{"database":"GPMDB","file_versions":[],"scores":{"citationCount":0,"reanalysisCount":0,"viewCount":56,"searchCount":5},"additional":{"omics_type":["Other"],"submitter":["Chawade A, et al."],"instrument_platform":["Instrument"],"disease":["Not Available"],"brenda_tissue":["Not available"],"species":["Streptococcus_pyogenes_m1_gas, Streptococcus_pyogenes_manfredo"],"publication":["25407311"],"submitter_mail":["fredrik.levander@immun.lth.se"],"model":["http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037591","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037581","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037537","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037601","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037566","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037545","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037586","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037576","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037596","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037571","http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037561"],"submitter_affiliation":["Department of Immunotechnology, Lund University"],"cell_type":["Not available"],"repository":["GPMDB"],"pubmed_abstract":["High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments."],"pubmed_title":["Data processing has major impact on the outcome of quantitative label-free LC-MS analysis."],"pubmed_authors":["Chawade Aakash A,Sandin Marianne M,Teleman Johan J,Malmström Johan J,Levander Fredrik F,","Chawade Aakash A, Sandin Marianne M, Teleman Johan J, Malmström Johan J, Levander Fredrik F"],"name_synonyms":["data analysis, count in organism, data processing, count, determination, number, chemical analysis., assay, quantitative, E430016J11Rik, RWDD5, presence, free, presence or absence in organism"],"description_synonyms":["data, Experiment, Proteomes."],"pubmed_title_synonyms":["data analysis, count in organism, data processing, count, determination, number, chemical analysis., assay, quantitative, E430016J11Rik, RWDD5, presence, free, presence or absence in organism"],"pubmed_abstract_synonyms":["liquid chromatography tandem mass spectroscopy, Bru, Plays, artificial sequence, Raw, determination, SRML1, Health Care Benchmarking, number, Spectrum Analyses, Computer, Healthcare Benchmarking, presence, LC-MS-MS, Polypeptides, Roles, LC-MSMS, Mass, Concepts, Software Engineering, Del(8)44H, Analysis, synthetic genetic interaction (sensu inequality), Computer Program, Mass Spectroscopy, Application, Toy, Mass Spectrum Analysis, Svc, LCMSMS, MRM, Analyses, Playthings, cell, Software Application, long, Computer Programs and Programming, synthetic genetic interaction defined by inequality, proteins, Computer Software Application, data processing, Puppets, Tools, Benchmarks, Benchmarking, Role Concepts, Play, Best Practice, Healthcare, z, statistical analysis, SPS1, Puppet, Applications Software, LC-MS2, data, Computer Software, PAPT, LC-MS/MS, artificial gene, Benchmark, synthetic DNA, SPDSY, Spectrum Analysis, Cell, results, Software Tools, Tool, Multiple Reaction Monitoring, Programs, Concept, Spectroscopy, polypeptide, Program, Computer Applications, count in organism, Software Tool, LC/MS/MS, Role Concept, count, Playthings and Play, Algorithm, Computer Applications Software, chemical analysis, Computer Applications Softwares, Role, synthetic, Plaything, Health Care, Softwares, Software, Mass Spectrum Analyses, Mass Spectrum, Software Applications, Col4a-1, SRM, Engineering, Spectrometry, Toys, synthetic constructs, Computer Programs, data analysis, Applications, SYNTHETIC CONSTRUCT sequences, Applications Softwares, liquid chromatography-tandem mass spectroscopy, Computer Software Applications., Best Practice Analysis, liquid chromatography tandem mass spectrometry, artificial, assay, quantitative, E430016J11Rik, RWDD5, Computer Software Applications, presence or absence in organism"],"view_count":["56"],"citation_count":["0"],"search_count":["5"],"full_dataset_link":["http://gpmdb.thegpm.org/~/dblist_gpmnum/gpmnum=GPM11210037596"],"search_domains":["dbgap_ncbi~0","patentfamilies~0","rfam~0","merops~0","complex-portal~0","uniprot~0","wormbaseparasite~0","embl-covid19~0","reactome~0","emdb~0","wgs_masters~0","ebiweb_resources~0","opentargets_genetics~0","biomodels_all~0","ipd-mhc~0","ebiweb_teams~0","taxonomy~0","genome_assembly~0","sc-experiments~0","ebiweb_people~0","enzymeportal_enzymes~0","ipd-nhkir~0","cellosaurus~0","pdbe~0","chebi~0","patentproteins~0","interpro7~0","uniref~0","chembl~0","pdbekb~0","gpcrdb~0","hgnc~0","sc-genes~0","intact~0","rhea~0","ebiweb_training~0","alphafold~0","imgt-hla~0","patentnucleotides~0","ensemblroot~0","eva_studies~0","non-coding~0","europepmc~0","pubmed~1","identifiers_registry~0","pdbechem~0","hpa-covid19~0","eva-variants-covid19~0","biosamples~0","gwas_catalog~0","biotools~0","tls_masters~0","mesh~0","coding~0","sra~0","opentargets~0","efo~0","embl-pathogen~0","project~0","pride~1","human_diseases~0","geo_datasets~0","embl~0","treefam~0","uniparc~0","ols~0","dgva~0","intenz~0","go~0","tsa_masters~0","biosamples-covid19~0","ebiweb_corporate~0","omim~0","lrg~0","earlycause-molecular-sequences~0","ipd-kir~0","empiar~0","rnacentral~0","orcid_data_claims~0","gpmdb~2","lineage-covid19~0","metagenomics~0","pfam~0","pride archive~1","varsite~0"],"reanalysis_count":["0"],"submitter_keywords":["Resource Reanalysis"],"citation_count_scaled":["0.0"],"reanalysis_count_scaled":["0.0"],"view_count_scaled":["0.01727328809376928"],"download_count_scaled":["0.0"],"normalized_connections":["1.0"],"additional_accession":[]},"is_claimable":false,"name":"Data processing has major impact on the outcome of quantitative label-free LC-MS analysis","description":"Data from ProteomeXchange, PXD ID: PXD001091. Experiment: 11, file: folder summary. Published as part of J Proteome Res. 2015 Feb 6;14(2):676-87  .","dates":{"submission":"2015-04-20"},"accession":"GPM11210037596","cross_references":{"pubmed":["25407311"],"Pride":["PXD001091"],"pride":[],"Pride Archive":["PXD001091"]}}