ライフサイエンスコーパス: genomic data

1 hods to keep pace with the rapid increase in genomic data.

2 it difficult to correlate phenotypic data to genomic data.

3 pigenomic datasets for the interpretation of genomic data.

4 n of NGS datasets, as well as other types of genomic data.

5 ic model limits performance on many types of genomic data.

6 edge to reduce the dimensionality of complex genomic data.

7 stigates evolutionary relationships based on genomic data.

8 timate the extent of uncertainty in reported genomic data.

9 ese approaches in the application to complex genomic data.

10 voluntary and secure sharing of clinical and genomic data.

11 monly used to infer individual ancestry from genomic data.

12 face the need to explore massive amounts of genomic data.

13 tures so that researchers may better analyze genomic data.

14 f MERS-CoV among different host species with genomic data.

15 onary lineages from tumors using single-cell genomic data.

16 iomedical researchers to collect large-scale genomic data.

17 pression is crucial in effective handling of genomic data.

18 ithm of the number of records in practice on genomic data.

19 sequencing has resulted in a rapid growth of genomic data.

20 cancer-related pathways from high-throughput genomic data.

21 and infiltrating cell populations to pooled genomic data.

22 for the secure storage of compressed aligned genomic data.

23 age, slightly beneficial yield a good fit to genomic data.

24 sualization of all published high-throughput genomic data.

25 ethods present real challenge in integrating genomic data.

26 create and deploy all-new visualizations of genomic data.

27 o identify GxE interactions using functional genomic data.

28 mo, a web-based search engine for functional genomic data.

29 fective solution for the storage of clinical genomic data.

30 viduals' phenotypes for complex traits using genomic data.

31 can be inferred from detailed functional and genomic data.

32 significant step in functional analysis for genomic data.

33 d ZIKV evolution, in part owing to a lack of genomic data.

34 with dictyBase-a repository of Dictyostelium genomic data.

35 st malaria based on antigens identified from genomic data.

36 horts and to explore the wealth of available genomic data.

37 s being transformed by analyses of these new genomic data.

38 ich was not found based on associations with genomic data.

39 among tribes of Chrysopinae based on the mt genomic data.

40 to mimic very closely the properties of real genomic data.

41 d by rigorous analysis and interpretation of genomic data.

42 ic by enabling simple navigation of personal genomic data.

43 al information retrieval in large volumes of genomic data.

44 r genes identified through multi-dimensional genomic data.

45 sst), and test it on both simulated and real genomic data.

46 ch pneumococcal isolate was determined using genomic data.

47 identify putative targets of selection from genomic data.

48 ative CCR genes were discovered from sorghum genomic data.

49 substantially improved by large expansion of genomics data.

50 he functional activity of DNA sequences from genomics data.

51 torage of, and operation on multiple diverse genomics data.

52 of both GWAS and eQTL results and functional genomics data.

53 acilitate the joint testing of multiplatform genomic data across an entire gene set.

54 are population specific, and that examining genomic data across diverse ancestries may facilitate th

55 ucers of brain-derived epigenetic functional genomic data, albeit initially from only two cancerous b

56 ne flow), and the widespread availability of genomic data allow such parameters to be estimated with

57 systematic study to reconsider a reliance on genomic data alone.

58 apping, analysis of genome synteny and other genomic data analyses.

59 High-dimensional genomic data analysis is challenging due to noises and b

60 tation') coverage and accuracy, and improved genomic data analysis tools.

61 lue procedure and maintain its high power in genomic data analysis, we propose a new multiple testing

62 example of best practices for development of genomics data analysis workflows by integrating remote H

63 PCA) is a crucial step in quality control of genomic data and a common approach for understanding pop

64 be applied to other taxa in order to extract genomic data and address new ecological and evolutionary

65 This genomic data and an ETS1 deletion line reveal that the o

66 entified from analyses on transcriptomic and genomic data and analyzed these in conjunction with shif

67 resurgent interest as potential archives of genomic data and for the unique perspective they provide

68 ations show that links between de-identified genomic data and named persons can sometimes be reestabl

69 We consider how best to leverage genomic data and recent experimental developments in ord

70 ve analysis of the lncRNA transcriptome with genomic data and SNP data from prostate cancer genome-wi

71 vides a new way to interpret existing cancer genomic data and to discriminate between functional and

72 The GDC aims to democratize access to cancer genomic data and to foster the sharing of these data to

73 ganizations to accumulate massive amounts of genomic data and use that data to answer a diverse range

74 L can replace existing practices to retrieve genomic data and, as we show, allow users to reduce the

75 integrating chemical genomics and structural genomics data and by introducing a functional site inter

76 Existing tools utilize functional genomics data and evolutionary information to evaluate t

77 our data with publicly available functional genomics data and identified a growth regulatory network

78 We integrate genetic, clinical and genomics data, and draw upon findings from non-mammalian

79 opportunities to rapidly collect and analyse genomic data anywhere.

80 In the analysis of current genomic data, application of machine learning and data m

81 In addition, genomic data are being produced at an increasingly faste

82 Text and genomic data are composed of sequential tokens, such as

83 The EBOV genomic data are consistent with that tree.

84 fy potentially causative variants, even when genomic data are limited.Obsessive-compulsive disorder (

85 aches combining regulatory networks (RN) and genomic data are needed to extract biological informatio

86 Genomic data are often categorical and high dimensional,

87 es provide evidence for large-scale consumer genomic data as a powerful and efficient complement to d

88 ssibility of brain epigenetic and functional genomic data as a single resource to allow investigators

89 Using the same raw read genomic data as input, there are several different appro

90 esulted in the generation of huge amounts of genomic data, as evidenced by projects such as the UK10K

91 ng code modeling as vast amounts of relevant genomic data become available.

92 Existing unrestricted-access genomic data browsing resources provide only summary sta

93 ave become a central tool in the analysis of genomic data but are widely regarded as hard to interpre

94 understanding cell biology and interpreting genomic data, but challenging to produce experimentally.

95 linically oriented pathway-based analysis of genomic data can accelerate the discovery of rare geneti

96 The contribution of each type of genomic data can be weighted, permitting integration of

97 By detailing the ways in which genomic data can help us understand viral disease outbre

98 ical traits and demographic information from genomic data challenges privacy and data deidentificatio

99 s (e.g. up-to-date literature and pan-cancer genomic data collection and curation), data types (nonco

100 ty of this framework by applying it to other genomic data collection and sharing endeavors.

101 mpressed storage of arbitrary types of large genomic data collections.

102 ate the analysis and interpretation of mtDNA genomic data coming from next generation sequencing (NGS

103 miRNAs in breast cancer were investigated in Genomic Data Common data portal miRNA-Seq dataset and Th

104 The National Cancer Institute Genomic Data Commons (GDC) is an information system for

105 The NCI Genomic Data Commons (GDC) was launched in 2016 and make

106 in future development and analyses of lossy genomic data compressors.

107 Independent population genomic data corroborate QTL regions as areas of high di

108 bined with future work on this strain, these genomic data could help provide potential new targets fo

109 Multiple types of high throughput genomics data create a potential opportunity to identify

110 rst, assuming an underlying structure in the genomic data, data mining might identify this and thus i

111 sively via command line interface to display genomic data directly in a terminal window.

112 A multi-genomic, data-driven approach, utilizing 106 human non-s

113 onding assessments for continuous functional genomic data (e.g. chromatin immunoprecipitation-sequenc

114 These genomic data elucidate early genome evolution in Brassic

115 GWAS using a genomic data-enriched LCL model system, together with fu

116 findings demonstrate the importance of tumor genomic data, especially IFN-gamma related genes, as pro

117 In the simulation study and a real genomic data example, we show how to boost association r

118 We generated genomic data for 1318 individuals from 35 populations in

119 HGD maintains genomic data for 9 bee species, 10 ant species and 1 was

120 RISPR-Cas9 combined with the availability of genomic data for a range of insects renders this strateg

121 Our combined analyses of genomic data for Brassicaceae indicate that extant chrom

122 s these challenges and facilitates access to genomic data for key reference projects in a clean, fast

123 the paradigm-shifting evidence derived from genomic data for sex and for the lack of heterokaryosis,

124 iological features can be inferred, based on genomic data, for many microbial lineages that remain un

125 herapy response and assess its prediction in genomic data from 10,000 human tissues across 30 differ

126 We used genomic data from 1070 Vibrio cholerae O1 isolates, acro

127 deficit hyperactivity disorder (ADHD) using genomic data from 150,656 Icelanders, excluding those di

128 Here we compare genomic data from 65 kidney-derived cell lines from the

129 Functional genomic data from a genome-wide shRNA screen in Ewing sa

130 We analyzed relevant genomic data from all currently available sequenced orga

131 Prokaryotic genomic data from all sources were collected and combine

132 For genomic data from apes, SISRS identified thousands of va

133 We analysed genomic data from approximately 500 taxa detected in thi

134 Employing integration of functional genomic data from c-Myc cistromics, 1000 Genomes, and th

135 While analyzing genomic data from calcaronean sponges Sycon ciliatum and

136 In cancer research, genomic data from cell lines are often utilized as featu

137 atasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sa

138 spread of the Scythian culture, we analysed genomic data from eight individuals and a mitochondrial

139 We gathered genomic data from grapes (Vitis vinifera ssp. vinifera),

140 tic children in Mbita, western Kenya, and 61 genomic data from Kilifi, eastern Kenya, were available

141 Analysing population genomic data from killer whale ecotypes, which we estima

142 ested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals c

143 Genomic data from more than 20,000 cancer genomes provid

144 flexible platform that allows integration of genomic data from multiple projects.

145 Here, we use new genomic data from over 1,000 uncultivated and little kno

146 bust protocols that enable secure sharing of genomic data from participants in genetic research.

147 hic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberc

148 Here, leveraging population genomic data from Saccharomyces cerevisiae, Schizosaccha

149 Here, we use heterochronous genomic data from samples obtained before and immediatel

150 ases to the systematic analysis of extensive genomic data from several species.

151 f variant calling, as tested on low-coverage genomic data from soybean.

152 evaluate the performance of the method using genomic data from synthetic and real tumor samples.

153 In analyses of genomic data from The Cancer Genome Atlas (TCGA), we obs

154 e, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas.

155 The ability to generate genomic data from these mammalian parasites, even from s

156 tions and apply it to a large set of ancient genomic data from Western Eurasia.

157 We analyzed genomics data from 136 uveal melanoma samples and found

158 and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006

159 n other biological disciplines as population genomic data grows.

160 theless, the availability of high-resolution genomic data has led to the development of new methodolo

161 The recent explosion of genomic data has underscored the need for interpretable

162 Recent genomic data have revealed multiple interactions between

163 The scarcity of NPC genomic data hinders the understanding of NPC biology, d

164 We combine modern and ancient genomic data in a simple statistic (DAnc) to time allele

165 archers can search through all available raw genomic data in a way similar to OMIM for genes or Unipr

166 neficial to store patients' complete aligned genomic data in addition to variant calls relative to a

167 most straightforward ways to explore complex genomic data in an epidemiological context.

168 ated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with dispa

169 cer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains ch

170 ronment to visualize multidimensional cancer genomic data in two layouts: matrix layout and combined

171 ng (NGS) has facilitated a massive influx of genomics data in the form of short reads.

172 ypes is an essential part of the analysis of genomic data, including in identification of sequence po

173 he interactive visualization and analysis of genomic data, including integrated features to support d

174 rs worldwide have generated a huge volume of genomic data, including thousands of genome-wide associa

175 roved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and

176 th the increasing availability of functional genomic data, incorporating genomic annotations into gen

177 Comparative genomics data indicate that these loci, and genomic isla

178 Genomic data indicated M. irregularis is heterothallic h

179 customized databases that utilize published genomics data integrated with experimental data which ca

180 ocus is to interpret and transform collected genomic data into biological information.

181 Translating the readily available genomic data into useful knowledge that can be applied i

182 road array of features (N = 432), including: genomic data, intra-, and interspecies conservation, gen

183 The segmentation of time series and genomic data is a common problem in computational biolog

184 In addition, privacy of genomic data is becoming an increasingly serious concern

185 tifying latent structure in high-dimensional genomic data is essential for exploring biological proce

186 is concern that the emergence of large-scale genomic data is exceeding our capacity to appropriately

187 discovering such therapeutic insights using genomic data is not straightforward and represents an ar

188 e exploration of different ways to visualize genomic data is still challenging due to the lack of fle

189 common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold in

190 For example, genomics data is increasingly large and distributed, and

191 With the fast growing genomic data, it is becoming increasingly critical for b

192 ing volume and complexity of high-throughput genomic data make analysis and prioritization of variant

193 In addition to genomic data, metabolome and transcriptome are increasin

194 Genomics Data Miner (GMine) is a user-friendly online so

195 n this article, we introduce a Collaborative Genomic Data Model (CGDM), aimed at significantly increa

196 ation tools enable the exploration of cancer genomics data, most biologists prefer simplified, curate

197 genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that

198 We review studies of genomic data obtained by sequencing hominin fossils with

199 a computational study of transcriptomic and genomic data of both ethanol-stressed and ethanol-adapte

200 ional sparse associations in deep sequencing genomic data of multi-ethnic individuals with random rel

201 Subsequently, genomic data of N = 105 cases of human NAFLD and N = 32

202 Analysis of genomic data of yeast mutation accumulation lines and hu

203 The integrative analysis of multiple genomics data often requires that genome coordinates-bas

204 Using genomic data on 88 P. vivax samples from western Thailan

205 Despite a plethora of genetic/genomic data on platelet reactivity, there are relativel

206 -state evolutionary model to the comparative genomic data on the gene content and gene order similari

207 ient screening approach across multiplatform genomic data on the level of biologically related sets o

208 flexible enough to automate the analysis of genomic data or even data from other NGS technologies.

209 eyond those that can be obtained from modern genomic data or the fossil and archaeological records al

210 ling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostic

211 roposed on the basis of the experimental and genomic data presented.

212 innovation for the generation of single-cell genomics data presents new challenges and opportunities

213 Massive amounts of high-throughput genomics data profiled from tumor samples were made publ

214 cal records are scarce, and demonstrate that genomic data provide another type of record that can she

215 ited utility of the concordance criterion as genomic data provide ever-increasing levels of resolutio

216 For high heritability traits, genomic data remain the most efficient predictors.

217 ction of corresponding target sequences from genomic data remains challenging.

218 Sequencing data are available in the genomics data repository GEO under reference series GSE8

219 However, querying genomic data requires a computer terminal and computatio

220 clusion of web services designed to federate genomic data resources allows the information on Cereals

221 gene expression in large transcriptomic and genomic data resources, and examined expression using lu

222 To improve access to massive genomic data resources, we have developed a fast search

223 der goal of increased ancestral diversity in genomic data resources.

224 in X-ray, computed tomography (CT) scan, and genomic data, respectively.

225 example, to observe whether a clustering of genomic data results in a meaningful differentiation in

226 mplification biases that limit the amount of genomic data retrieved from a single cell.

227 Surprisingly, genomic data revealed that the majority of individuals i

228 Transcriptomic and genomic data revealed three IF protein genes in the tard

229 Population genomics data revealed that genomic regions encoding bio

230 dopods that we call "alpha-motility." Mining genomic data reveals a clear trend: only organisms with

231 M, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage.

232 B. stacei and B. hybridum, for which a large genomic data set has been compiled.

233 We apply SMITE to a complex genomic data set including the epigenomic and transcript

234 om the reanalysis of a published genetic and genomic data set through iFORM/eQTL gain new discoveries

235 address this problem by generating multiple genomic data sets for three different cancer cell lines,

236 Large genomic data sets generated with restriction site-associ

237 and expression of the PIM1 proto-oncogene in genomic data sets of patients with TNBC.

238 n this study, we generate eight high-quality genomic data sets of the filamentous ascomycete Neurospo

239 aur can enable institutions handling massive genomic data sets to shift part of their analysis to the

240 ovarian cancer molecular profiles, including genomic data sets, from four patient cohorts identifying

241 as the candidate at the locus by integrating genomic data sets.

242 erate biologically important results on real genomic data sets.

243 ent workflow for gene flow identification in genomic data sets.

244 als by uniformly processing 1,263 functional genomics data sets, developing approaches to reduce the

245 drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCG

246 owever, due to the difficulty of integrating genomics data sets, the relationships among these domain

247 ive literature addresses obstacles to global genomic data sharing, yet a series of public polls sugge

248 more effective, quantifiable protections for genomic data sharing.

249 quests, while still facilitating responsible genomic data-sharing.

250 Genomic data shed new light on transmission dynamics and

251 rs to search for and access a range of human genomic data sources through a single, easy-to-use inter

252 wing for unprecedented levels of security in genomic data storage.

253 is building an online platform that indexes genomic data stored in repositories and thus enables res

254 te ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implement

255 rogrammatic access to protein and associated genomics data such as curated protein sequence positiona

256 Genomic data suggest donor-to-recipient transmission of

257 ry dead end, morphological, cytogenetic, and genomic data suggest that bdelloid rotifers, a clade of

258 The genomic data suggest that C. trachomatis respiratory cha

259 With the recent increase in the amount of genomic data that is being produced and the ever-growing

260 quencing has enabled the rapid generation of genomic data that predict the locations of CREs, but a b

261 a principle of proportionality be applied to genomic data that weighs the depth of data (what is shar

262 ined with routinely collected patient biopsy genomic data, this method can enable a richer understand

263 tegrates open chromatin, gene expression and genomic data to accurately infer monomeric and homodimer

264 n of WGS, this study used publicly available genomic data to evaluate a duplex real-time PCR (RT-PCR)

265 e appropriate or inappropriate for analyzing genomic data to examine allele-specific expression.

266 g experiments with population and functional genomic data to examine neo-XY chromosome evolution and

267 l dependencies, is one method that leverages genomic data to identify differential genetic dependenci

268 orking to promote efficient sharing of human genomic data to improve the outcome of genomic research

269 Using reduced genomic data to infer species-specific demographical par

270 ays probe functional phenotypes that connect genomic data to patient health.

271 g in WS; and provide new histopathologic and genomic data to test several popular models of WS diseas

272 The addition of biobanks and genomic data to the information contained in the electro

273 We utilize genomic data to uncover components of distant pedigrees,

274 these markers, along with transcriptomic and genomic data, to identify distinct sex chromosomes in bo

275 Some of the common genomic data types are supported and data access on remo

276 outcome, requires the integration of several genomic data types for which an 'integrate by intersecti

277 nalysis tools applicable to a broad array of genomic data types.

278 visualize integrated multidimensional cancer genomic data under R environment.

279 Presently, users must search for relevant genomic data using a keyword, accession number of meta-d

280 Millions of individuals have access to raw genomic data using direct-to-consumer companies.

281 portantly, the integration of functional and genomic data using HitWalker allowed for prioritization

282 By combining structural, functional and genomic data we have assessed a novel bacterial protein

283 In this context of constantly growing genomic data, we discuss how screening strategies must b

284 Using re-sequenced whole genomic data, we estimated that the effective population

285 Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure

286 rophysiological, neuroimaging, proteomic and genomic data were available.

287 ansion-were contrasted between subtypes, and genomic data were correlated to histologic and clinical

288 Genomic data were experimentally validated for PSMA expr

289 These questions can be addressed with genomic data, which can rule out artifacts by demonstrat

290 ata content is mostly genomic and functional genomic data while new data types include protein microa

291 New technologies for acquisition of genomic data, while offering unprecedented opportunities

292 However, the genomic data will be of limited use without a more mecha

293 This combined set of phenotypic and genomic data will enable hypothesis testing to elucidate

294 es a generalized linear model for functional genomic data with a probabilistic model of molecular evo

295 Furthermore, by combining the genomic data with genetic analysis of an additional 800

296 The integration of genomic data with high-tech animal instrumentation comes

297 ant implications that integrating additional genomic data with multivariate statistics can help ident

298 sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific prop

299 summary, we show how systematic analysis of genomic data within a regulatory context can help interp

300 egrating our proteomic measurements with the genomic data yielded a number of insights into disease,