <em>protAssemblyParams_withQuantData: idpassemble_0.01_iTRAQ4plex.params</em> <em>protConvertParams: iTRAQ4plex_idpQueryProt.params</em> <em>protConvertParams_withGene: iTRAQ4plex_idpQueryGene.params</em> <em>protConvertParams_pepLevel: iTRAQ4plex_idpQueryPep.params</em> Output files (Required): <em>custom_protConvertSpectra_redone: Protein groups results file</em> <em>custom_protConvertSpectraWithGene_redone: Gene groups results file</em> <em>custom_protConvertSpectra_pepData: Peptide results file</em> <strong>Executing metaworkflows on WINGS to reproduce figures from the original paper:</strong> Metaworkflows typically accept “Run ID(s)” from the multiomic workflows as input. Below are the metaworkflows necessary to reproduce the figures from the original paper Figure 2: MetaAnalysis_OriginalVsReanalysis Figure 3: MetaAnalysis_ParamSensitivity Figure 4: MetaAnalysis_AdditionalData Figure 5: MetaAnalysis_NewData Note: Refer to Tables 3 and 5 below from the “DockerWorkflowFilesDescription” document for metaworkflow input file and output file descriptions. For metaworkflow parameter descriptions, refer to Table 1 from the “DockerWorkflowParametersDescription.docx” document. <strong>Metaworkflows with files as input for simplicity</strong> Figure 2: MetaAnalysis_OriginalVsReanalysisFiles Input files: <em>OriginalProtFile</em>: ProteinFile_Deposited.tsv <em>ReanalysisProtFile</em>: ProteinFile_Stage3.tsv <em>ORSEMNormWideFile</em>: RSEMNormMatrix_Original.tsv <em>ReRSEMNormWideFile</em>: RSEMNormMatrix_Reanalysis.tsv <em>SampleIDs</em>: SampleIDs_CRC95.txt Input Parameters: <em>DatasetsStr</em>: Original,Reanalysis <em>ODatasetName</em>: Original <em>OProtRSEMCorrByGeneColname</em>: Original_ProteinRNA_Corr_ByGene <em>OProtRSEMCorrBySampleColname</em>: Original_ProteinRNA_Corr_BySample <em>ReProtRSEMCorrByGeneColname</em>: Reanalysis_ProteinRNA_Corr_ByGene <em>ReProtRSEMCorrBySampleColname</em>: Reanalysis_ProteinRNA_Corr_BySample <em>ReDatasetName</em>: Reanalysis <em>OReProtRSEMByGeneSplot</em>: Original_Reanalysis_ProteinRNA_Corr_ByGene <em>OReProtRSEMBySampleSplot</em>: Original_Reanalysis_ProteinRNA_Corr_BySample <em>OReProtCorrColname</em>: Original_Reanalysis_ProteinCorrelation <em>OReProtDPlotTitle</em>: Original_Reanalysis_ProteinCorrelation <em>OReRSEMCorrColname</em>: Original_Reanalysis_RNACorrelation <em>OReRSEMDPlotTitle</em>: Original_Reanalysis_RNACorrelation <em>vennTitle_Proteins</em>: Original_Reanalysis_ProteinGroups Output plots (required): <em>OReProtDensityPlot: </em>Density plot that illustrates the protein correlation between deposited and stage 3 reanalysis datasets <em>OReProtRSEMByGeneSPlot: </em>Scatter plot that illustrates mRNA-protein correlation per gene, between deposited and stage 3 reanalysis datasets <em>OReProtRSEMBySampleSPlot: </em>Scatter plot that illustrates mRNA-protein correlation per sample, between deposited and stage 3 reanalysis datasets <em>OReProtsBarplot: </em>Barplot showing protein count for each sample per dataset <em>OReRSEMDensityPlot: </em>Density plot illustrating correlation of RNA measures between deposited and stage 3 reanalysis datasets <em>vennProtIDs: </em>Venn<em> </em>diagram of protein groups in deposited and stage 3 reanalysis datasets Figure 3: MetaAnalysis_ParamSensitivityFiles Input files: <em>OSAAVFile: </em>SAAVs_Published.tsv <em>ReSAAVFile:</em> SAAVs_Stage3.tsv <em>OVariantPepFile: </em>VariantPeptides_Published.tsv <em>ReVariantPepFile: </em>VariantPeptides_Stage3.tsv <em>RNAProtPaired_SampleIDs: </em>ZhangPGPaired_91SampleIDs.txt <em>ReCustomProtFile: </em>ProteinFile_CustomDB.tsv <em>SampleIDs: </em>SampleIDs_CRC95.txt <em>MyrimatchProtFile1: </em>ProteinFile_Myrimatch1.tsv <em>MyrimatchProtFile2: </em>ProteinFile_Myrimatch2.tsv <em>MyrimatchPepFile1: </em>Peptide_Myrimatch1.tsv <em>MyrimatchPepFile2: </em>Peptide_Myrimatch2.tsv <em>ProtFile1: </em>ProteinFile_Search1.tsv <em>ProtFile2: </em>ProteinFile_Search2.tsv <em>ProtFile3: </em>ProteinFile_Search3.tsv <em>ProtFile4: </em>ProteinFile_Search4.tsv Input Parameters: <em>Myrimatch_DatasetsStr: </em>Myrimatch1,Myrimatch2 <em>OReDatasetStr: </em>Original,Reanalysis <em>ReRefCustomMMDplot: </em>Ref_Custom_ProteinCorrelation_Reanalysis <em>SAAVColname: </em>SAAV <em>VariantPeptideColname: </em>peptide_sequence <em>datasetName1: </em>Search1 <em>datasetName2:</em> Search2 <em>datasetName3: </em>Search3 <em>datasetName4: </em>Search4 <em>datasetNameMyrimatch1: </em>Myrimatch1 <em>datasetNameMyrimatch2: </em>Myrimatch2 <em>datasetNameOProteogenomic: </em>Original <em>datasetNameReProteogenomic: </em>Reanalysis <em>pepseqColname: </em>Sequence <em>protAccessionsColname: </em>Accessions <em>vennTitle_PepMyrimatch: </em>Peptides <em>vennTitle_ProtMyrimatch: </em>Protein_Groups <em>vennTitle_SAAVs: </em>SAAVs <em>vennTitle_VariantPeptides: </em>Variant_Peptides Output plots (required): <em>densityPlot:</em> Plot showing the density distribution of protein correlations for overlapping proteins between reference and custom protein sequence database searches <em>protsPerSearchBarplot: </em>Bar plot depicting number of protein identifications across search engines <em>vennOReSAAVs: </em>Venn diagram of SAAVs in the deposited and stage3 analysis datasets. Note: The SAAV Venn diagram needs manual intervention to produce accurate plot. For example, same reference protein IDs and SAAV IDs. The deposited and stage3 reanalysis differ in the protein IDs. <em>vennOReVariantPeptides: </em>Venn diagram of variant peptide sequences in the deposited and stage3 analysis datasets<em>.</em> <em>vennPepMyrimatch:</em> Venn diagram of peptide sequences in two Myrimatch searches<em>. </em> <em>vennProtMyrimatch: </em>Venn diagram of protein groups in two Myrimatch searches<em>.</em> Figure 4: MetaAnalysis_AdditionalDataFiles Input files: <em>ProtFile1: </em>ProteinFile1_95Samples.tsv <em>ProtFile2:</em> ProteinFile2_100Samples.tsv <em>ProtFile3:</em> ProteinFile3_105Samples.tsv <em>ProtFile4:</em> ProteinFile4_110Samples.tsv <em>ProtFile5: </em>ProteinFile5_115Samples.tsv <em>SampleIDs1: </em>SampleIDs_CRC95.txt <em>SampleIDs2: </em>SampleIDs_CRC95_CO_5.txt <em>SampleIDs3: </em>SampleIDs_CRC95_CO_10.txt <em>SampleIDs4: </em>SampleIDs_CRC95_CO_15.txt <em>SampleIDs5: </em>SampleIDs_CRC95_CO_20.txt <em>sampleDupAnn1: </em>CRC_sampleAnn.txt <em>sampleDupAnn2: </em>CRC_sampleAnn100.txt <em>sampleDupAnn3: </em>CRC_sampleAnn105.txt <em>sampleDupAnn4: </em>CRC_sampleAnn110.txt <em>sampleDupAnn5: </em>CRC_sampleAnn115.txt <em>batchesFile: </em>CRC_BatchAnn115.txt <em>dataSubtypeAnn: </em>CRC_subtypeAnn.txt Input Parameters: <em>datasetName1: </em>CRC95 <em>datasetName2: </em>CRC100 <em>datasetName3: </em>CRC105 <em>datasetName4: </em>CRC110 <em>datasetName5: </em>CRC115 Output plots (required): upsetPlot: UpSet Plot showing protein intersections and counts within and across datasets. Figure 5: MetaAnalysis_NewDataFiles Input files: <em>ProtFile1:</em> ProteinFile1_95Samples.tsv <em>ProtFile2:</em> ProteinFile_NCI60.tsv <em>ProtFile3:</em> ProteinFile_OV.tsv <em>ProtFile4:</em> ProteinFile_BRCA.tsv <em>SAAVFile1</em>: SAAVs_Stage3.tsv <em>SAAVFile2</em>: SAAVs_OV.tsv <em>SAAVFile3:</em> SAAVs_BRCA.tsv <em>SampleIDs1: </em>SampleIDs_CRC95.txt <em>SampleIDs2: </em>SampleIDs_NCI60.txt <em>SampleIDs3: </em>SampleIDs_OV.txt <em>SampleIDs4: </em>SampleIDS_BRCA.txt Input Parameters: <em>DatasetsStr:</em> CRC95,NCI60,OV,BRCA <em>SAAVColname:</em> SAAV <em>datasetName1: </em>CRC95 <em>datasetName2: </em>NCI60 <em>datasetName3: </em>OV <em>datasetName4: </em>BRCA Output plots: barplotProtsPerDataset: Barplot depicting protein counts for CRC95,NCI60,OV and BRCA datasets barplotSAAVsPerDataset: Barplot depicting SAAV counts for CRC95,OV and BRCA datasets vennSAAVs: Venn diagram of SAAVs in CRC95,OV,BRCA datasets Note: Refer to Tables 3, 4 and 5 from the “DockerWorkflowFilesDescription” document for input and output files description. In addition to the above-mentioned meta workflows, the docker also contains few workflows starting with “MetaAnalysisCompare”. These “MetaAnalysisCompare” workflows are subsets of the “MetaAnalysis” workflows listed above. These are available independently to ease individual comparison between datasets.