ライフサイエンスコーパス: transcriptomics

1 ate orthology inference is critical to phylo-transcriptomics.

2 ISH), an image-based approach to single-cell transcriptomics.

3 es that are changing the state of the art in transcriptomics.

4 gical hurdles remain to examine host-microbe transcriptomics.

5 rgest assembled for echinoderm phylogeny and transcriptomics.

6 olution, as well as recent progress in brain transcriptomics.

7 integrate ordinal clinical information with transcriptomics.

8 eomics, copy number variation, and polysomal transcriptomics.

9 particular by recent advances in single-cell transcriptomics.

10 next-generation sequencing for genomics and transcriptomics.

11 endometrial stromal cells, using single-cell transcriptomics.

12 rdination, showcasing the need for long-read transcriptomics.

13 ical advances since the late 1990s have made transcriptomics a widespread discipline.

14 By combining proteomics and transcriptomics, a gene responsible for myrosinase activ

15 Comparative transcriptomics across mutants and developmental stages

16 We used network analysis of lung transcriptomics (Affymetrix arrays) in 70 former smokers

17 y cross-strategy and cross-omics (proteomics-transcriptomics) agreement.

18 Recent advances in genomics, phenomics, and transcriptomics allow in-depth analysis of natural varia

19 We report, using differential transcriptomics alongside immunohistologic and biochemic

20 Transcriptomics analyses showed increased expression of

21 ress this question, we performed comparative transcriptomics analyses to identify candidate genes and

22 ntegrated proteomics, phosphoproteomics, and transcriptomics analyses, we identified the downstream s

23 inhibitor phenotypic screens, and miRNA-mRNA transcriptomics analyses, we identify three proviral and

24 Here, we exploited a genome wide transcriptomics analysis combined with a systems biology

25 short read sequencing data and a comparative transcriptomics analysis of the developing leaf of D. ol

26 Transcriptomics analysis revealed that the expression of

27 Transcriptomics analysis revealed that, in POU5F1-null c

28 of tools to perform comparative genomic and transcriptomics analysis that are available at PATRIC.

29 Herein, we applied transcriptomics analysis to compare SGs mRNA levels in A

30 A combined lipidomics and transcriptomics analysis was performed on mouse myeloma

31 Through unbiased "in situ transcriptomics" analysis-gene expression profiling of l

32 bution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence mi

33 Using transcriptomics and bioenergetic analysis, we discovered

34 In this study we have used transcriptomics and chromatin immunoprecipitation and hi

35 computational approach (PSFinder) that fuses transcriptomics and clinical data to identify HGS-OvCa p

36 ology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal hu

37 signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE an

38 ssification tree model on publicly available transcriptomics and DNase-seq data and assessed the pred

39 ve facilitated studies that combine genetic, transcriptomics and epigenomics data to address a wide r

40 such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always suffi

41 and compared immune responses in lung using transcriptomics and flow cytometry.

42 This has been achieved through transcriptomics and high-throughput immunohistochemistry

43 Using transcriptomics and in-depth splicing analysis in young

44 In this study, transcriptomics and label-free proteomics were combined

45 nt on cardiometabolic disease phenotypes via transcriptomics and metabolomic signatures.

46 However, transcriptomics and metabolomics analyses uncovered syst

47 Transcriptomics and metabolomics analysis on enzymatic d

48 Using an integrated transcriptomics and metabolomics approach, we demonstrat

49 nd the specialist herbivore Pieris brassicae Transcriptomics and metabolomics data were evaluated usi

50 ic pathway utilizing the first comprehensive transcriptomics and metabolomics datasets for Rhodiola r

51 High-throughput transcriptomics and metabolomics revealed unique signatu

52 allowing us to study cell size in vivo using transcriptomics and metabolomics.

53 Single-cell transcriptomics and mutagenesis analysis delineated dyna

54 Here we show--using proteomics, transcriptomics and network analyses--that in human LSCs

55 Cell type-specific transcriptomics and pharmacological experiments revealed

56 s of available training examples, comprising transcriptomics and phosphoproteomics data.

57 These metabolite-guided transcriptomics and phylogenetic and gene expression ana

58 Through megakaryocyte transcriptomics and platelet proteomics, we identified s

59 facilitate new experiments in connectomics, transcriptomics and protein localization.

60 comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated

61 We also perform meta-analyses of public transcriptomics and proteomics data, which indicate that

62 plex molecular datasets, including genomics, transcriptomics and proteomics data.

63 Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown

64 We integrated large transcriptomics and proteomics datasets from malignant a

65 Network analysis of transcriptomics and proteomics datasets uncovered persis

66 While high-throughput methods have made transcriptomics and proteomics datasets widely accessibl

67 Using transcriptomics and proteomics followed by pathway analy

68 Transcriptomics and proteomics identified direct and ind

69 We review current knowledge on the transcriptomics and proteomics of tick tissues from a sy

70 Transcriptomics and proteomics show that selenium affect

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

72 Using transcriptomics and purified MqsR, we determined that en

73 RNA-seq (sequencing)-based transcriptomics and SILAC (stable isotope labeling of am

74 We used genome-wide transcriptomics and single-cell phenotyping to explore t

75 We combined transcriptomics and systematic imaging to determine the

76 Recent advances in transcriptomics and the complete genome sequencing of mo

77 nding of non-coding regulatory mechanisms of transcriptomics and unraveled essential molecular biomar

78 Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a popu

79 e current study we use comparative genomics, transcriptomics, and functional studies to characterize

80 integrated approach combining metabolomics, transcriptomics, and gene function analyses to character

81 We used thermal performance, transcriptomics, and genome scans to measure responses o

82 essential in the advancement of proteomics, transcriptomics, and genomics science.

83 nts through systems integration of genomics, transcriptomics, and metabolomics.

84 Together with genomics, transcriptomics, and other technologies, transomic data

85 ctical uses and application of metagenomics, transcriptomics, and proteomics data and associated tool

86 merging field is clearly linked to genomics, transcriptomics, and proteomics.

87 We take a transcriptomics approach to build the first reproducible

88 The purpose of this transcriptomics approach was to generate testable hypoth

89 Here, a cell-specific and region-specific transcriptomics approach was used to determine gene expr

90 Fusarium head blight (FHB) based on metabolo-transcriptomics approach.

91 CI9831 based on integrated metabolomics and transcriptomics approach.

92 ge commitment, using an unbiased single-cell transcriptomics approach.

93 Fusarium head blight (FHB) based on metabolo-transcriptomics approach.

94 In summary, our single-cell transcriptomics approaches enabled us to reconstruct the

95 aluated using complementary metabolomics and transcriptomics approaches with the aim of discovering t

96 we combined single-cell lineage-tracing and transcriptomics approaches with time-lapse imaging.

97 Recent advances in single-cell transcriptomics are ideally placed to unravel intratumor

98 These findings demonstrate "cross-disease" transcriptomics as an approach to gain insights into the

99 hermore, implicate mRNA modification and epi-transcriptomics as novel regulators of memory formation.

100 e biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases cause

101 A transcriptomics-based pre-vaccination predictor of respo

102 his offers the opportunity for genomics- and transcriptomics-based selection of patients for rational

103 g approach to spatially resolved single-cell transcriptomics because of its ability to directly image

104 ificant differences in sputum proteomics and transcriptomics between the clusters.

105 experimental and analytical protocol for our transcriptomics biomarker, as well as an enhanced applic

106 Lung transcriptomics, bone marrow transplantation experiments

107 ata remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes exp

108 Cell type-specific transcriptomics can be used to identify pathways that co

109 Recent advances such as single-cell transcriptomics, CNS cell type-specific and developmenta

110 In total, the results of comparative transcriptomics correctly predicted a 2AA-dependent moti

111 ned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with div

112 sed, data-rich biological methodology (i.e., transcriptomics) could be utilized to evaluate relative

113 Based on transcriptomics data across environmental and genetic ba

114 2.0 database hosts large-scale genomics and transcriptomics data and provides integrative bioinforma

115 dynamics of differentiation from single cell transcriptomics data and to build predictive models of t

116 nomics provide a way of routinely generating transcriptomics data at the single cell level.

117 database that integrates publicly available transcriptomics data for several prokaryotic model organ

118 Application of PSFinder to transcriptomics data from 180 HGS-OvCa patients treated

119 We used basal transcriptomics data from a panel of human lymphoblastoi

120 In this study, we integrate transcriptomics data from multiple databases and systema

121 integrated analysis of previously published transcriptomics data from strain ATCC19606.

122 By integrating genomics and transcriptomics data from the same patients, we identifi

123 eomics data could be readily integrated with transcriptomics data in standard annotation tools.

124 in human tissues is now displayed alongside transcriptomics data in the same tissues.

125 apply an original computational workflow to transcriptomics data of innate immune cells integrating

126 ignatures to The Cancer Genome Atlas patient transcriptomics data of multiple cancer types and single

127 To do this, a high-resolution time series transcriptomics data set was produced, coupled with deta

128 clust has great potential in the analysis of transcriptomics data to identify large-scale unknown eff

129 e integrated the ionomics, metabolomics, and transcriptomics data to identify the genes and metabolic

130 By mapping the transcriptomics data to KEGG pathways, we found expressi

131 store, visualize and analyze epigenomics and transcriptomics data using a biologist-friendly web inte

132 We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic p

133 ls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system.

134 Transcriptomics data were analyzed using Partek Genomics

135 We will discuss analytical strategies for transcriptomics data, the significance of noncoding RNA

136 Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct

137 statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynam

138 gulatory chromatin regions solely relying on transcriptomics data, which complements and improves the

139 findings are corroborated by proteomics and transcriptomics data, which show, among other things, an

140 were compared based on sputum proteomics and transcriptomics data.

141 y on correlating and clustering tumours with transcriptomics data.

142 genes by incorporating genome sequencing and transcriptomics data.

143 assessment, including investigation of IgAN transcriptomics datasets (Nephroseq database), their rep

144 ave been proposed to identify gene-sets from transcriptomics datasets deposited in public domain.

145 ion signatures across multiple public access transcriptomics datasets of human asthma, followed by te

146 Integrating proteomics and transcriptomics datasets revealed reduced inflammatory a

147 sing both synthetic and real (cancer-related transcriptomics) datasets.

148 Comparative transcriptomics demonstrated that choline not only induc

149 nce) are downregulated, and microarray-based transcriptomics demonstrating that indole decreases the

150 is analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segm

151 VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data store

152 em cell (iPSC) technology can be advanced by transcriptomics, epigenomics, and bioinformatics that in

153 y 14% (from 3612 to 4113), compared to using transcriptomics evidence alone.

154 valuate our predictions using an independent transcriptomics experiment involving over-expression of

155 rk for interpreting results from single-cell transcriptomics experiments.

156 libraries are rapidly enhancing the power of transcriptomics for neuroscience applications.

157 stem as an example and utilizing Associative Transcriptomics for the first time in a plant pathology

158 lification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.

159 prehensively, serving as a key complement to transcriptomics, genomics, and metabolomics--a combinati

160 Data types included transcriptomics, genomics, and proteomics.

161 roughput omics datasets, namely epigenomics, transcriptomics, glycomics and metabolomics, with a comp

162 Transcriptomics has been defined by repeated technologic

163 Single-cell transcriptomics has been employed in a growing number of

164 suitability of these methods for single-cell transcriptomics has not been assessed.

165 larity and phylogenetic relatedness and that transcriptomics has the capacity to greatly enhance ecol

166 We argue that single-cell transcriptomics has the potential to provide a new persp

167 Using reporter gene fusions and transcriptomics, here we report that DnrF selectively re

168 However, global transcriptomics highlight differences between TPH cells

169 Transcriptomics identified expression patterns that expl

170 Whole-genome sequencing and comparative transcriptomics identified highly-upregulated degradatio

171 study demonstrates the power of single-cell transcriptomics in dissecting cellular process and linea

172 esults thus confirm the power of associative transcriptomics in dissection of the genetic control of

173 e killifish genome sequences and comparative transcriptomics in four pairs of sensitive and tolerant

174 power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the ge

175 of fetal and tumor microenvironments through transcriptomics in mice revealed strikingly similar and

176 geneity in number, morphology, activity, and transcriptomics in nuclei relevant to motor control and

177 and peripheral blood mononuclear cell (PBMC) transcriptomics in patients receiving high-dose statin t

178 ed by mass spectrometry-based proteomics and transcriptomics in the CD8(+) DC line.

179 Image-based approaches to single-cell transcriptomics, in which RNA species are identified and

180 Here a comprehensive comparative transcriptomics interrogation of gene expression among f

181 ution imaging, microbiome, metabolomics, and transcriptomics into future research efforts; and build

182 sults demonstrate the utility of integrating transcriptomics into the study of human genetic disease

183 Single-cell transcriptomics is becoming an important component of th

184 metabolic profiling platforms, genomics, and transcriptomics is creating significant progress in iden

185 lar attention to the impact that single cell transcriptomics is expected to have on our understanding

186 Finally, transcriptomics is fueling progress in understanding the

187 Transcriptomics is shedding new light on the relationshi

188 Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and pr

189 s method, which we call "genome-guided phylo-transcriptomics", is compared to other recently publishe

190 ze allows the accumulation of sequencing and transcriptomics layers to guide the identification of ca

191 The results indicated that transcriptomics may even pinpoint pertinent adverse outc

192 al information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping

193 pproaches, such as (meta-) genomics, (meta-) transcriptomics, (meta-) metabolomics, and (meta-) prote

194 s in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies.

195 nents on multiple "omic" measures, including transcriptomics, metabolomics, proteomics, lipidomics, e

196 We conclude that our transcriptomics modelling predicts that hypoxia activate

197 Other techniques, such as flow cytometry and transcriptomics, must be combined with intravital imagin

198 Using in situ transcriptomics (nCounter), we demonstrate a significant

199 Predictions were correlated with transcriptomics (Nephroseq) and relevant protein express

200 ans, and determined if abnormalities in NRG3 transcriptomics occur in mood disorders and are genetica

201 Here we combined transcriptomics of both male and female reproductive gla

202 Here we carry out de novo transcriptomics of five representative charophyte specie

203 d at low levels in bulk tissues, single-cell transcriptomics of hundreds of neocortex cells reveal th

204 We performed RNA sequencing-based transcriptomics of islets from two obese mouse strains,

205 Integrated metabolomics and transcriptomics of Medicago truncatula seedling border c

206 Using genome-wide transcriptomics of Mtb infected lungs we developed data

207 maternal-specific stress responsiveness and transcriptomics of the paraventricular nucleus of the hy

208 lution quantitative imaging with single-cell transcriptomics of wild-type and Fgf receptor (Fgfr) mut

209 Transcriptomics offers a new approach to understanding t

210 High-throughput single-cell transcriptomics offers an unbiased approach for understa

211 tant biological questions demand single-cell transcriptomics on a large scale.

212 on piece, I review the evidence arising from transcriptomics on the topics of the evolution of germ l

213 domonas reinhardtii We conducted comparative transcriptomics on this alga to discern processes releva

214 Despite its immense value and in contrast to transcriptomics, only a handful of studies in crop plant

215 ended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics meas

216 Here we describe findings that utilized transcriptomics, physiological assays, and RNA interfere

217 lishment of the first full-scale Associative Transcriptomics platform for B. napus enables rapid prog

218 Additionally, single-cell transcriptomics presents unique analysis challenges that

219 This comparative whole-blood transcriptomics profiling of virulent and avirulent mala

220 backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequenci

221 ily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgent

222 by initially storing genomics, methylomics, transcriptomics, proteomics and microRNA data that has b

223 edge, we performed comparative metabolomics, transcriptomics, proteomics, and (13)C-labeling of type

224 ination of single-cell fluorescence imaging, transcriptomics, proteomics, and in vivo studies.

225 Different omic approaches, such as genomics, transcriptomics, proteomics, and metabolomics, have expa

226 oughput technologies, including epigenomics, transcriptomics, proteomics, and metabolomics, is now ma

227 , and describe their ongoing functions using transcriptomics, proteomics, and metabolomics.

228 e biomarkers in four major groups: genomics, transcriptomics, proteomics, and metabolomics/microbiota

229 tion at multiple levels-including phenomics, transcriptomics, proteomics, chromosome segregation, and

230 e of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics

231 ATGC transcriptomics provides access to non-expert computer u

232 expression, as assessed by using Luminex or transcriptomics/quantitative real-time RT-PCR, were anal

233 Single-cell transcriptomics requires a method that is sensitive, acc

234 bly and additional white spruce genomics and transcriptomics resources, we performed MAKER-P annotati

235 Integration of these findings with transcriptomics resulted in 253 "verified" proteins base

236 Transcriptomics revealed a neuroprotective role for both

237 n 5-log10 in <24 h, comparative genomics and transcriptomics revealed differences in the genomes and

238 Transcriptomics revealed downregulation of key molecules

239 Comparative transcriptomics revealed pectinase expression to be enri

240 Global transcriptomics revealed proton accumulation triggers th

241 Colon mucosa transcriptomics revealed that antibiotics block heme-ind

242 Mouse transcriptomics revealed that PO differentially regulate

243 Transcriptomics revealed the dysregulated expression of

244 These results, together with transcriptomics, revealed that the vast majority of thes

245 Single-cell transcriptomics reveals gene expression heterogeneity bu

246 oying proteomics (tandem mass spectrometry), transcriptomics (RNA microarray hybridization), and othe

247 s review, we describe recent developments in transcriptomics (RNA-seq) and functional genomics that w

248 Comparative transcriptomics show expression levels of genes encoding

249 Cross-species transcriptomics showed that both models are closely rela

250 is method could also have high potential for transcriptomics studies in other organisms.

251 The earliest transcriptomics studies in P. falciparum suggested a cas

252 We then review published NGS genomics and transcriptomics studies of thermal adaptation to heat st

253 t within hours, and can be widely applied to transcriptomics studies ranging from clinical RNA sequen

254 tudy highlights the importance of conducting transcriptomics studies that leverage more than one refe

255 In agreement with previous transcriptomics studies, amongst the most marked changes

256 rticle we consider how recent proteomics and transcriptomics studies, together with ultrastructural o

257 Here we reported a comprehensive transcriptomics study to capture the global gene profile

258 Transcriptomics technologies are the techniques used to

259 methods facilitate single-cell genomics and transcriptomics, the characterization of metabolites and

260 Transcriptomics, the high-throughput characterization of

261 With a focus on transcriptomics, this Review discusses how high-throughp

262 used shotgun proteomics, OxICAT and RNA-seq transcriptomics to analyse protein S-mycothiolation, rev

263 novative techniques such as metabolomics and transcriptomics to comparatively examine resistant-AS ch

264 Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and p

265 in mesenchyme we used tissue and single cell transcriptomics to define mesenchymal subsets and subset

266 resolution mapping of DSBs with multilayered transcriptomics to dissect the events shaping gene expre

267 In this study, we used single-cell type transcriptomics to identify more than 4000 differentiall

268 Treutlein et al. (2016) applied single-cell transcriptomics to identify routes and detours during ea

269 We applied associative transcriptomics to identify sequence polymorphisms linke

270 Here, we use single-cell transcriptomics to identify the molecular signature of N

271 cell division, demonstrating the utility of transcriptomics to predict the occurrence and timing of

272 gly involved multimodal approaches including transcriptomics to profile gene expression.

273 ally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire

274 erein, we used different omics (genomics and transcriptomics) to identify novel biomarkers of thiazid

275 approach that has recently emerged is phylo-transcriptomics (transcriptome-based phylogenetic infere

276 r the onset of lipogenesis was determined by transcriptomics using the oleaginous fungus Mortierella

277 The use of the platform for Associative Transcriptomics was first tested by analysing the geneti

278 Transcriptomics was performed by whole genome microarray

279 genetics, histology, liver damage assays and transcriptomics we discovered that iron deficiency arisi

280 oss Selaginella moellendorffii Using de novo transcriptomics, we confirmed expression of five transcr

281 Using single-cell transcriptomics, we confirmed the presence of mRNAs enco

282 se model of proneural glioma and comparative transcriptomics, we determined that PDGF signaling upreg

283 Combining phylogenetics and transcriptomics, we discovered conservation of a core se

284 -cell laser microdissection with single cell transcriptomics, we establish that interferon-stimulated

285 Via comparative transcriptomics, we established that the GAS CcpA core r

286 Using genome-wide transcriptomics, we find that amoeboid melanoma cells ar

287 d cell of origin, and performing comparative transcriptomics, we identified several EMT-related genes

288 Using forward transcriptomics, we show that beta-catenin/Tcf signaling

289 4GFP knock-in reporter mouse and single-cell transcriptomics, we show that ID4 marks a stem cell-enri

290 in vivo metabolic imaging, metabolomics and transcriptomics, we show that mTORC1 deletion impairs gl

291 Using single-cell transcriptomics, we show that transcript variability eme

292 sue samples through omics-based whole-genome transcriptomics while using healthy individuals as backg

293 Further 'omics' approaches, through GWAS and transcriptomics, will finally shed light on the interact

294 This study presents a method that combines transcriptomics with biophysical recordings to character

295 Combining transcriptomics with high-resolution DamID mapping of nu

296 cation studies should integrate genomics and transcriptomics with longitudinal sampling to elucidate

297 e we sequence 30 fungal genomes, and perform transcriptomics with three representative Rhizopus and M

298 veloped a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface

299 es, representing a major advance for spatial transcriptomics, with exciting potential applications in

300 ptive immune system, using CD4+ T-lymphocyte transcriptomics, would identify gene expression correlat