8,184 results match your criteria omics data


iNetModels 2.0: an interactive visualization and database of multi-omics data.

Nucleic Acids Res 2021 Apr 13. Epub 2021 Apr 13.

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden.

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). Read More

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Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.

Authors:
Pradeep Natarajan Akhil Pampana Sarah E Graham Sanni E Ruotsalainen James A Perry Paul S de Vries Jai G Broome James P Pirruccello Michael C Honigberg Krishna Aragam Brooke Wolford Jennifer A Brody Lucinda Antonacci-Fulton Moscati Arden Stella Aslibekyan Themistocles L Assimes Christie M Ballantyne Lawrence F Bielak Joshua C Bis Brian E Cade Ron Do Harsha Doddapaneni Leslie S Emery Yi-Jen Hung Marguerite R Irvin Alyna T Khan Leslie Lange Jiwon Lee Rozenn N Lemaitre Lisa W Martin Ginger Metcalf May E Montasser Jee-Young Moon Donna Muzny Jeffrey R O'Connell Nicholette D Palmer Juan M Peralta Patricia A Peyser Adrienne M Stilp Michael Tsai Fei Fei Wang Daniel E Weeks Lisa R Yanek James G Wilson Goncalo Abecasis Donna K Arnett Lewis C Becker John Blangero Eric Boerwinkle Donald W Bowden Yi-Cheng Chang Yii-Der I Chen Won Jung Choi Adolfo Correa Joanne E Curran Mark J Daly Susan K Dutcher Patrick T Ellinor Myriam Fornage Barry I Freedman Stacey Gabriel Soren Germer Richard A Gibbs Jiang He Kristian Hveem Gail P Jarvik Robert C Kaplan Sharon L R Kardia Eimear Kenny Ryan W Kim Charles Kooperberg Cathy C Laurie Seonwook Lee Don M Lloyd-Jones Ruth J F Loos Steven A Lubitz Rasika A Mathias Karine A Viaud Martinez Stephen T McGarvey Braxton D Mitchell Deborah A Nickerson Kari E North Aarno Palotie Cheol Joo Park Bruce M Psaty D C Rao Susan Redline Alexander P Reiner Daekwan Seo Jeong-Sun Seo Albert V Smith Russell P Tracy Ramachandran S Vasan Sekar Kathiresan L Adrienne Cupples Jerome I Rotter Alanna C Morrison Stephen S Rich Samuli Ripatti Cristen Willer Gina M Peloso

Nat Commun 2021 04 12;12(1):2182. Epub 2021 Apr 12.

Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Read More

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Multiomics integrative analysis identifies allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis.

Aging (Albany NY) 2021 Apr 12;13. Epub 2021 Apr 12.

Andalusion Bioiformatics Research Centre (CAEBi), Sevilla, Spain.

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Read More

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Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV.

Nature 2021 Apr 12. Epub 2021 Apr 12.

Technical University of Munich, School of Medicine, Institute of Virology, 81675, Munich, Germany.

The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. Read More

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Application of the omics sciences to the study of Naegleria fowleri, Acanthamoeba spp., and Balamuthia mandrillaris: current status and future projections.

Parasite 2021 12;28:36. Epub 2021 Apr 12.

CONACYT-Instituto Tecnológico de Sonora, Ciudad Obregón, 85000 Sonora, México.

In this review, we focus on the sequenced genomes of the pathogens Naegleria fowleri, Acanthamoeba spp. and Balamuthia mandrillaris, and the remarkable discoveries regarding the pathogenicity and genetic information of these organisms, using techniques related to the various omics branches like genomics, transcriptomics, and proteomics. Currently, novel data produced through comparative genomics analyses and both differential gene and protein expression in these free-living amoebas have allowed for breakthroughs to identify genes unique to N. Read More

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Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose.

Diabetologia 2021 Apr 12. Epub 2021 Apr 12.

Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.

Aims/hypothesis: An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses. Read More

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Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells.

J Bioinform Syst Biol 2021 26;4(1):13-32. Epub 2021 Feb 26.

Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, USA.

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. Read More

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February 2021

Polishing the crystal ball: mining multi-omics data in dermatomyositis.

Ann Transl Med 2021 Mar;9(5):435

Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York, NY, USA.

Precision medicine, which recognizes and upholds the uniqueness of each individual patient and the importance of discerning these inter-individual differences on a molecular scale in order to provide truly personalized medical care, is a revolutionary approach that relies on the discovery of clinically-relevant biomarkers derived from the massive amounts of data generated by epigenomic, genomic, transcriptomic, proteomic, microbiomic, and metabolomic studies, collectively known as multi-omics. If harnessed and mined appropriately with the help of ever-evolving computational and analytic methods, the collective data from omics studies has the potential to accelerate delivery of targeted medical treatment that maximizes benefit, minimizes harm, and eliminates the "fortune-telling" inextricably linked to the prevailing trial-and-error approach. For a disease such as dermatomyositis (DM), which is characterized by remarkable phenotypic heterogeneity and varying degrees of multi-organ involvement, an individualized approach that incorporates big data derived from multi-omics studies with the results of currently available serologic, histopathologic, radiologic, and electrophysiologic tests, and, most importantly, with clinical findings obtained from a thorough history and physical examination, has immense diagnostic, therapeutic, and prognostic value. Read More

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Integrative Pan-Cancer Analysis Reveals Decreased Melatonergic Gene Expression in Carcinogenesis and as a Prognostic Marker for Hepatocellular Carcinoma.

Front Oncol 2021 25;11:643983. Epub 2021 Mar 25.

Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.

Background: Melatonin has been shown to play a protective role in the development and progression of cancer. However, the relationship between alterations in the melatonergic microenvironment and cancer development has remained unclear.

Methods: We performed a comprehensive investigation on 12 melatonergic genes and their relevance to cancer occurrence, progression and survival by integrating multi-omics data from microarray analysis and RNA sequencing across 11 cancer types. Read More

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Integrating omics to characterize eco-physiological adaptations: How moose diet and metabolism differ across biogeographic zones.

Ecol Evol 2021 Apr 4;11(7):3159-3183. Epub 2021 Mar 4.

Department of Wildlife, Fish, and Environmental Studies Swedish University of Agricultural Sciences Umeå Sweden.

With accelerated land conversion and global heating at northern latitudes, it becomes crucial to understand, how life histories of animals in extreme environments adapt to these changes. Animals may either adapt by adjusting foraging behavior or through physiological responses, including adjusting their energy metabolism or both. Until now, it has been difficult to study such adaptations in free-ranging animals due to methodological constraints that prevent extensive spatiotemporal coverage of ecological and physiological data. Read More

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