Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30654742", "kind": "science-record", "revision": 6, "content_hash": "sha256:ef0c6b6bc1c51836b9b52f036a7f71788ae89e0f162a07d55f304c182e68a8a2", "created_at": "2026-11-01T15:40:40Z", "updated_at": "2026-11-01T21:21:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes.", "doi": "10.1186/s12864-019-5431-9", "pmid": "30654742", "authors": ["Laetitia Guillot", "Ludovic Delage", "Alain Viari", "Yves Vandenbrouck", "Emmanuelle Com", "Andrés Ritter", "Régis Lavigne", "Dominique Marie", "Pierre Peterlongo", "Philippe Potin", "Charles Pineau"], "journal": "BMC genomics", "year": 2019, "abstract": "BACKGROUND: Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes.\nRESULTS: Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data.\nCONCLUSIONS: Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu . Data are available via ProteomeXchange under identifier PXD010618.", "claims": [{"id": "claim-method", "statement": "Sensitivity was defined as the percent of expected proteins matched (true_pos/nref) and selectivity the percent of expected proteins among all proteins matched using our workflow (true_pos/nfound).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30654742", "locator": "sections[7] \"Step 2: PSTs mapping and clustering\" paragraph 2", "quote": "Sensitivity was defined as the percent of expected proteins matched (true_pos/nref) and selectivity the percent of expected proteins among all proteins matched using our workflow (true_pos/nfound)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30654742", "locator": "sections[25] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:30654742"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD010618"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD010618"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30654742", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12864-019-5431-9", "sha256": "sha256:27646b2a31b3d0c7e5e335ae1e549c329d2fb4564fc9f735669975c0c293d4de", "bytes": 70599, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12864-019-5431-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6337836", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:PXD010618", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=PXD010618", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD010618", "version": null, "note": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": "Peptimapper -  Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes", "resolution_checked_at": "2026-10-31T18:35:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:30654742:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:58445caa65855db0b1699c1ddcb76145b0815d5a5aac6c99e7a9bd0a48f5adf9", "bytes": 2403, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#Tab1", "version": null, "note": "Comparison of tag quality between Peaks and PepNovo+, using Proteome Discoverer ™ peptide identifications as the expected sequences", "structure": {"pmcid": "PMC6337836", "section_index": 11, "section_title": "Step 1: From MS/MS spectra to PSTs", "row_count": 32, "column_count": 10}, "table": {"label": "Table 1", "caption": "Comparison of tag quality between Peaks and PepNovo+, using Proteome Discoverer ™ peptide identifications as the expected sequences", "rows": [["Peaks", "Proteome discoverer(MASCOT identifications)", "Pepnovo+"], ["ID", "Tag sequence", "Significant score >60%", "Rank", "Protein AC", "Reference Sequence", "e-value", "ID", "Tag sequence", "Significant score >1,5"], ["271", "WVQAAGAGASR", "23", "0", "Esi0085_0010", "SVVQAAGAGDAK", "5,65568E-06", "Spectrum271_scans", "QAAGAGA", "6,346"], ["271", "WVQAAGAGWK", "21.5", "1"], ["271", "WVQAAGAGTGR", "18.5", "2"], ["552", "(CamC)VGVSEETTTRHR", "31", "0", "Esi0091_0058", "cVGVSEETTTGVHR", "2,36909E-07", "Spectrum552_scans", "GVSEDD", "4,237"], ["552", "QMGVSEETTTRHR", "29", "1"], ["552", "V(CamC)GVSEETTTRHR", "17", "2"], ["577", "QFAGDDAPR", "43", "0", "Esi0203_0038", "AGFAGDDAPR", "1,69937E-06", "Spectrum578_scans", "GDDAPR", "7,539"], ["577", "K(MetOxM)AGDDAPR", "41", "1"], ["577", "AGFAGDDAPR", "11.5", "2"], ["642", "KAENPMSKR", "100", "0", "Esi0349_0012", "KAEDIDTIR", "1,01272E-05", "Spectrum643_scans", "DLDTLR", "4,727"], ["642", "KAQDPMSKR", "0.1", "1"], ["642", "QAKDPMSKR", "0.02", "2"], ["1118", "DGLVYGK(MetOxM)NEPPGAR", "38", "0", "Esi0327_0021", "ATLVYGQmNEPPGAR", "2,42457E-06", "Spectrum1119_scans", "EPPGAR", "1,396"], ["1118", "DGLVYGQFNEPPGAR", "37", "1"], ["1118", "DGLVYGQ(MetOxM)NEPPGVK", "8", "2"], ["2462", "DESAAVFAWK", "87,5", "0", "Esi0091_0058", "DESAAVFAWK", "1,64304E-05", "Spectrum2463_scans", "SAAVFA", "4,78"], ["2462", "DESAAVFAGTR", "8", "1"], ["2462", "LMSAAV(MetOxM)AGEK", "1.5", "2"], ["2479", "MDDLTNNALARK", "58", "0", "Esi0888_0002", "mVDLTPMAIAAGR", "4,70366E-07", "Spectrum2480_scans", "ALAAGR", "1,943"], ["2479", "(MetOxM)VDLTNNALAAGR", "39.5", "1"], ["2479", "MDDLTNGGALARK", "1", "2"], ["2558", "ED(MetOxM)ETE(CamC)AVNYDNLYQVMK", "48", "0", "Esi0349_0012", "TccEAEcAVNYDNLYQAMR", "1,01272E-05", "Spectrum2559_scans", "VNYDNL", "2,289"], ["2558", "DE(MetOxM)ETE(CamC)AVNYDNLYQVMK", "8.5", "1"], ["2558", "ED(MetOxM)ETE(CamC)AVNYDNLYQMVK", "8", "2"], ["2610", "TFQAGEVASALLGR", "32", "0", "Esi0327_0021", "FTQAGAEVSALLGR", "5,99619E-08", "Spectrum2610_scans", "SALLGR", "4,356"], ["2610", "FTQAGEVASALLGR", "25", "1"], ["2610", "TFKAGAGNGSALLGR", "10", "2"], ["3191", "VALTGLTLAEYFR", "53", "0", "Esi0327_0021", "VALTGLTIAEYFR", "", "Spectrum3196_scans", "LAEYFR", "4,471"], ["3191", "GLLTGLTLAEYFR", "25.5", "1"], ["3191", "AVLTGLTLAEYFR", "11", "2"]], "row_count": 32, "column_count": 10, "numeric_cells": 131, "header_rows": 2, "locator": "section[11]/table-wrap[1]#Tab1"}}, {"id": "asset:table:pmid:30654742:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:f14d56315f5b398751356be3ea994de449233757ee493f631502f8aa127a1d0b", "bytes": 6365, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[1]#Tab2", "version": null, "note": "Eukaryotic proteogenomics pipelines and Galaxy workflows", "structure": {"pmcid": "PMC6337836", "section_index": 17, "section_title": "Discussion", "row_count": 15, "column_count": 9}, "table": {"label": "Table 2", "caption": "Eukaryotic proteogenomics pipelines and Galaxy workflows", "rows": [["Name", "Pipeline Interface", "Database-driven for peptide identifi-cation", "de novo peptide inter-pretation", "User-friendly for biologists", "Results curation", "Results visuali-zation", "Description", "Revelance"], ["Peptimapper (released in 2018)", "Command line, Docker image, Galaxy tools", "–", "√", "√", "√", "√", "Peptide Sequence Tags (PSTs) obtained from partial interpretation of ion trap mass spectra are mapped onto the six-frame translation of genomic sequences giving hits. Hits are then clustered to detect potential coding regions. Clusters are evaluated and further compared to existing gene predictions. Clusters are available as GFF file to be uploaded into a genome viewer. https://galaxy.protim.eu https://hub.docker.com/r/dockerprotim/peptimapper/ or https://docker-ui.genouest.org/app/#/container/dockerprotim/peptimapper https://github.com/laeticlo/Ectoline", "Improves genome annotation"], ["IPAW (2018) [61]", "Command line", "√", "–", "–", "√", "–", "This is an Integrated Proteomics Analysis Workflow: i) Peptide spectra are searched in two different databases in parallel: VarDB filtered by class-specific FDR for SAAV peptides and 6FT of the human genome filtered by peptides pI. ii) SAAV candidates are curated by SpectrumAI and potential novel proteins are blasted onto public databases. ii) Curated results are validated by different controls. https://github.com/yafeng/proteogenomics_python", "Identification of Pseudogenes, lncRNAs, nsSNPs and somatic mutations"], ["JUMPg (2016) [62]", "Command line", "√", "–", "–", "√", "√", "This pipeline includes multiple customized databases construction, tag-based database search, peptide-spectrum match filtering, ans data visualization. https://github.com/gatechatl/JUMPg/", "Improves genome annotation"], ["PGMiner (2016) [63]", "Command line", "√", "–", "–", "√", "√", "This workflow allows acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, and mapping confident peptides to gene models. https://github.com/olalonde/pgtools", "Improves genome annotation"], ["PROTEO-FORMER (2015) [64]", "Command line, Virtual machine, Galaxy tools", "√", "–", "√", "√", "√", "RIBO-seq NGS data are processed to delineates proteoforms. RIBO-seq-derived sequences are then translated and mapped to a public database, creating a custom search database for peptides to MS/MS matching.", "Identification of novel translation products"], ["PGTools (2015) [65]", "Command line", "√", "–", "–", "√", "√", "The software is divided into 2 phases: Phase 1 contains 8 modules to analyse MS/MS data using known proteins databases. Phase 2 contains 5 modules and 7 customized databases that allow MS/MS data to be analysed against the genome. That software includes applications, libraries, customized databases and visualization tools.", "Improves genome annotation"], ["NextSearch (2015) [66]", "Command line", "–", "–", "–", "√", "√", "Nucleotide EXon-graph Transcriptome Search identifies peptides by directly searching the nucleotide exon graph against tandem mass spectra. NextSearch outputs which are the proteome-genome/transcriptome mapping that can be visualized using public tools.", "Improves genome annotation"], ["ProteoAnnotator (2014) [52]", "Command line, Stand alone application", "√", "–", "√", "√", "√", "MS spectrum are queried by one or several proteomics databases search engines (MASCOT, OMSSA, X!Tandem or MSGF+) and results are converted into GFF adding genome coordinates and statistical confidence values. It exports mzIdentML files. http://www.proteoannotator.org", "Improves genome annotation"], ["Peppy (2013) [67]", "Command line, Stand alone application", "√", "–", "N/A", "√", "–", "This workflow generates a peptide database from a genome, tracks peptide loci, matches peptides to MS/MS spectra and assigns FDR confidence values to those matches. http://geneffects.com/peppy", "Improves genome annotation"], ["Protk (released in 2012)", "Command line, Galaxy tools", "√", "–", "√", "–", "√", "It is a suite of tools for proteomics providing the following analysis tasks: (i) MS/MS data search with X!Tandem, Mascot, OMSSA and MS-GF+; (ii) peptide and protein inference with Peptide Prophet, iProphet and Protein Prophet; (iii) conversion of pepXML or protXML to tabular format, and (iv) mapping of peptides to genomic coordinates https://github.com/iracooke/protk", "Improves genome annotation"], ["IggyPep (2010) [54]", "Web interface", "√", "√", "N/A", "–", "–", "The pipeline is based on a database system with advanced indexing and querying strategy, which holds the translated genome in all six reading frames. It can be queried with de novo sequences or partial peptide sequence tags (PSTs). It determines the ORF amino acid comprising these tags and compiles a fasta-formated sequence file for a database-driven search. www.iggypep.org (No more accessible)", "Improves genome annotation"], ["PepLine (2008) [18]", "Command line", "–", "√", "N/A", "√", "–", "Peptide Sequence Tags (PSTs) obtained from partial interpretation of QTOF mass spectra are mapped onto the six-frame translation of genomic sequences giving hits. Hits are then clustered to detect potential coding regions.www.grenoble.prabi.fr/protehome/software/pepline (no more accessible)", "Improves genome annotation"], ["Workflows for Proteomics Informed by Transcriptomics (2015) [57]", "Galaxy tools", "√", "–", "√", "√", "√", "Galaxy Integrated Omics (GIO) provides workflows for 4 common use cases: i) a standard search against a reference proteome; ii) PIT protein identification without a reference genome; iii) PIT protein identification using a genome guide; iiii) and PIT genome annotation. http://gio.sbcs.qmul.ac.uk", "Improves genome annotation"], ["Workflows for proteogenomics studies using Galaxy-P (2014–2018) [55, 56, 58, 59]", "Galaxy tools", "√", "–", "√", "√", "√", "These modular workflows incorporating both established and customized software tools that improve depth and quality of proteogenomic results. http://galaxyp.org", "Improves genome annotation"]], "row_count": 15, "column_count": 9, "numeric_cells": 17, "header_rows": 1, "locator": "section[17]/table-wrap[1]#Tab2"}}, {"id": "asset:table:pmid:30654742:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:8e03a2f28c94ae8ca93a5a5e845ef1adad8a8c9a5e6bfd1a843202b492ac4b27", "bytes": 2105, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[2]#Tab3", "version": null, "note": "Additional clusters currently under investigation", "structure": {"pmcid": "PMC6337836", "section_index": 17, "section_title": "Discussion", "row_count": 17, "column_count": 11}, "table": {"label": "Table 3", "caption": "Additional clusters currently under investigation", "rows": [["Cluster ID", "Contig", "Strand", "From", "To", "Strain", "Tot pep", "RNA data", "Genetic map", "Action", "Identification"], ["113", "sctg_117", "D", "265421", "281797", "EC494", "10", "ESTs+RNAseq", "LGUn", "probable new gene", "Esi0117_0046 similar sequence"], 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"146476", "147692", "Ec32", "3", "RNAseq", "LG03", "Esi0043_0035 model correction", "Catalase"], ["822", "sctg_48", "R", "267223", "267911", "Ec32", "5", "RNAseq", "LG23", "Esi0048_0051 model correction", "Hypothetical protein"], ["492", "sctg_253", "D", "215846", "216917", "Ec32", "3", "RNAseq", "LGUn", "probable new gene", "none"], ["567", "sctg_291", "R", "60813", "65620", "Ec32", "3", "RNAseq", "LG03", "Esi0291_0011 model correction", "mTERF domain-containing protein"], ["618", "sctg_310", "D", "63974", "71773", "Ec32", "3", "RNAseq", "LGUn", "probable new gene", "none"], ["697", "sctg_365", "R", "137829", "143627", "Ec32", "3", "RNAseq", "LGUn", "probable new gene", "none"], ["218", "sctg_87", "R", "471174", "478804", "Ec410", "3", "No data", "LG26", "probable new gene", "Retrotransposon integrase-like protein"]], "row_count": 17, "column_count": 11, "numeric_cells": 115, "header_rows": 1, "locator": "section[17]/table-wrap[2]#Tab3"}}], "gaps": ["asset:data:geo:PXD010618 not downloaded", "asset:data:geo:PXD010618 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Sensitivity and selectivity", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Sensitivity was defined as the percent of expected proteins matched (true_pos/nref) and selectivity the percent of expected proteins among all proteins matched using our workflow (true_pos/nfound)."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["PXD010618"], "resolved_accessions": ["PXD010618"], "statements": ["The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request."], "basis": "The paper names PXD010618 in ProteomeXchange Consortium via the PRIDE partner repository and the accession resolves in the repository. 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CONCLUSIONS: Peptimapper is a compleme", "clusters under investigation listed Table 3. RNA-sequencing and EST data have been incorporated in the b"], "order_basis": "paper_text_order:1600", "id": "assay:1"}]}