Kelli HM, Kassas I. Cardio metabolic syndrome: a global epidemic. J Diabetes Metab. 2016;6(3):2–14.
Google Scholar
Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation. 2014;129(3):e28-292.
PubMed
Google Scholar
Leal J, Luengo-Fernández R, Gray A, Petersen S, Rayner M. Economic burden of cardiovascular diseases in the enlarged European Union. Eur Heart J. 2006;27(13):1610–9.
PubMed
Google Scholar
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52.
PubMed
Google Scholar
Azzu V, Vacca M, Virtue S, Allison M, Vidal-Puig A. Adipose tissue-liver cross talk in the control of whole-body metabolism: implications in non-alcoholic fatty liver disease. Gastroenterology. 2020. https://doi.org/10.1053/j.gastro.2019.12.054.
Article
PubMed
Google Scholar
Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5.
CAS
PubMed
Google Scholar
Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–7.
CAS
PubMed
Google Scholar
Stienstra R, Stefan N. Tipping the inflammatory balance: inflammasome activation distinguishes metabolically unhealthy from healthy obesity. Diabetologia. 2013;56(11):2343–6.
CAS
PubMed
Google Scholar
Shimada YJ, Gibo K, Tsugawa Y, Goto T, Yu EW, Iso H, et al. Bariatric surgery is associated with lower risk of acute care use for cardiovascular disease in obese adults. Cardiovasc Res. 2019;115(4):800–6.
CAS
PubMed
Google Scholar
Pahan K. Lipid-lowering drugs. Cell Mol Life Sci. 2006;63(10):1165–78.
CAS
PubMed
PubMed Central
Google Scholar
Majithia A, Bhatt DL. Novel antiplatelet therapies for atherothrombotic diseases. Arterioscler Thromb Vasc Biol. 2019;39(4):546–57.
CAS
PubMed
PubMed Central
Google Scholar
Sanchez-Rangel E, Inzucchi SE. Metformin: clinical use in type 2 diabetes. Diabetologia. 2017;60(9):1586–93.
CAS
PubMed
Google Scholar
Drucker DJ, Nauck MA. The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet. 2006;368(9548):1696–705.
CAS
PubMed
Google Scholar
Kosmas CE, Silverio D, Sourlas A, Montan PD, Guzman E, Garcia MJ. Anti-inflammatory therapy for cardiovascular disease. Ann Transl Med. 2019;7(7):147.
CAS
PubMed
PubMed Central
Google Scholar
Stefan N, Schick F, Häring H-U. Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab. 2017;26(2):292–300.
CAS
PubMed
Google Scholar
Kip KE, Marroquin OC, Kelley DE, Johnson BD, Kelsey SF, Shaw LJ, et al. Clinical importance of obesity versus the metabolic syndrome in cardiovascular risk in women: a report from the Women’s Ischemia Syndrome Evaluation (WISE) study. Circulation. 2004;109(6):706–13.
PubMed
Google Scholar
St-Pierre AC, Cantin B, Mauriège P, Bergeron J, Dagenais GR, Després J-P, et al. Insulin resistance syndrome, body mass index and the risk of ischemic heart disease. CMAJ. 2005;172(10):1301–5.
PubMed
PubMed Central
Google Scholar
Katzmarzyk PT, Janssen I, Ross R, Church TS, Blair SN. The importance of waist circumference in the definition of metabolic syndrome: prospective analyses of mortality in men. Diabetes Care. 2006;29(2):404–9.
PubMed
Google Scholar
Nichols GA, Horberg M, Koebnick C, Young DR, Waitzfelder B, Sherwood NE, et al. Cardiometabolic risk factors among 1.3 million adults with overweight or obesity, but not diabetes, in 10 geographically diverse regions of the United States, 2012–2013. Prev Chronic Dis. 2017;14:E22–31.
PubMed
PubMed Central
Google Scholar
Virtue S, Vidal-Puig A. Adipose tissue expandability, lipotoxicity and the metabolic syndrome—an allostatic perspective. Biochim Biophys Acta. 2010;1801(3):338–49.
CAS
PubMed
Google Scholar
Choe SS, Huh JY, Hwang IJ, Kim JI, Kim JB. Adipose tissue remodeling: its role in energy metabolism and metabolic disorders. Front Endocrinol. 2016;7:30.
Google Scholar
Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science. 1993;259(5091):87–91.
CAS
PubMed
Google Scholar
Vishvanath L, Gupta RK. Contribution of adipogenesis to healthy adipose tissue expansion in obesity. J Clin Invest. 2019;129(10):4022–31.
PubMed
PubMed Central
Google Scholar
Ramirez GA, Manfredi AA, Maugeri N. Misunderstandings between platelets and neutrophils build in chronic inflammation. Front Immunol. 2019;10:2491.
CAS
PubMed
PubMed Central
Google Scholar
Puhr-Westerheide D, Schink SJ, Fabritius M, Mittmann L, Hessenauer MET, Pircher J, et al. Neutrophils promote venular thrombosis by shaping the rheological environment for platelet aggregation. Sci Rep. 2019;9(1):15932.
PubMed
PubMed Central
Google Scholar
Bobryshev YV, Ivanova EA, Chistiakov DA, Nikiforov NG, Orekhov AN. Macrophages and their role in atherosclerosis: pathophysiology and transcriptome analysis. Biomed Res Int. 2016;2016:9582430.
PubMed
PubMed Central
Google Scholar
Nording HM, Seizer P, Langer HF. Platelets in inflammation and atherogenesis. Front Immunol. 2015;6:98.
PubMed
PubMed Central
Google Scholar
van Tuijl J, Joosten LAB, Netea MG, Bekkering S, Riksen NP. Immunometabolism orchestrates training of innate immunity in atherosclerosis. Cardiovasc Res. 2019;115(9):1416–24.
PubMed
PubMed Central
Google Scholar
Gros A, Ollivier V, Ho-Tin-Noé B. Platelets in inflammation: regulation of leukocyte activities and vascular repair. Front Immunol. 2014;5:678.
PubMed
Google Scholar
Koupenova M, Clancy L, Corkrey HA, Freedman JE. Circulating platelets as mediators of immunity, inflammation, and thrombosis. Circ Res. 2018;122(2):337–51.
CAS
PubMed
PubMed Central
Google Scholar
Caielli S, Banchereau J, Pascual V. Neutrophils come of age in chronic inflammation. Curr Opin Immunol. 2012;24(6):671–7.
CAS
PubMed
PubMed Central
Google Scholar
Wright HL, Moots RJ, Bucknall RC, Edwards SW. Neutrophil function in inflammation and inflammatory diseases. Rheumatology. 2010;49(9):1618–31.
CAS
PubMed
Google Scholar
Ghosh S, Dent R, Harper M-E, Gorman SA, Stuart JS, McPherson R. Gene expression profiling in whole blood identifies distinct biological pathways associated with obesity. BMC Med Genomics. 2010;3:56.
PubMed
PubMed Central
Google Scholar
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.
CAS
PubMed
Google Scholar
Søndergaard E, Espinosa De Ycaza AE, Morgan-Bathke M, Jensen MD. How to measure adipose tissue insulin sensitivity. J Clin Endocrinol Metab. 2017;102(4):1193–9.
PubMed
PubMed Central
Google Scholar
Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33.
PubMed
PubMed Central
Google Scholar
Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317–25.
CAS
PubMed
Google Scholar
Artigao-Rodenas LM, Carbayo-Herencia JA, Divisón-Garrote JA, Gil-Guillén VF, Massó-Orozco J, Simarro-Rueda M, et al. Framingham risk score for prediction of cardiovascular diseases: a population-based study from southern Europe. PLoS ONE. 2013;8(9):e73529.
CAS
PubMed
PubMed Central
Google Scholar
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–82.
PubMed
PubMed Central
Google Scholar
Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An overview and update on obesity and the obesity paradox in cardiovascular diseases. Prog Cardiovasc Dis. 2018;61(2):142–50.
PubMed
Google Scholar
Turro E, Astle WJ, Megy K, Gräf S, Greene D, Shamardina O, et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature. 2020;583(7814):96–102.
CAS
PubMed
PubMed Central
Google Scholar
Mann JP, Savage DB. What lipodystrophies teach us about the metabolic syndrome. J Clin Invest. 2019;129(10):4009–21.
PubMed
PubMed Central
Google Scholar
Chen L, Ge B, Casale FP, Vasquez L, Kwan T, Garrido-Martín D, et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell. 2016;167(5):1398-414.e24.
CAS
PubMed
PubMed Central
Google Scholar
Cirulli ET, Guo L, Leon Swisher C, Shah N, Huang L, Napier LA, et al. Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metab. 2019;29(2):488-500.e2.
CAS
PubMed
PubMed Central
Google Scholar
Moore SC, Matthews CE, Sampson JN, Stolzenberg-Solomon RZ, Zheng W, Cai Q, et al. Human metabolic correlates of body mass index. Metabolomics. 2014;10(2):259–69.
CAS
PubMed
Google Scholar
Fiorenza CG, Chou SH, Mantzoros CS. Lipodystrophy: pathophysiology and advances in treatment. Nat Rev Endocrinol. 2011;7(3):137–50.
CAS
PubMed
Google Scholar
Huang-Doran I, Sleigh A, Rochford JJ, O’Rahilly S, Savage DB. Lipodystrophy: metabolic insights from a rare disorder. J Endocrinol. 2010;207(3):245–55.
CAS
PubMed
Google Scholar
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4. https://doi.org/10.2202/1544-6115.1128.
Swystun LL, Liaw PC. The role of leukocytes in thrombosis. Blood. 2016;128(6):753–62.
CAS
PubMed
Google Scholar
Donkin I, Versteyhe S, Ingerslev LR, Qian K, Mechta M, Nordkap L, et al. Obesity and bariatric surgery drive epigenetic variation of spermatozoa in humans. Cell Metab. 2016;23(2):369–78.
CAS
PubMed
Google Scholar
Campbell LE, Langlais PR, Day SE, Coletta RL, Benjamin TR, De Filippis EA, et al. Identification of novel changes in human skeletal muscle proteome after Roux-en-Y gastric bypass surgery. Diabetes. 2016;65(9):2724–31.
CAS
PubMed
PubMed Central
Google Scholar
Kieffer-Kwon K-R, Tang Z, Mathe E, Qian J, Sung M-H, Li G, et al. Interactome maps of mouse gene regulatory domains reveal basic principles of transcriptional regulation. Cell. 2013;155(7):1507–20.
CAS
PubMed
Google Scholar
Quintin J, Saeed S, Martens JHA, Giamarellos-Bourboulis EJ, Ifrim DC, Logie C, et al. Candida albicans infection affords protection against reinfection via functional reprogramming of monocytes. Cell Host Microbe. 2012;12(2):223–32.
CAS
PubMed
Google Scholar
Kvaløy K, Page CM, Holmen TL. Epigenome-wide methylation differences in a group of lean and obese women—a HUNT study. Sci Rep. 2018;8(1):16330.
PubMed
PubMed Central
Google Scholar
Busetto L, Dicker D, Azran C, Batterham RL, Farpour-Lambert N, Fried M, et al. Practical Recommendations of the Obesity Management Task Force of the European Association for the study of obesity for the post-bariatric surgery medical management. Obes Facts. 2017;10(6):597–632.
PubMed
PubMed Central
Google Scholar
Adams TD, Davidson LE, Litwin SE, Kim J, Kolotkin RL, Nanjee MN, et al. Weight and metabolic outcomes 12 years after gastric bypass. N Engl J Med. 2017;377(12):1143–55.
PubMed
PubMed Central
Google Scholar
Raoux L, Moszkowicz D, Vychnevskaia K, Poghosyan T, Beauchet A, Clauser S, et al. Effect of bariatric surgery-induced weight loss on platelet count and mean platelet volume: a 12-month follow-up study. Obes Surg. 2017;27(2):387–93.
PubMed
Google Scholar
Periasamy M, Lieb DC, Butcher MJ, Kuhn N, Galkina E, Fontana M, et al. Bariatric surgery decreases monocyte-platelet aggregates in blood: a pilot study. Obes Surg. 2014;24(8):1410–4.
PubMed
Google Scholar
Horie T, Nishino T, Baba O, Kuwabara Y, Nakao T, Nishiga M, et al. MicroRNA-33 regulates sterol regulatory element-binding protein 1 expression in mice. Nat Commun. 2013;4:2883.
PubMed
Google Scholar
Singer K, DelProposto J, Morris DL, Zamarron B, Mergian T, Maley N, et al. Diet-induced obesity promotes myelopoiesis in hematopoietic stem cells. Mol Metab. 2014;3(6):664–75.
CAS
PubMed
PubMed Central
Google Scholar
Swanson KV, Deng M, Ting JP-Y. The NLRP3 inflammasome: molecular activation and regulation to therapeutics. Nat Rev Immunol. 2019;19(8):477–89.
CAS
PubMed
PubMed Central
Google Scholar
Lee WL, Grinstein S. Immunology. The tangled webs that neutrophils weave. Science. 2004;303(5663):1477–8.
CAS
PubMed
Google Scholar
GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60.
Rendo-Urteaga T, García-Calzón S, González-Muniesa P, Milagro FI, Chueca M, Oyarzabal M, et al. Peripheral blood mononuclear cell gene expression profile in obese boys who followed a moderate energy-restricted diet: differences between high and low responders at baseline and after the intervention. Br J Nutr. 2015;113(2):331–42.
CAS
PubMed
Google Scholar
Das SK, Ma L, Sharma NK. Adipose tissue gene expression and metabolic health of obese adults. Int J Obes. 2015;39(5):869–73.
CAS
Google Scholar
Brown AJ, Sepuru KM, Rajarathnam K. Structural basis of native CXCL7 monomer binding to CXCR2 receptor N-domain and glycosaminoglycan heparin. Int J Mol Sci. 2017. https://doi.org/10.3390/ijms18030508.
Article
PubMed
PubMed Central
Google Scholar
Kuan-Yu I, Huang Y-S, Hu C-H, Tseng W-Y, Cheng C-H, Stacey M, et al. Activation of adhesion GPCR EMR2/ADGRE2 induces macrophage differentiation and inflammatory responses via Gα16/Akt/MAPK/NF-κB signaling pathways. Front Immunol. 2017;8:463.
Google Scholar
Leentjens J, Bekkering S, Joosten LAB, Netea MG, Burgner DP, Riksen NP. Trained innate immunity as a novel mechanism linking infection and the development of atherosclerosis. Circ Res. 2018;122(5):664–9.
CAS
PubMed
Google Scholar
Bekkering S, Stiekema LCA, Bernelot Moens S, Verweij SL, Novakovic B, Prange K, et al. Treatment with statins does not revert trained immunity in patients with familial hypercholesterolemia. Cell Metab. 2019;30(1):1–2.
CAS
PubMed
Google Scholar
Brinkmann V. Neutrophil extracellular traps kill bacteria. Science. 2004;303:1532–5. https://doi.org/10.1126/science.1092385.
Article
CAS
PubMed
Google Scholar
Wang H, Wang Q, Venugopal J, Wang J, Kleiman K, Guo C, et al. Obesity-induced endothelial dysfunction is prevented by neutrophil extracellular trap inhibition. Sci Rep. 2018;8(1):4881.
PubMed
PubMed Central
Google Scholar
Cui B-B, Tan C-Y, Schorn C, Tang H-H, Liu Y, Zhao Y. Neutrophil extracellular traps in sterile inflammation: the story after dying? Autoimmunity. 2012;45(8):593–6.
CAS
PubMed
Google Scholar
Gavillet M, Martinod K, Renella R, Wagner DD, Williams DA. A key role for Rac and Pak signaling in neutrophil extracellular traps (NETs) formation defines a new potential therapeutic target. Am J Hematol. 2018;93(2):269–76.
CAS
PubMed
Google Scholar
Gérard A, Patino-Lopez G, Beemiller P, Nambiar R, Ben-Aissa K, Liu Y, et al. Detection of rare antigen-presenting cells through T cell-intrinsic meandering motility, mediated by Myo1g. Cell. 2014;158(3):492–505.
PubMed
PubMed Central
Google Scholar
Lood C, Arve S, Ledbetter J, Elkon KB. TLR7/8 activation in neutrophils impairs immune complex phagocytosis through shedding of FcgRIIA. J Exp Med. 2017;214(7):2103–19.
CAS
PubMed
PubMed Central
Google Scholar
Liu J, Liang G, Siegmund KD, Lewinger JP. Data integration by multi-tuning parameter elastic net regression. BMC Bioinformatics. 2018;19(1):369.
CAS
PubMed
PubMed Central
Google Scholar
Wu C, Zhou F, Ren J, Li X, Jiang Y, Ma S. A selective review of multi-level omics data integration using variable selection. High Throughput. 2019. https://doi.org/10.3390/ht8010004.
Article
PubMed
PubMed Central
Google Scholar
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Stat Methodol). 2005;67:301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
Article
Google Scholar
Murphy KP. Machine learning: a probabilistic perspective. Cambridge: MIT Press; 2012. p. 1067.
Google Scholar
Lindsay T, Westgate K, Wijndaele K, Hollidge S, Kerrison N, Forouhi N, et al. Descriptive epidemiology of physical activity energy expenditure in UK adults (The Fenland study). Int J Behav Nutr Phys Act. 2019;16(1):126.
PubMed
PubMed Central
Google Scholar
Hall Z, Bond NJ, Ashmore T, Sanders F, Ament Z, Wang X, et al. Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease. Hepatology. 2017;65(4):1165–80.
CAS
PubMed
Google Scholar
Sanders FWB, Acharjee A, Walker C, Marney L, Roberts LD, Imamura F, et al. Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption. Genome Biol. 2018;19(1):79.
PubMed
PubMed Central
Google Scholar
Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26.
CAS
PubMed
PubMed Central
Google Scholar
Tarkin JM, Joshi FR, Evans NR, Chowdhury MM, Figg NL, Shah AV, et al. Detection of atherosclerotic inflammation by Ga-DOTATATE PET compared to [F]FDG PET imaging. J Am Coll Cardiol. 2017;69(14):1774–91.
CAS
PubMed
PubMed Central
Google Scholar
Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34(5):525–7.
CAS
PubMed
Google Scholar
Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521.
PubMed
Google Scholar
Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46(D1):D754–61.
CAS
PubMed
Google Scholar
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
PubMed
PubMed Central
Google Scholar
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3.
CAS
PubMed
PubMed Central
Google Scholar
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013;14:128.
Google Scholar
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–7.
CAS
PubMed
PubMed Central
Google Scholar
Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26(5):589–95.
PubMed
PubMed Central
Google Scholar
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
PubMed
PubMed Central
Google Scholar
Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012;22(9):1813–31.
CAS
PubMed
PubMed Central
Google Scholar
Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008;26(12):1351–9.
CAS
PubMed
PubMed Central
Google Scholar
Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44(W1):W160–5.
PubMed
PubMed Central
Google Scholar
Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012;481(7381):389–93.
CAS
PubMed
PubMed Central
Google Scholar
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89.
CAS
PubMed
PubMed Central
Google Scholar
Müller F, Scherer M, Assenov Y, Lutsik P, Walter J, Lengauer T, et al. RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biol. 2019;20(1):55.
PubMed
PubMed Central
Google Scholar
Triche TJ Jr, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA methylation BeadArrays. Nucleic Acids Res. 2013;41(7):e90.
CAS
PubMed
PubMed Central
Google Scholar
Maksimovic J, Gordon L, Oshlack A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 2012;13(6):R44.
PubMed
PubMed Central
Google Scholar
Nordlund J, Bäcklin CL, Wahlberg P, Busche S, Berglund EC, Eloranta M-L, et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 2013;14(9):r105.
PubMed
PubMed Central
Google Scholar
Chen Y-A, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9.
CAS
PubMed
PubMed Central
Google Scholar
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.
CAS
PubMed
PubMed Central
Google Scholar
Xi Y, Li W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics. 2009;10:232.
PubMed
PubMed Central
Google Scholar
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006.
CAS
PubMed
PubMed Central
Google Scholar
Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11:587.
CAS
Google Scholar
Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–8.
CAS
PubMed
Google Scholar
Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13(10):R87.
PubMed
PubMed Central
Google Scholar
Wang H-Q, Tuominen LK, Tsai C-J. SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures. Bioinformatics. 2011;27(2):225–31.
PubMed
Google Scholar
O’Brien KA, Atkinson RA, Richardson L, Koulman A, Murray AJ, Harridge SDR, et al. Metabolomic and lipidomic plasma profile changes in human participants ascending to Everest Base Camp. Sci Rep. 2019;9(1):2297.
PubMed
PubMed Central
Google Scholar
Eiden M, Koulman A, Hatunic M, West JA, Murfitt S, Osei M, et al. Mechanistic insights revealed by lipid profiling in monogenic insulin resistance syndromes. Genome Med. 2015;7:63.
PubMed
PubMed Central
Google Scholar
Race AM, Styles IB, Bunch J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J Proteomics. 2012;75(16):5111–2.
CAS
PubMed
Google Scholar
Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78(3):779–87.
CAS
PubMed
Google Scholar
Tautenhahn R, Böttcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 2008;9:504.
Google Scholar
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
PubMed
PubMed Central
Google Scholar
Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, et al. A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv Biobank. 2015;13(5):311–9.
PubMed
PubMed Central
Google Scholar
Jain A, Tuteja G. TissueEnrich: Tissue-specific gene enrichment analysis. Bioinformatics. 2019;35(11):1966–7.
CAS
PubMed
Google Scholar
Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.
PubMed
Google Scholar
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.
Google Scholar
Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol. 2007;1:54.
PubMed
PubMed Central
Google Scholar
Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites. 2019. https://doi.org/10.3390/metabo9030057.
Article
PubMed
PubMed Central
Google Scholar
Cabassi A, Seyres D, Frontini M, Kirk PDW. Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome. http://arxiv.org/2008.00235