ライフサイエンスコーパス: omics

1 ological measurements with new insights from omics.

2 ics related to next-generation breast cancer omics.

3 , we present Online Resource for Integrative Omics, a web-based resource with an intuitive user inter

4 a "verified" dataset based on cross-strategy/omics agreement was defined following their comparison w

5 Herein, we discuss both human data from meta'omics analyses and data from mechanistic studies in cell

6 By integrating omics analyses in 50 matched samples, we uncover in Taiw

7 Our extensive omics analyses provide a high-quality resource of altere

8 Taken together, our concurrent multi-omics analyses provide new mechanistic insights into the

9 Our Trans-Omics analyses reveal a modest but potentially relevant

10 from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting ti

11 g adenocarcinomas (LuADCs) using integrative omics analyses, and discover that mRNA levels of DTL, DC

12 mental CCS values for use in high throughput omics analyses.

13 fected HCC cells were characterized by multi-omics analyses.

14 Integrated "-Omics" analyses showed that addition of galactose to cul

15 were detected in the poly-synthetic strain "omics" analyses.

16 ances have been made using carbon isotopes, 'omics' analyses and surveys of respiration rates in meso

17 Following multi-omics analysis (including whole genome and transcriptome

18 athophysiology, we performed multi-platform 'omics analysis of peripheral blood mononuclear cells and

19 inicophysiologic parameters and performed an omics analysis of sputum.

20 of Cell, insights from a longitudinal multi-omics analysis of the largest yet-reported cohort of mel

21 S/MS), a widely used method for comparative 'omics analysis, experiences challenges with compound ide

22 nd validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer

23 ily integrated with other methods for cancer omics analysis.

24 Here, using integrative 'omics' analysis, we identified an arachidonate 12-lipoxy

25 findings highlight the power of integrative omics and biochemical analyses for annotating the functi

26 studies building on the explosive growth in omics and cell biology methods have facilitated the in-d

27 can be analyzed in the full context of other omics and clinical information.

28 When provided with omics and experimental data, ODG will create a comparati

29 e a Precancer Atlas (PCA), integrating multi-omics and immunity - basic tenets of the neoplastic proc

30 and models in developing an integrated multi-omics and immunity PCA - an immense national resource to

31 ing IL-18 concentrations, we applied various omics and molecular biology approaches.

32 ularly with respect to the incorporation of -omics and next-generation sequencing data and continual

33 We discuss key differences between MS-based -omics and other booming -omics technologies and highligh

34 The subject matter experts invited to the Omics and Precision Oncology Workgroup were tasked with

35 CO convened two complementary workshops: the Omics and Precision Oncology Workshop in October 2016 an

36 following areas of need were identified: 1) omics and precision oncology, 2) advancing interoperabil

37 ide association studies in humans and mice, -omics and systems genetics approaches, and unique experi

38 accompanied by the growing availability of "-omics" and ancient DNA data, promises a new era in our u

39 hnological innovations, such as single cell "omics" and human stem cell derivation, have now greatly

40 This review introduces a range of "omics" and patient data sources relevant to managing inf

41 to microfluidics for mammalian single-cell 'omics' and discuss challenges and future opportunities.

42 dual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omi

43 emporary technologies in molecular biology, -omics, and cell biology aids in exploring the comparativ

44 rom diverse disciplines (e.g., biochemistry, omics, and computational biology; microbiology, immunolo

45 for the potential of scanning LC-FAIMS-MS in omics applications is demonstrated for the nontargeted p

46 MS-MS) is shown to enhance peak capacity for omics applications.

47 functions against HIV using a novel focused omics approach ("communicome") has the potential to sign

48 In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and an

49 t accompany lung adenocarcinomas, we took an omics approach in profiling both the coding genes and th

50 dy underlines the potential role of an inter-omics approach in understanding the metabolic pathways i

51 This integrative multi-omics approach permits more detailed single-cell interro

52 Here, we develop an image-omics approach to integrate quantitative cell imaging da

53 Using an integrated omics approach, we investigated the basis for the phenot

54 Using an integrated omics approach, we present a TF network in the major org

55 ng patients with heart failure using a multi-omics approach.

56 Here we use a 'multi-omics' approach to demonstrate that the duration of poly

57 g multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high t

58 on on microplastics at sea using imaging and omics approaches are further indicated to better underst

59 mixture of microbial species) and other meta-omics approaches hold even greater promise for providing

60 ions, but few studies have used non-targeted omics approaches to explore differences between diving m

61 The combination of omics approaches with genetics and microscopy allows res

62 As the power of "meta-omics" approaches to natural products discovery further

63 new regulators in IRF5 pathway, we used two "omics" approaches: affinity purification coupled with ma

64 Further 'omics' approaches, through GWAS and transcriptomics, wil

65 publicly available datasets and performed "-omics"-based integrative, and network topology analyses

66 iations and the promise of high-throughput "-omics"-based systems biology approach in providing great

67 Our integrative "-omics"--based analyses identified dynamic complexes asso

68 efit from, current and future application of omics-based approaches to understand the host response i

69 Furthermore, using only the omics-based features the method can still identify MPs w

70 Application of omics-based methodologies is advancing understanding of

71 igated using gingival tissue samples through omics-based whole-genome transcriptomics while using hea

72 As 'omics' biotechnologies accelerate the capability to cont

73 First, omics can be used to clarify the extent and form of soci

74 Second, omics can be used to examine the consequences of sociali

75 This work provides an example of how meta-omics can increase our understanding of industrial waste

76 Using large-scale multi-omics cancer datasets, we show that InFlo exhibits highe

77 is expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific co

78 ern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing.

79 mics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta

80 The Omics Dashboard is a software tool for interactive explo

81 The Omics Dashboard is organized as a hierarchy of cellular

82 continuous (e.g. mRNA expression) and binary omics data (e.g. discretized methylation data).

83 he ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabol

84 STATION database integrates diverse types of omics data across mammals to advance understanding of th

85 io of statistical tools for high-dimensional omics data analysis covering normalization, pattern reco

86 Despite its potential usefulness for omics data analysis, no efficient R implementation is pu

87 g the gap between the complexity captured by omics data and governing principles of proteome allocati

88 systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks.

89 Integration of omics data and RNA immunoprecipitation experiments estab

90 knowledgebase that integrates ecosystem-wide omics data and the development of molecular tools/resour

91 The multi-omics data are integrated through direct links between g

92 Multiple omics data are rapidly becoming available, necessitating

93 od with five imputation methods using single omics data at different noise levels, sample sizes and d

94 nt metabolic system and also illustrates how omics data can be integrated to generate new hypotheses

95 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models pr

96 study presents an integrative procedure for -omics data exploitation, giving rise to biologically rel

97 We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome A

98 Forest regression for integrating multiple ~ omics data for prediction of four quality traits of pota

99 ernal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metaboli

100 Integration of multi-omics data from 521 prostate tumor samples indicated a s

101 epresent a general formalism for integrating omics data from any experimental condition into constrai

102 variant, and that models trained on matched -omics data from non-cancerous cell-lines are able to pre

103 as become the method of choice for analyzing omics data in general and gene expression data in partic

104 Rapid generation of omics data in recent years have resulted in vast amounts

105 Omics data included those from historical genome-wide as

106 IP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, o

107 The integrated -omics data indicate purine and pyrimidine metabolism pat

108 tions for application of metabolomics-based -omics data integration in understanding disease pathogen

109 Applying this novel approach for multi-omics data integration yields a model consisting of seve

110 nsitioning into genomics, bioinformatics and omics data integration.

111 Integrative analysis of multi-omics data is becoming increasingly important to unravel

112 nvironmental ontology reconstructed from the omics data is substantially different and complementary

113 to explore and query the content of diverse omics data is very limited.

114 e estimates of missing value from individual omics data itself as well as from other omics, and 2) si

115 ion designs, (c) integration of multi-layer -omics data leading to identification of genes and pathwa

116 Taken together our integrated multi-omics data point to multiple classes of Puf3p targets, w

117 st decade, as large amounts of experimental 'omics data relevant to glycosylation processing have acc

118 Integrated omics data reveal IL8 as one of the most perturbed genes

119 Integration of the multi-omics data revealed that UVR-induced transcriptional dys

120 we show that pairwise integration of primary omics data reveals regularities that tie cellular proces

121 This will enable contact with omics data sets and allow acclimation and adaptive respo

122 cellular fractionation proteomics with other omics data sets and is generally applicable to other tis

123 states with high collagen, is now utilizing 'omics data sets and is revealing polymer physics-type, n

124 ed with novel, easy to read visualization of omics data sets and network modules.

125 Through analysis of three real omics data sets and simulation studies, we found the amo

126 we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic

127 Modern time series gene expression and other omics data sets have enabled unprecedented resolution of

128 In conclusion, combining multiple omics data sets in the public domain increases robustnes

129 glycomics measurements together with other 'omics data sets will lead to a deeper understanding and

130 We conclude that this ontology can, from omics data sets, enable the development of detailed SCP

131 s of such research for the interpretation of omics data sets.

132 ion of HRGP classes in existing and emerging omics data sets.

133 onnections and information imbedded in multi-omics data sets.

134 s the heterogeneity present in the different omics data sources, which makes it difficult to discern

135 t fully utilize the potential of these multi-omics data sources.

136 n package GSAR are applicable to any type of omics data that can be represented in a matrix format.

137 as resulted in massive quantities of diverse omics data that continue to accumulate rapidly.

138 Here, we harness omics data to calculate kmax(vivo), the observed maximal

139 able strategy to integrate multiple lines of omics data to identify a core pool of regulator targets.

140 lementary information encoded in each of the omics data to identify novel driver genes through an int

141 Integrating omics data to refine or make context-specific models is

142 egy for selecting and integrating multiple ~ omics data using random forest method and selected repre

143 utation methods mainly focus on using single omics data while ignoring biological interconnections an

144 Omics data with gene identifiers not supported by WebGes

145 ell- and condition-specific high-dimensional Omics data with interaction information from existing da

146 Furthermore, integration of omics data with mechanistic and epidemiological data is

147 Integrating omics data with models enabled the discovery of two nove

148 ermediates in situations of high-dimensional omics data with varying degrees of success.

149 Integrative analysis of high-throughput omics data with virologic and histopathologic data uncov

150 approach for the analysis of IgAN-relevant -omics data, aiming at identification of novel molecular

151 Given the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatic

152 When it comes to longitudinal omics data, it will often depend on the overall objectiv

153 reasing availability of multiple additional -omics data, this quest has been frustrated by various th

154 gies allow for measurements of many types of omics data, yet the meaningful integration of several di

155 able at that time nor the scarcely available omics data-let alone metabolic modeling and other nowada

156 classification using high-dimensional sparse omics data.

157 hes to the interpretation of high-throughput omics data.

158 tions of cellular metabolism and leveraging -omics data.

159 a variety of application domains, including -omics data.

160 biological interpretation of high-throughput omics data.

161 ausality in increasingly complex networks of omics data.

162 ge-passing to integrate multiple sources of 'omics data.

163 for extracting enzyme kinetic constants from omics data.

164 biological datasets with different types of -omics data.

165 e used to integrate gene expression or other omics data.

166 everely hinder integrative analysis of multi-omics data.

167 BI resources for the representation of multi-omics data.

168 and integrated pathway analysis of multiple omics data.

169 tegration, and interpretation of single-cell omics data.

170 , but direct comparisons between models and "omics" data are lacking.

171 l interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable w

172 lly expressed genes (DEGs) across different 'omics' data types or multi-dimensional data including ti

173 identifying regulatory relationships across 'omics' data within an organism and for comparative gene

174 must accommodate the challenges inherent in 'omics' data, including high-dimensionality, noise, and t

175 sholds with applications for high throughput omics-data, optimal alpha, which minimizes the probabili

176 The Omics Database Generator (ODG) allows users to create cu

177 ve analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid

178 nd 2) simultaneously impute multiple missing omics datasets by an iterative algorithm.

179 as proposed to integrate multiple correlated omics datasets for improving the imputation accuracy.

180 m that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of

181 However, the currently available multi-omics datasets inevitably suffer from missing values due

182 is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of

183 gic approach to integrate publicly available omics datasets with histopathologic features.

184 omputational pipelines for integrating multi-omics datasets, and functional perturbation to systemica

185 markers from four different high-throughput omics datasets, namely epigenomics, transcriptomics, gly

186 e that hosts a large collection of processed omics datasets.

187 xpression Atlas and upload and analyze their Omics datasets.

188 hances data science applications of multiple omics datasets.

189 Expanding 'omics' datasets for parasitic nematodes have accelerated

190 er - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with t

191 ies have led to an increase of datasets from omics disciplines allowing the understanding of the comp

192 nt response, and incorporation of emerging "-omics" discoveries.

193 is work provides an integrative framework of omics-driven predictive modelling that is broadly applic

194 ate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at

195 nses of canola, including physiological and -omics effects of drought.

196 approach will allow integration of different omics, environmental and phenotypic data sets.

197 With the advent of the omics era and accelerated development of targeted therap

198 search and discusses future prospects in the omics era.

199 Therefore, single omics experiments cannot profile their underlying target

200 high-throughput DNA sequencing technologies, omics experiments have become the mainstay for studying

201 Here we present the Visual Omics Explorer (VOE), a cross-platform data visualizatio

202 ematical biology; functional and comparative OMICs; gene editing; expanded use of model organisms; an

203 Herein, we used different omics (genomics and transcriptomics) to identify novel b

204 genomics appearing as part of the series on "omics." Genomics pertains to all components of an organi

205 Our multi-omics graphical model demonstrates the interconnectivity

206 The application of OMICS has provided more depth to existing hypotheses as

207 Single-cell multi-omics has recently emerged as a powerful technology by w

208 ative analysis of clinically-annotated multi-omics HNSCC data released by the Cancer Genome Atlas.

209 The ability to integrate 'omics' (i.e. transcriptomics and proteomics) is becoming

210 s Europe were combined with systems biology (omics, IgE measurement using microarrays) and environmen

211 In this study, a novel multi-omics imputation method was proposed to integrate multip

212 nsistent aberration patterns across multiple-omics in tumors.

213 ddresses the necessity to integrate multiple omics information arising from dynamic profiling in a pe

214 Recent large-scale omics initiatives have catalogued the somatic alteration

215 Omics Integrator also provides an elegant framework to i

216 Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid be

217 In this work, we introduce Omics Integrator, a software package that takes a variet

218 In parallel, integrative multi-omics investigations have generated high-resolution mole

219 While variance in the multi-omics is dominated by inter-individual differences, temp

220 Plant-omics is rapidly becoming an important field of study in

221 ain a multi-scale model that integrates four omics layers to predict genome-wide concentrations and g

222 del ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines.

223 ethods for their integration across multiple omics layers.

224 order, when iterating through the different "omics" layers, and implementing this algorithm in the fa

225 es the possible interplay between different "omics" layers.

226 tact and degraded cartilage in at least two -omics levels, 16 of which have not previously been impli

227 y differentially regulated across all three -omics levels, confirming their differential expression i

228 rioritize biomarkers, to integrate different omics levels, to design follow-up functional assay exper

229 s to assess conditional dependencies between omics markers and phenotypes while eliminating mediated

230 rious components of the epigenome into multi-omics measurements allows for studying cellular heteroge

231 In vitro screening tools and 'omics methods are increasingly being incorporated into t

232 Omics network and pathway analyses predicted a link betw

233 elevant, and thus expand the 'interpretable 'omics' of single subjects (e.g. personalome).

234 red to studies of a single omics type, multi-omics offers the opportunity to understand the flow of i

235 le, ODG can be used to conduct complex multi-omics, pattern-matching queries.

236 ome of acute hyperoxic lung injury using the omics platforms: microarray and Reverse Phase Proteomic

237 the integration of metabolomics with other "omics" platforms will allow us to gain insight into path

238 Other 'omics' platforms trade coverage for sensitivity, althoug

239 We then evaluated multi-omics profiles of primary high-grade serous ovarian canc

240 ional data sets generated from recent cancer omics profiling projects have presented new challenges a

241 including annotated genomes, high-throughput omics profiling, and genome editing, have begun to eluci

242 ment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement.

243 Although findings from OMICS research have been greatly informative, problems r

244 Despite the obstacles cited above, OMICS research is expected to encourage the discovery of

245 By integrating bioinformatics approaches, omics resources and transcriptome collections today avai

246 ructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because so

247 cally characterized MPs using functional and omics-scale information.

248 "Omics" sciences have been developed to provide a holisti

249 spective for integrating hundreds of various omics-seq data together.

250 the current published data regarding other "omics" strategies-proteomics, metabolomics, and the micr

251 (Arabidopsis thaliana) using an integrative omics strategy.

252 ion of metabolic pathways - a common goal of omics studies - could be incorrect if well-recognized pa

253 Hypotheses generated through omics studies can be directly tested using site-directed

254 Omics studies comparing the microbiome of, and its inter

255 data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and ne

256 UVR) on skin homeostasis, we performed multi-omics studies to characterize UV-induced genetic and epi

257 ic investigation followed by high-throughput omics studies.

258 Pathway analysis is widely used in omics studies.

259 Clustering is used widely in 'omics' studies and is often tackled with standard method

260 Using a staged multi-omics study design, we link a subset of 517 epigenetic l

261 heart failure, we performed the first multi-omics study in myocardial tissue and blood of patients w

262 This study represents the first omics study on a gemfibrozil-degrading bacterium.

263 By integrative omics study, we identified genes and pathways tightly re

264 the detection of PFOS, and were subjected to omics study.

265 omatin immunoprecipitation (ChIP-seq) is the omics technique that enables genome wide localization of

266 Novel approaches using OMICS techniques enable a collective assessment of multi

267 a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at

268 king it attractive in combination with other omics techniques.

269 Modern omics technologies allow characterization of functional

270 atics platform to stay current with emerging omics technologies and analysis methods to continue supp

271 Multi-omics technologies and ecological context underlie effec

272 this review, we provide an overview of such omics technologies and focus on methods for their integr

273 s between MS-based -omics and other booming -omics technologies and highlight what we view as the fut

274 Current -omics technologies are able to sense the state of a biol

275 Though barley genome sequence and advanced omics technologies are available, till date none of the

276 We conclude with a perspective on new multi-omics technologies capable of integrating several readou

277 Recent advances in high-throughput omics technologies have enabled biomedical researchers t

278 Recent advances in omics technologies have not been accompanied by equally

279 This is the first study combining omics technologies to describe the impact of differences

280 ntly descriptive nature of localization and -omics technologies to provide functional, quantitative,

281 a multitude of advantages as demonstrated by omics technologies, helping to support both government a

282 The use of different omics technologies, including transcriptomics, proteomic

283 Other omics technologies, such as proteomics and metabolomics,

284 nt of many innovative tools derived from the omics technologies, transplant medicine is slowly enteri

285 d by whole genome sequencing and an array of omics technologies.

286 at emphasize community-level analyses using 'omics technologies.

287 formatics containers with a special focus on omics technologies.

288 logy" centered on cutting-edge genetic and "-omics technologies." Framingham Heart Study investigator

289 been a driving force in recent years in the "omics" technologies and while great strides have been ac

290 cal imaging, electronic health records, and "omics" technologies have produced a deluge of data.

291 Recently, mass spectrometry-based "omics" technology has been applied to the RBC storage le

292 Cardiac Failure, Cardiomyopathies, Genetics, Omics, & Tissue Regeneration, and Hypertension (1-60).

293 Cardiac Failure, Cardiomyopathies, Genetics, Omics, & Tissue Regeneration, and Hypertension.

294 y highlights the strength of using different omics to identify novel biomarkers of drug response and

295 As compared to studies of a single omics type, multi-omics offers the opportunity to unders

296 s of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy,

297 for querying and analyzing PGDBs, including Omics Viewers and tools for comparative analysis.

298 Individual omics-wide molecular diagnostics, extracorporeal therapi

299 In this context, metagenomics and functional omics will likely play a central role as they will allow

300 increasingly implemented in high throughput omics workflows, and new informatics approaches are nece