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Journal articles on the topic 'Integration of peptidomic data'

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Author: Grafiati

Published: 6 April 2023

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1

Fang, Hai Yan, Guo Ping Zhang, Feng Gao, Xiao Ping Zhao, Peng Shen, and Shu Fang Wang. "Comparison of Auto and Manual Integration for Peptidomics Data Based on High Performance Liquid Chromatography Coupled with Mass Spectrometry." Advanced Materials Research 340 (September 2011): 266–72. http://dx.doi.org/10.4028/www.scientific.net/amr.340.266.

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A growing number of literatures appealed the necessity to develop methods of data processing for peptidome profiling and analysis. Although some methods had been established, many of them focused on the development and application of auto integration softwares. In this work, we paid attention to comparison of auto integration by software and manual integration for peptidomics data based on high performance liquid chromatography coupled with mass spectrometry (HPLC-MS). Two data processing procedures, auto integration by XCMS and manual integration, were applied in processing of peptidomics data based on HPLC-MS from cerebral infarction and breast cancer patients blood samples, respectively. And, it was found that almost all peaks contained in chromatograms could be picked out by XCMS, but the areas of these peaks were greatly different from those given by manual integration. Furthermore, t-test (2-tailed) results of the two data processing procedures were also different and different potential biomarkers were obtained. The results of this work will provide helpful reference for data processing of peptidomics research.

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Fortier, Marie-Hélène, Étienne Caron, Marie-Pierre Hardy, Grégory Voisin, Sébastien Lemieux, Claude Perreault, and Pierre Thibault. "The MHC class I peptide repertoire is molded by the transcriptome." Journal of Experimental Medicine 205, no. 3 (February 25, 2008): 595–610. http://dx.doi.org/10.1084/jem.20071985.

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Under steady-state conditions, major histocompatibility complex (MHC) I molecules are associated with self-peptides that are collectively referred to as the MHC class I peptide (MIP) repertoire. Very little is known about the genesis and molecular composition of the MIP repertoire. We developed a novel high-throughput mass spectrometry approach that yields an accurate definition of the nature and relative abundance of unlabeled peptides presented by MHC I molecules. We identified 189 and 196 MHC I–associated peptides from normal and neoplastic mouse thymocytes, respectively. By integrating our peptidomic data with global profiling of the transcriptome, we reached two conclusions. The MIP repertoire of primary mouse thymocytes is biased toward peptides derived from highly abundant transcripts and is enriched in peptides derived from cyclins/cyclin-dependent kinases and helicases. Furthermore, we found that ∼25% of MHC I–associated peptides were differentially expressed on normal versus neoplastic thymocytes. Approximately half of those peptides are derived from molecules directly implicated in neoplastic transformation (e.g., components of the PI3K–AKT–mTOR pathway). In most cases, overexpression of MHC I peptides on cancer cells entailed posttranscriptional mechanisms. Our results show that high-throughput analysis and sequencing of MHC I–associated peptides yields unique insights into the genesis of the MIP repertoire in normal and neoplastic cells.

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Labas, Valérie, Lucie Spina, Clémence Belleannee, Ana-Paula Teixeira-Gomes, Audrey Gargaros, Françoise Dacheux, and Jean-Louis Dacheux. "Data in support of peptidomic analysis of spermatozoa during epididymal maturation." Data in Brief 1 (December 2014): 79–84. http://dx.doi.org/10.1016/j.dib.2014.10.003.

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4

Schrader, Michael, and Hartmut Selle. "The Process Chain for Peptidomic Biomarker Discovery." Disease Markers 22, no. 1-2 (2006): 27–37. http://dx.doi.org/10.1155/2006/174849.

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Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.

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Abdelati, Abeer A., Rehab A. Elnemr, Noha S. Kandil, Fatma I. Dwedar, and Rasha A. Ghazala. "Serum Peptidomic Profile as a Novel Biomarker for Rheumatoid Arthritis." International Journal of Rheumatology 2020 (August 3, 2020): 1–10. http://dx.doi.org/10.1155/2020/6069484.

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Over the last decades, there has been an increasing need to discover new diagnostic RA biomarkers, other than the current serologic biomarkers, which can assist early diagnosis and response to treatment. The purpose of this study was to analyze the serum peptidomic profile in patients with rheumatoid arthritis (RA) by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The study included 35 patients with rheumatoid arthritis (RA), 35 patients with primary osteoarthritis (OA) as the disease control (DC), and 35 healthy controls (HC). All participants were subjected to serum peptidomic profile analysis using magnetic bead (MB) separation (MALDI-TOF-MS). The trial showed 113 peaks that discriminated RA from OA and 101 peaks that discriminated RA from HC. Moreover, 95 peaks were identified and discriminated OA from HC; 38 were significant (p<0.05) and 57 nonsignificant. The genetic algorithm (GA) model showed the best sensitivity and specificity in the three trials (RA versus HC, OA versus HC, and RA versus OA). The present data suggested that the peptidomic pattern is of value for differentiating individuals with RA from OA and healthy controls. We concluded that MALDI-TOF-MS combined with MB is an effective technique to identify novel serum protein biomarkers related to RA.

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Fortier, Marie-Hélène, Etienne Caron, Marie-Pierre Hardy, Grégory Voisin, Sébastien Lemieux, Claude Perreault, and Pierre Thibault. "The MHC I Immunopeptidome Is Moulded by the Transcriptome and Conceals a Tissue-Specific Signature." Blood 110, no. 11 (November 16, 2007): 1327. http://dx.doi.org/10.1182/blood.v110.11.1327.1327.

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Abstract Background: Cell surface MHC I molecules are associated with self peptides that are collectively referred to as the self MHC I immunopeptidome (sMII). The sMII plays vital roles: it shapes the repertoire of developing thymocytes, transmits survival signals to mature CD8 T cells, amplifies responses against intracellular pathogens, allows immunosurveillance of neoplastic cells, and influences mating preferences in mice. Despite the tremendous importance of the sMII, very little is known on its genesis and molecular composition. Methodology/Principal Findings: We developed a novel high-throughput mass spectrometry approach that yields an accurate definition of the nature and relative abundance of unlabeled peptides presented by MHC I molecules. Two major points emerged from a comprehensive analysis of the sMII of primary mouse thymocytes: the sMII is enriched in peptides derived from highly abundant transcripts; and the sMII conceals a tissue-specific signature that emanates from about 17% of genes represented in the sMII. We found that about 25% of MHC I-associated peptides were differentially expressed on normal versus neoplastic thymocytes. Remarkably, about half of those peptides derived from molecules implicated in neoplastic transformation. Integration of peptidomic and transcriptomic data unveiled that, in most cases, overexpression of MHC I peptides on cancer cells entailed posttranscriptional mechanisms. Finally, mice immunized against peptides overexpressed by 10 to ≥ 85 fold on cancer cells generated specific cytotoxic T-cell responses against malignant cells endogenously expressing the target epitope. Conclusion: High-throughput analysis and sequencing of MHC I-associated peptides yields unique insights into the genesis of the sMII in normal and neoplastic cells, and can be used to discover peptide targets for cancer immunotherapy. Furthermore, global portrayal of the sMII offers a novel perspective into how neoplastic transformation affects protein metabolism. Figure 1. Relative Quantification of Differentially Expressed MHC I peptides and Source mRNAs from Thymocytes and EL4 Cells (A) Volcano Plot representation illustrate MHC I peptides reproducibly detected across biological replicates (n = 3). Peptides over- and underexpressed on EL4 cells relative to thymocytes (p-values≤0.05; fold change ≥ 2.5) were highlighted in blue and red, respectively. MS-MS spectra of circled peptides are shown in B and C. B) Scatter plot shows the correlation between relative expression of mRNA and that of MHC I peptide. Expression ratios for source mRNA (x axis) and MHC I peptide (y axis) between EL4 cells and thymocytes were plotted on a log 2 scale for 47 pairs. A Spearman correlation coefficient was calculated from the linear regression. MHC I peptides overexpressed in EL4 cells or normal thymocytes are highlighted in blue and red, respectively; peptides that were not differentially expressed are in grey. Dashed box includes peptides whose overexpression on EL4 cells did not correlated with increased mRNA levels of their source protein. Figure 1. Relative Quantification of Differentially Expressed MHC I peptides and Source mRNAs from Thymocytes and EL4 Cells (A) Volcano Plot representation illustrate MHC I peptides reproducibly detected across biological replicates (n = 3). Peptides over- and underexpressed on EL4 cells relative to thymocytes (p-values≤0.05; fold change ≥ 2.5) were highlighted in blue and red, respectively. MS-MS spectra of circled peptides are shown in B and C. B) Scatter plot shows the correlation between relative expression of mRNA and that of MHC I peptide. Expression ratios for source mRNA (x axis) and MHC I peptide (y axis) between EL4 cells and thymocytes were plotted on a log 2 scale for 47 pairs. A Spearman correlation coefficient was calculated from the linear regression. MHC I peptides overexpressed in EL4 cells or normal thymocytes are highlighted in blue and red, respectively; peptides that were not differentially expressed are in grey. Dashed box includes peptides whose overexpression on EL4 cells did not correlated with increased mRNA levels of their source protein.

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7

Miralles, B., J. Sanchón, L. Sánchez-Rivera, D. Martínez-Maqueda, Y. Le Gouar, D. Dupont, L. Amigo, and I. Recio. "Peptidomic data in porcine duodenal effluents after oral administration of micellar casein." Data in Brief 38 (October 2021): 107326. http://dx.doi.org/10.1016/j.dib.2021.107326.

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8

Sheng, Pijie, Minyan Xu, Zhenzhen Zheng, Xiaojing Liu, Wanlu Ma, Ting Ding, Chenchen Zhang, et al. "Peptidome and Transcriptome Analysis of Plant Peptides Involved in Bipolaris maydis Infection of Maize." Plants 12, no. 6 (March 14, 2023): 1307. http://dx.doi.org/10.3390/plants12061307.

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Southern corn leaf blight (SCLB) caused by Bipolaris maydis threatens maize growth and yield worldwide. In this study, TMT-labeled comparative peptidomic analysis was established between infected and uninfected maize leaf samples using liquid-chromatography-coupled tandem mass spectrometry. The results were further compared and integrated with transcriptome data under the same experimental conditions. Plant peptidomic analysis identified 455 and 502 differentially expressed peptides (DEPs) in infected maize leaves on day 1 and day 5, respectively. A total of 262 common DEPs were identified in both cases. Bioinformatic analysis indicated that the precursor proteins of DEPs are associated with many pathways generated by SCLB-induced pathological changes. The expression profiles of plant peptides and genes in maize plants were considerably altered after B. maydis infection. These findings provide new insights into the molecular mechanisms of SCLB pathogenesis and offer a basis for the development of maize genotypes with SCLB resistance.

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9

Santos-Hernández, Marta, Beatriz Miralles, Lourdes Amigo, and Isidra Recio. "Peptidomic data of egg white gastrointestinal digests prepared using the Infogest Harmonized Protocol." Data in Brief 31 (August 2020): 105932. http://dx.doi.org/10.1016/j.dib.2020.105932.

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10

Mostovenko, Ekaterina, Samantha Saunders, Pretal P. Muldoon, Lindsey Bishop, Matthew J. Campen, Aaron Erdely, and Andrew K. Ottens. "Carbon Nanotube Exposure Triggers a Cerebral Peptidomic Response: Barrier Compromise, Neuroinflammation, and a Hyperexcited State." Toxicological Sciences 182, no. 1 (April 21, 2021): 107–19. http://dx.doi.org/10.1093/toxsci/kfab042.

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Abstract The unique physicochemical properties of carbon nanomaterials and their ever-growing utilization generate a serious concern for occupational risk. Pulmonary exposure to these nanoparticles induces local and systemic inflammation, cardiovascular dysfunction, and even cognitive deficits. Although multiple routes of extrapulmonary toxicity have been proposed, the mechanism for and manner of neurologic effects remain minimally understood. Here, we examine the cerebral spinal fluid (CSF)-derived peptidomic fraction as a reflection of neuropathological alterations induced by pulmonary carbon nanomaterial exposure. Male C57BL/6 mice were exposed to 10 or 40 µg of multiwalled carbon nanotubes (MWCNT) by oropharyngeal aspiration. Serum and CSFs were collected 4 h post exposure. An enriched peptide fraction of both biofluids was analyzed using ion mobility-enabled data-independent mass spectrometry for label-free quantification. MWCNT exposure induced a prominent peptidomic response in the blood and CSF; however, correlation between fluids was limited. Instead, we determined that a MWCNT-induced peptidomic shift occurred specific to the CSF with 292 significant responses found that were not in serum. Identified MWCNT-responsive peptides depicted a mechanism involving aberrant fibrinolysis (fibrinopeptide A), blood-brain barrier permeation (homeobox protein A4), neuroinflammation (transmembrane protein 131L) with reactivity by astrocytes and microglia, and a pro-degradative (signal transducing adapter molecule, phosphoglycerate kinase), antiplastic (AF4/FMR2 family member 1, vacuolar protein sorting-associated protein 18) state with the excitation-inhibition balance shifted to a hyperexcited (microtubule-associated protein 1B) phenotype. Overall, the significant pathologic changes observed were consistent with early neurodegenerative disease and were diagnostically reflected in the CSF peptidome.

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11

Mao, Fan, Yongbo Bao, Nai-Kei Wong, Minwei Huang, Kunna Liu, Xiangyu Zhang, Zhuo Yang, et al. "Large-Scale Plasma Peptidomic Profiling Reveals a Novel, Nontoxic, Crassostrea hongkongensis-Derived Antimicrobial Peptide against Foodborne Pathogens." Marine Drugs 19, no. 8 (July 26, 2021): 420. http://dx.doi.org/10.3390/md19080420.

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Antimicrobial peptides are a fundamental component of mollusks’ defense systems, though they remain a thinly investigated subject. Here, infection by Vibrio parahemolyticus triggered a significant increase in antimicrobial activity in oyster plasma. By using PBS-challenged oysters as a control, plasma peptides from immunologically challenged oysters were subjected to peptidomic profiling and in silico data mining to identify bioactive peptides. Thirty-five identified plasma peptides were up-regulated post infection, among which, six up-regulated peptides (URPs) showed a relatively high positive charge. URP20 was validated with significant antibacterial activity. Virtually, URP20 triggered aggregation of bacterial cells, accompanied by their membrane permeabilization. Interestingly, URP20 was found to be active against Gram-positive and Gram-negative foodborne pathogens as well as Candida albicans, with no cytotoxicity to mammalian cells and mice. Our study provides the first large-scale plasma peptidomic dataset that identifies novel bioactive peptides in marine mollusks. Further exploration of peptide diversity in marine invertebrates should prove a fruitful pursuit for designing novel AMPs with broad applications.

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12

Baldanzi, Gianluca, Beatrice Purghè, Beatrice Ragnoli, Pier Paolo Sainaghi, Roberta Rolla, Annalisa Chiocchetti, Marcello Manfredi, and Mario Malerba. "Circulating Peptidome Is Strongly Altered in COVID-19 Patients." International Journal of Environmental Research and Public Health 20, no. 2 (January 14, 2023): 1564. http://dx.doi.org/10.3390/ijerph20021564.

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Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.

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13

Huang, Qi-Fang, Zhen-Yu Zhang, Jan Van Keer, Sander Trenson, Esther Nkuipou-Kenfack, Wen-Yi Yang, Lutgarde Thijs, et al. "Urinary peptidomic biomarkers of renal function in heart transplant recipients." Nephrology Dialysis Transplantation 34, no. 8 (July 2, 2018): 1336–43. http://dx.doi.org/10.1093/ndt/gfy185.

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AbstractBackgroundChronic kidney disease (CKD) is common in patients after heart transplantation (HTx). We assessed whether in HTx recipients the proteomic urinary classifier CKD273 or sequenced urinary peptides revealing the parental proteins correlated with the estimated glomerular filtration rate (eGFR).MethodsIn 368 HTx patients, we measured the urinary peptidome and analysed CKD273 and 48 urinary peptides with a detectable signal in >95% of participants. After 9.1 months (median), eGFR and the urinary biomarkers were reassessed.ResultsIn multivariable Bonferroni-corrected analyses of the baseline data, a 1-SD increase in CKD273 was associated with a 11.4 [95% confidence interval (CI) 7.25–15.5] mL/min/1.73 m2 lower eGFR and an odds ratio of 2.63 (1.56–4.46) for having eGFR <60 mL/min/1.73 m2. While relating eGFR category at follow-up to baseline urinary biomarkers, CKD273 had higher (P = 0.007) area under the curve (0.75; 95% CI 0.70–0.80) than 24-h proteinuria (0.64; 95% CI 0.58–0.69), but additional adjustment for baseline eGFR removed significance of both biomarkers. In partial least squares analysis, the strongest correlates of the multivariable-adjusted baseline eGFR were fragments of collagen I (positive) and the mucin-1 subunit α (inverse). Associations between the changes in eGFR and the urinary markers were inverse for CKD273 and mucin-1 and positive for urinary collagen I.ConclusionsWith the exception of baseline eGFR, CKD273 was more closer associated with imminent renal dysfunction than 24-h proteinuria. Fragments of collagen I and mucin-1—respectively, positively and inversely associated with eGFR and change in eGFR—are single-peptide markers associated with renal dysfunction.

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Olmsted, Aspen. "Heterogeneous system integration data integration guarantees." Journal of Computational Methods in Sciences and Engineering 17 (January 19, 2017): S85—S94. http://dx.doi.org/10.3233/jcm-160682.

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15

NASSIRI, Hassana. "Data Model Integration." International Journal of New Computer Architectures and their Applications 7, no. 2 (2017): 45–49. http://dx.doi.org/10.17781/p002327.

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Miller, Renée J. "Open data integration." Proceedings of the VLDB Endowment 11, no. 12 (August 2018): 2130–39. http://dx.doi.org/10.14778/3229863.3240491.

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Dong, Xin Luna, and Divesh Srivastava. "Big data integration." Proceedings of the VLDB Endowment 6, no. 11 (August 27, 2013): 1188–89. http://dx.doi.org/10.14778/2536222.2536253.

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Dong, Xin Luna, and Divesh Srivastava. "Big Data Integration." Synthesis Lectures on Data Management 7, no. 1 (February 15, 2015): 1–198. http://dx.doi.org/10.2200/s00578ed1v01y201404dtm040.

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Vargas-Vera, Maria. "Data Integration Framework." International Journal of Knowledge Society Research 7, no. 1 (January 2016): 99–112. http://dx.doi.org/10.4018/ijksr.2016010107.

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This paper presents a proposal for a data integration framework. The purpose of the framework is to locate automatically records of participants from the ALSPAC database (Avon Longitudinal Study of Parents and Children) within its counterpart GPRD database (General Practice Research Database). The ALSPAC database is a collection of data from children and parents from before birth to late puberty. This collection contains several variables of interest for clinical researchers but we concentrate in asthma as a golden standard for evaluation of asthma has been made by a clinical researcher. The main component of the framework is a module called Mapper which locates similar records and performs record linkage. The mapper contains a library of similarity measures such Jaccard, Jaro-Winkler, Monge-Elkan, MatchScore, Levenstein and TFIDF similarity. Finally, the author evaluates the approach on quality of the mappings.

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Slater, Ted, Christopher Bouton, and Enoch S. Huang. "Beyond data integration." Drug Discovery Today 13, no. 13-14 (July 2008): 584–89. http://dx.doi.org/10.1016/j.drudis.2008.01.008.

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21

Tang, Lin. "Genomics data integration." Nature Methods 20, no. 1 (January 2023): 34. http://dx.doi.org/10.1038/s41592-022-01736-4.

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Bakshi, Waseem Jeelani, Rana Hashmy, Majid Zaman, and Muheet Ahmed Butt. "Logical Data Integration Model for the Integration of Data Repositories." International Journal of Database Theory and Application 11, no. 1 (March 31, 2018): 21–28. http://dx.doi.org/10.14257/ijdta.2018.11.1.03.

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23

CALVANESE, DIEGO, GIUSEPPE DE GIACOMO, MAURIZIO LENZERINI, DANIELE NARDI, and RICCARDO ROSATI. "DATA INTEGRATION IN DATA WAREHOUSING." International Journal of Cooperative Information Systems 10, no. 03 (September 2001): 237–71. http://dx.doi.org/10.1142/s0218843001000345.

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Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

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Chromiak, Michal, and Marcin Grabowiecki. "Heterogeneous Data Integration Architecture-Challenging Integration Issues." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 15, no. 1 (January 1, 2015): 7. http://dx.doi.org/10.17951/ai.2015.15.1.7-11.

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As of today, most of the data processing systems have to deal with a large amount of data originated from numerous sources. Data sources almost always differ regarding its purpose of existence. Thus model, data processing engine and technology differ intensely. Due to current trend for systems fusion there is a growing demand for data to be present in a common way regardless of its legacy. Many systems have been devised as a response to such integration needs. However, the present data integration systems mostly are dedicated solutions that bring constraints and issues when considered in general. In this paper we will focus on the present solutions for data integration, their flaws originating from their architecture or design concepts and present an abstract and general approach that could be introduced as an response to existing issues. The system integration is considered out of scope for this paper, we will focus particularly on efficient data integration.

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Catanese, Lorenzo, Justyna Siwy, Emmanouil Mavrogeorgis, Kerstin Amann, Harald Mischak, Joachim Beige, and Harald Rupprecht. "A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease." Proteomes 9, no. 3 (July 13, 2021): 32. http://dx.doi.org/10.3390/proteomes9030032.

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Non-invasive urinary peptide biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree of renal interstitial fibrosis and tubular atrophy (IFTA) in CKD patients. The urinary peptide profiles of 435 patients enrolled in this study were analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n = 200) and a test (n = 235) cohort. The fibrosis group was defined as IFTA ≥ 15% and no fibrosis as IFTA < 10%. Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides with differential occurrence in samples with and without fibrosis. Several collagen fragments and peptide fragments of fetuin-A and others were combined into a peptidomic classifier. The classifier separated fibrosis from non-fibrosis patients in an independent test set (n = 186) with area under the curve (AUC) of 0.84 (95% CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed Rho = 0.5, p < 0.0001. We identified a peptidomic classifier for renal fibrosis containing 29 peptide fragments corresponding to 13 different proteins. Urinary proteomics analysis can serve as a non-invasive tool to evaluate the degree of renal fibrosis, in contrast to kidney biopsy, which allows repeated measurements during the disease course.

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Bernasconi, Anna. "Data quality-aware genomic data integration." Computer Methods and Programs in Biomedicine Update 1 (2021): 100009. http://dx.doi.org/10.1016/j.cmpbup.2021.100009.

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Salinas, Sonia Ordonez, and Alba Consuelo Nieto Lemus. "Data Warehouse and Big Data Integration." International Journal of Computer Science and Information Technology 9, no. 2 (April 30, 2017): 01–17. http://dx.doi.org/10.5121/ijcsit.2017.9201.

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Bernstein, Philip A. "Data Integration for Data-Intensive Science." OMICS: A Journal of Integrative Biology 15, no. 4 (April 2011): 241. http://dx.doi.org/10.1089/omi.2011.0020.

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Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (February 10, 2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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30

Lu, James J. "A Data Model for Data Integration." Electronic Notes in Theoretical Computer Science 150, no. 2 (March 2006): 3–19. http://dx.doi.org/10.1016/j.entcs.2005.11.031.

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31

Curcin, V., A. Barton, M. M. McGilchrist, H. Bastiaens, A. Andreasson, J. Rossiter, L. Zhao, et al. "Clinical Data Integration Model." Methods of Information in Medicine 54, no. 01 (2015): 16–23. http://dx.doi.org/10.3414/me13-02-0024.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance.Objectives: TRANSFoRm’s general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care.Methods: TRANSFoRm utilizes a unified structural / terminological interoperability frame work, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care.Results: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm’s use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM.Conclusion: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

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Neang, Andrew B., Will Sutherland, Michael W. Beach, and Charlotte P. Lee. "Data Integration as Coordination." Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (January 5, 2021): 1–25. http://dx.doi.org/10.1145/3432955.

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Bertino, E., and E. Ferrari. "XML and data integration." IEEE Internet Computing 5, no. 6 (2001): 75–76. http://dx.doi.org/10.1109/4236.968835.

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Di Lorenzo, Giusy, Hakim Hacid, Hye-young Paik, and Boualem Benatallah. "Data integration in mashups." ACM SIGMOD Record 38, no. 1 (June 24, 2009): 59–66. http://dx.doi.org/10.1145/1558334.1558343.

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Pineda, Silvia, Daniel G. Bunis, Idit Kosti, and Marina Sirota. "Data Integration for Immunology." Annual Review of Biomedical Data Science 3, no. 1 (July 20, 2020): 113–36. http://dx.doi.org/10.1146/annurev-biodatasci-012420-122454.

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Over the last several years, next-generation sequencing and its recent push toward single-cell resolution have transformed the landscape of immunology research by revealing novel complexities about all components of the immune system. With the vast amounts of diverse data currently being generated, and with the methods of analyzing and combining diverse data improving as well, integrative systems approaches are becoming more powerful. Previous integrative approaches have combined multiple data types and revealed ways that the immune system, both as a whole and as individual parts, is affected by genetics, the microbiome, and other factors. In this review, we explore the data types that are available for studying immunology with an integrative systems approach, as well as the current strategies and challenges for conducting such analyses.

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Kaufman, G. "Pragmatic ECAD Data Integration." ACM SIGDA Newsletter 20, no. 1 (June 1990): 60–81. http://dx.doi.org/10.1145/378886.1062259.

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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (January 1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.

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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

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Brazhnik, Olga, and John F. Jones. "Anatomy of data integration." Journal of Biomedical Informatics 40, no. 3 (June 2007): 252–69. http://dx.doi.org/10.1016/j.jbi.2006.09.001.

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Powell, V. J. H., and A. Acharya. "Disease Prevention: Data Integration." Science 338, no. 6112 (December 6, 2012): 1285–86. http://dx.doi.org/10.1126/science.338.6112.1285-b.

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Riedemann, Catharina, and Christian Timm. "Services for data integration." Data Science Journal 2 (2003): 90–99. http://dx.doi.org/10.2481/dsj.2.90.

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Kezunovic, M. "Integration of Substation Data." IFAC Proceedings Volumes 44, no. 1 (January 2011): 12861–66. http://dx.doi.org/10.3182/20110828-6-it-1002.02654.

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Resnick, Richard J. "Data Integration in Genomics." Biotech Software & Internet Report 1, no. 1-2 (April 2000): 40–43. http://dx.doi.org/10.1089/152791600319268.

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Larsen, N., R. Overbeek, S. Pramanik, T. M. Schmidt, E. E. Selkov, O. Strunk, J. M. Tiedje, and J. W. Urbance. "Towards microbial data integration." Journal of Industrial Microbiology and Biotechnology 18, no. 1 (January 1, 1997): 68–72. http://dx.doi.org/10.1038/sj.jim.2900366.

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Almeida, Jonas S., Chuming Chen, Robert Gorlitsky, Romesh Stanislaus, Marta Aires-de-Sousa, Pedro Eleutério, João Carriço, et al. "Data integration gets 'Sloppy'." Nature Biotechnology 24, no. 9 (September 1, 2006): 1070–71. http://dx.doi.org/10.1038/nbt0906-1070.

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Dong, Xin Luna, Alon Halevy, and Cong Yu. "Data integration with uncertainty." VLDB Journal 18, no. 2 (November 14, 2008): 469–500. http://dx.doi.org/10.1007/s00778-008-0119-9.

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Sivertsen, Gunnar. "Data integration in Scandinavia." Scientometrics 106, no. 2 (December 22, 2015): 849–55. http://dx.doi.org/10.1007/s11192-015-1817-x.

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Meyer, Ingo. "Data matters - no service integration without data integration: a transnational learning exercise." International Journal of Integrated Care 21, S1 (September 1, 2021): 28. http://dx.doi.org/10.5334/ijic.icic20545.

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Lin, Lin, Jiaxin Zheng, Fangjian Zheng, Zonglong Cai, and Quan Yu. "Advancing serum peptidomic profiling by data-independent acquisition for clear-cell renal cell carcinoma detection and biomarker discovery." Journal of Proteomics 215 (March 2020): 103671. http://dx.doi.org/10.1016/j.jprot.2020.103671.

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Patel, Jayesh. "Bridging Data Silos Using Big Data Integration." International Journal of Database Management Systems 11, no. 3 (June 29, 2019): 01–06. http://dx.doi.org/10.5121/ijdms.2019.11301.

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Kim, Seungwhan, and Sunghae Jun. "Big Data Integration using Data De-identification." Journal of Korean Institute of Intelligent Systems 29, no. 3 (June 30, 2019): 235–41. http://dx.doi.org/10.5391/jkiis.2019.29.3.235.

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