{"database":"PAXDB","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["http://pax-db.org/downloads/latest/datasets/bioprojects-abundance-files-v4.0.zip"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"scores":{"citationCount":0,"reanalysisCount":0,"viewCount":0,"searchCount":0},"additional":{"omics_type":["Proteomics"],"submitter":["Christian von Mering"],"species":["4932"],"full_dataset_link":["https://pax-db.org/dataset/4932/3468689758"],"submitter_email":["mering@imls.uzh.ch"],"submitter_affiliation":["University of Zurich"],"sample_protocol":[""],"repository":["PAXDB"],"data_protocol":["For the rescaling, the\ndatasets are first parsed or processed such that the data reflect\nproportional abundances of whole protein molecules\n(i.e. proportionality to counts of complete, individual protein\nmolecules, not to molecular weights, protein volumes, or digested\npeptides). In the case of spectral counting data protein. The proportional abundances are rescaled linearly to add up\nto one million; this means the abundance of each protein of\ninterest is finally expressed in (parts per million,) relative to\nall other proteins in a sample. \nFor a given protein abundance dataset, we then compute\nthe absolute log abundance ratios of all pairs of proteins\nannotated to be functionally linked. The median of these absolute\nlog abundance ratios represents an indirect quality\nmetric: the closer it is to zero, the better (i.e. the more there\nis consistency between abundance values and functional annotations\nsuch as protein complexes or pathways). We then\ncompute a background expectation for this metric, by permuting\nthe abundance values in a given dataset randomly,\nand recomputing the median log abundance ratios. The permutation\nis repeated several times, yielding a distribution of\nmedians. The actually observed median is then expressed as a\nZ-score distance to the random distribution ofmedians—this\ndistance is termed the interaction consistency score."],"pubmed_abstract":["Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms."],"pubmed_title":["Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles."],"pubmed_authors":["Piening Brian D BD, Wang Pei P, Bangur Chaitanya S CS, Whiteaker Jeffrey J, Zhang Heidi H, Feng Li-Chia LC, Keane John F JF, Eng Jimmy K JK, Tang Hua H, Prakash Amol A, McIntosh Martin W MW, Paulovich Amanda A"],"data_synonyms":["Add, DmelCG43443, ADD, ADD-87, Hts-RC, data, AU023367, Data Set, protein complex, supply, Proteins, Ovhts, Gene, HtsRC, CG9325, protein, neutral molecular compounds, protein-containing complex, Dmel_CG9325, Xt, Peptide, 1B1, add, Polypeptides, anon-EST:Posey9, protein polypeptide chains, native protein, peptido, htsRC, GLI3-190, natural protein, polypeptide chain, Add-hts, Protein, CG43443, Gene Products, l(2)k14523, Dmel_CG34197, l(2)00634, median, Ovhts-RC, background, supply and distribution, protein aggregate, all_pairs, molecule, Bph, molecula, Random selection by shearing, oligonucleotide random primer, proportion, HTS-R1, HTS, Hts, molecules, peptides, l(2)k06121, adducin, GLI3FL, distribution, AI854843, proportionality, add-like, HTS-RC, rate, proteins, Molekuel, Pdn, sample population, introduction, Protein Gene Products, Gene Proteins, Adducin, RANDOM, 10^[-6], ppm, sample, supply., quotient, Peptid, peptidos, Polypeptide, Attention Deficit Hyperactivity Disorder, l(2)01103, EST D, CG34197, HtsF, ratio"],"pubmed_title_synonyms":["lager beer yeast., Health Care Benchmarking, Saccharomyces cerevisiae 'var. diastaticus', Feature, Liquid Chromatography Mass Spectrometry, Metrics, Brewer's, High Performance Liquid Chromatography Mass Spectrometry, baker's yeast, Ultra Performance Liquid Chromatography-Mass Spectrometry, High Pressure Liquid Chromatography-Mass Spectrometry, Benchmark, Healthcare Benchmarking, Saccharomyces italicus, Saccaromyces cerevisiae, High Performance Liquid Chromatography-Mass Spectrometry, Baker's Yeasts, Sccharomyces cerevisiae, HPLC-MS, Characteristic, Liquid Chromatography-Mass, Saccharomyces diastaticus, yeast, Health Care, Analysis, Saccharomyces oviformis, UPLC-MS, Ultra Performance Liquid Chromatography Mass Spectrometry, Yeast, Ultra-Performance Liquid Chromatography-Mass Spectrometry, High Pressure Liquid Chromatography Mass Spectrometry, Control, Spectrometry, Saccharomyes cerevisiae, Spectrometries, Quality, Baker, Controls, Features, Saccharomyces uvarum var. melibiosus, S. cerevisiae, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, S cerevisiae, Quality Controls, Candida robusta, Benchmarks, Benchmarking, Saccharomyces capensis, Liquid Chromatography-Mass Spectrometries, Best Practice Analysis, Chromatography-Mass Spectrometry, Liquid, Baker's, LC-MS, Best Practice, Mycoderma cerevisiae, Characteristics, 1883, Baker's Yeast, brewer's yeast, Chromatography-Mass Spectrometries, Healthcare, Brewer's Yeast, Baker Yeast"],"name_synonyms":["total expressed protein, organism, whole body, multi-cellular organism, whole organism, animal, body, Koerper, Proteomes."],"pubmed_abstract_synonyms":["atypia, Biological Markers, Viral Marker, Surrogate Endpoints, single-organism developmental process, Clinical Markers, Laboratory, Health Care Benchmarking, Clinical Marker, Feature, Flagyl, supply, postnatal development, Liquid Chromatography Mass Spectrometry, Biochemical, Brewer's, High Performance Liquid Chromatography Mass Spectrometry, FBN, Endpoint, baker's yeast, Metrodzhil, growth and development, Ultra Performance Liquid Chromatography-Mass Spectrometry, Serum, Healthcare Benchmarking, Saccharomyces italicus, High Performance Liquid Chromatography-Mass Spectrometry, Metronidazole Hydrochloride, Surrogate End Points, peptide, Surrogate Markers, Polypeptides, Laboratory Markers, HPLC-MS, Liquid Chromatography-Mass, peptido, utilization, Biological, ECTOL1, yeast, atypical, Analysis, WMS, Metronidazole Phosphoester, lager beer yeast, UPLC-MS, Biomarker, Ultra Performance Liquid Chromatography Mass Spectrometry, Ultra-Performance Liquid Chromatography-Mass Spectrometry, Clinical, peptides, distribution, Biological Marker, Danizol, Saccharomyes cerevisiae, Spectrometries, Baker, Saccharomyces uvarum var. melibiosus, S. cerevisiae, OCTD, 2 Methyl 5 nitroimidazole 1 ethanol, Immunologic Markers, S cerevisiae, Immune, Markers, Quality Controls, Candida robusta, Benchmarks, Benchmarking, Gineflavir, MetroGel, Saccharomyces capensis, Liquid Chromatography-Mass Spectrometries, Viral Markers, Baker's, peptidos, s, biological marker, Best Practice, Mycoderma cerevisiae, Characteristics, 1883, brewer's yeast, Chromatography-Mass Spectrometries, Healthcare, GPHYSD2, Immunologic Marker, Biologic, SGS, data, use, Viral, Surrogate Endpoint, aberrant, Saccharomyces cerevisiae 'var. diastaticus', statistics and numerical data, Serum Markers, Metronidazole Phosphate, Metrics, End Point, total expressed protein, Biochemical Markers, High Pressure Liquid Chromatography-Mass Spectrometry, 2-Methyl-5-nitroimidazole-1-ethanol, defective, Benchmark, Biologic Marker, Peptide, Saccaromyces cerevisiae, Immune Marker, ACMICD, development, polypeptide, Baker's Yeasts, Sccharomyces cerevisiae, Characteristic, Trichopol, Satric, Saccharomyces diastaticus, Marker, Surrogate End Point, Algorithm, Clont, Vagilen, Metronidazole Monohydrochloride, numerical data, Health Care, MFS1, Bayer 5360, supply and distribution, WMS2, Biologic Markers, Saccharomyces oviformis, Yeast, Serum Marker, Algorithm., End Points, High Pressure Liquid Chromatography Mass Spectrometry, Surrogate, Endpoints, postnatal growth, Spectrometry, Control, Quality, Immunologic, MASS, Laboratory Marker, Controls, Features, Surrogate Marker, Metrogyl, Trivazol, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, Biochemical Marker, Best Practice Analysis, Chromatography-Mass Spectrometry, SSKS, 2-methyl-5-nitro-, Liquid, LC-MS, Peptid, Polypeptide, 1H-Imidazole-1-ethanol, Baker's Yeast, Trichazol, growth, Metric, Proteomes, Brewer's Yeast, Baker Yeast, Immune Markers"],"citation_count":["0"],"additional_accession":[]},"is_claimable":false,"name":"S.cerevisiae - Whole organism, SC (Piening,j_proteome_res,2006)","description":"abundance based on Spectral counting, Interaction consistency score: 17.9, Coverage: 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