PAXDBapplication/xmlhttp://pax-db.org/downloads/latest/datasets/bioprojects-abundance-files-v4.0.zipprimaryOK2000000ProteomicsChristian von Mering10090https://pax-db.org/dataset/10090/1257877986mering@imls.uzh.chUniversity of ZurichPAXDBFor the rescaling, the datasets are first parsed or processed such that the data reflect proportional abundances of whole protein molecules (i.e. proportionality to counts of complete, individual protein molecules, not to molecular weights, protein volumes, or digested peptides). In the case of spectral counting data protein. The proportional abundances are rescaled linearly to add up to one million; this means the abundance of each protein of interest is finally expressed in (parts per million,) relative to all other proteins in a sample. For a given protein abundance dataset, we then compute the absolute log abundance ratios of all pairs of proteins annotated to be functionally linked. The median of these absolute log abundance ratios represents an indirect quality metric: the closer it is to zero, the better (i.e. the more there is consistency between abundance values and functional annotations such as protein complexes or pathways). We then compute a background expectation for this metric, by permuting the abundance values in a given dataset randomly, and recomputing the median log abundance ratios. The permutation is repeated several times, yielding a distribution of medians. The actually observed median is then expressed as a Z-score distance to the random distribution ofmedians—this distance is termed the interaction consistency score.Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.Gomez-Cabrero David D, Tarazona Sonia S, Ferreirós-Vidal Isabel I, Ramirez Ricardo N RN, Company Carlos C, Schmidt Andreas A, Reijmers Theo T, Paul Veronica von Saint VVS, Marabita Francesco F, Rodríguez-Ubreva Javier J, Garcia-Gomez Antonio A, Carroll Thomas T, Cooper Lee L, Liang Ziwei Z, Dharmalingam Gopuraja G, van der Kloet Frans F, Harms Amy C AC, Balzano-Nogueira Leandro L, Lagani Vincenzo V, Tsamardinos Ioannis I, Lappe Michael M, Maier Dieter D, Westerhuis Johan A JA, Hankemeier Thomas T, Imhof Axel A, Ballestar Esteban E, Mortazavi Ali A, Merkenschlager Matthias M, Tegner Jesper J, Conesa Ana AAdd, 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, ratioPanomics, Mus musculus, Multi Omics, Laboratory Mice., Integrative-Omics, B-lymphocyte differentiation, Data Set, Laboratory, Swiss, mice, Mus domesticus, B-cell differentiation, mouse, Swiss Mouse, Integrative, House Mice, Swiss Mice, Omics, Integrative Omics, House Mouse, mice C57BL/6xCBA/CaJ hybrid, domesticus, Mus muscaris, Multi-Omics, House, Mus, B cell development, B lymphocyte differentiation, Multi-Omic, Pan Omics, Mus musculus domesticus, Pan-Omics, Mouse, house mouse, Mice, Laboratory MouseB cell, B-cell, Bursa-Equivalent Lymphocyte, B Cells, muscle, B lymphocyte, B-lymphocyte, B-Lymphocyte, B Lymphocytes, musculus, muscle element, Bursa-Dependent Lymphocytes.Panomics, Regulations, cytoplasmic chromatin, d230, single-organism developmental process, Procedures, determination, conformation, Laboratory, Peptidomics, dSmurf1, postnatal development, Mus domesticus, number, Metabonomic, Gene, dTAFII250, growth and development, Metabonomics, EfW1, body system, House Mouse, Social Controls, protrusion, Multi-Omics, dmTAF[[II]]230, Techniques, DSmurf, House, dmTAF1, Method, Taf230, Studies, Mus musculus domesticus, system, Metabolomic, Mice, Formal Social Controls, Technique, Chromatins, TAF250, Taf200, anatomical systems, B-lymphocyte differentiation, dTAF[[II]]250, Gene Expressions, TFIID TAF250, pre-mortem, growth of cell, cel, Swiss, cell, non-developmental growth of a unicellular organism, Swiss Mice, Taf1p, metabolic process resulting in cell growth, Omics, procedures, Expressions, Social, Study, dTAF250, d-smurf, Methodological Studies, Smurf, B lymphocyte differentiation, chromosome scaffold, cellular growth, Pan-Omics, Expression, house mouse, TAF, relational structural quality, CG4943, D-smurf, nuclear chromatin, close to, data, dTAF[[II]]230, TAF[[II]]250, Integrative-Omics, anatomical protrusion, Data Set, Formal Social Control, metabolism resulting in cell growth, mouse, B-cell differentiation, TAF200, l(3)84Ab, function, BG:DS00004.13, Lack, Procedure, TAFII-250, Integrative Omics, Dsmurf, TAF250/230, Cell, mice C57BL/6xCBA/CaJ hybrid, Mus muscaris, dTAF230, near to, development, dSmurf, cell expansion, TAFII250, Social Control, Mus, body system., p230, non-developmental cell growth, Smurf ubiquitin ligase, chemical analysis, Multi-Omic, Pan Omics, TAF[[II]]250/230, TFIID, techniques, connected anatomical system, Taf[[II]]250, Mus musculus, Multi Omics, Epistemology, TAF[[II]]230, DmelCG4943, mice, postnatal growth, Swiss Mouse, Integrative, Control, House Mice, TAF[II]250, Methodological, Controls, CG17603, Methodological Study, TAF[[II]], domesticus, Laboratory Mice, organ system, living, DmelCG17603, Taf250, spine, approaches, SR3-5, B cell development, vicinity of, cardinality, regulation, Mouse, assay, Regulation, growth, Laboratory Mouse, TAF230, methodology, TAF10falseM.musculus - B cell, iBAQ (Gomez-cabreroetal,scidata2019)abundance based on iBAQ, Interaction consistency score: 11.5, Coverage: 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