ライフサイエンスコーパス: gene discovery

1 es (GWAS) are still the primary steps toward gene discovery.

2 standing continues to lag behind the pace of gene discovery.

3 tes critically important data for eukaryotic gene discovery.

4 hich the rate-limiting step may no longer be gene discovery.

5 s to improve the prediction power of disease gene discovery.

6 y, which has historically complicated driver gene discovery.

7 ications in population inference and disease gene discovery.

8 enetic mosaic zebrafish for tumor suppressor gene discovery.

9 sample of deeply phenotyped individuals for gene discovery.

10 ork for evaluating various study designs for gene discovery.

11 des a powerful new tool for familial disease gene discovery.

12 an be used to prioritize variants in disease-gene discovery.

13 al advantages of this founder population for gene discovery.

14 ole genome and RNA sequencing approaches for gene discovery.

15 tic heterogeneity has proven challenging for gene discovery.

16 he development of new approaches for disease-gene discovery.

17 s a powerful technique for Mendelian disease gene discovery.

18 ting and benchmarking applications in fusion gene discovery.

19 ant vertebrate model organism for functional gene discovery.

20 ole exome sequencing can be used for disease gene discovery.

21 rative genomics approach for innate immunity gene discovery.

22 cific ESC reporter line paradigm for in vivo gene discovery.

23 issue components will facilitate eye disease gene discovery.

24 PIs, can guide better strategies for disease gene discovery.

25 provides a powerful alternative strategy for gene discovery.

26 alleles, providing a clear path forward for gene discovery.

27 powerful resource to facilitate ALS disease gene discovery.

28 e homeostasis and to test the feasibility of gene discovery.

29 ts potential of pathway-based approaches for gene discovery.

30 richment information to improve the power in gene discovery.

31 ental aspects of cell biology as well as for gene discovery.

32 e of the most recent technologies for cancer gene discovery.

33 olution, speciation, domestication and novel gene discovery.

34 rapidly become a standard method for disease gene discovery.

35 al and differentiation, and will allow novel gene discovery.

36 rtional mutagenesis is a powerful method for gene discovery.

37 perform wheat EST database mining for nsLtp gene discovery.

38 significantly speed up the process of cancer-gene discovery.

39 utility of our approach for tissue-specific gene discovery.

40 opens the way for its use as a phenotype in gene discovery.

41 atform technology with broad applications in gene discovery.

42 ms, highlighting the value of Drosophila for gene discovery.

43 y the genetic heterogeneity revealed through gene discovery.

44 rom rudimentary genome maps to trait maps to gene discovery.

45 d provide valuable molecular tags for cancer gene discovery.

46 ource for candidate myeloid tumor suppressor gene discovery.

47 ample transcriptome and can accelerate novel gene discovery.

48 e FUSIL as an efficient approach for disease gene discovery.

49 s and highlights the value of MTAR for novel gene discovery.

50 e phenotype, and could lead to novel disease-gene discovery.

51 gregation analysis for novel disease-causing gene discovery.

52 ing ones such as Augustus for more sensitive gene discovery.

53 is suitable for rapid genetic screening and gene discovery.

54 ce has emerged as a powerful tool for cancer gene discovery.

55 ensory cells may hold potential for deafness gene discovery.

56 of most noncoding variants has bottlenecked gene discovery.

57 Brain-based phenotypes could aid gene discovery.

58 ients, underscoring the ongoing need for DCM gene discovery.

59 hting the potential of pleiotropy to improve gene discovery.

60 methods of clinical MSI diagnosis and cancer gene discovery.

61 es; however, none of these represented novel gene discoveries.

62 gh cost of gene testing all hindered earlier gene discoveries.

63 ion of human genomes and can advance disease gene discovery(1-4).

64 We identified 3 factors that limited novel gene discovery: (1) imperfect sequencing coverage across

65 re of autism spectrum disorders (ASDs), with gene discovery accelerating as the characterization of g

66 Here, we present a spatio-temporal gene discovery algorithm, which leverages information fr

67 iscovery process up, a few network-based ASD gene discovery algorithms were proposed.

68 ined a pioneering approach to genomics-based gene discovery, an astute appreciation of translational

69 ases has recently increased because of novel gene discoveries and advancements in DNA sequencing tech

70 new classification, syndromic approach, new gene discoveries and genotype-phenotype correlations.

71 alies continue to be a valuable resource for gene discovery and annotation.

72 nce tags (ESTs) offer a low-cost approach to gene discovery and are being used by an increasing numbe

73 semblies will provide a basis for functional gene discovery and breeding to deliver the next generati

74 ship with SDW supports future efforts toward gene discovery and breeding wheat cultivars with reduced

75 To accelerate maize gene discovery and breeding, we present the Complete-dia

76 ents a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes fo

77 tically tractable model, the fly facilitates gene discovery and can complement mammalian models of di

78 eading to missed opportunities for improving gene discovery and characterization.

79 sequencing and have implications for disease gene discovery and clinical diagnosis.

80 ll replace regionally focused approaches for gene discovery and clinical testing in the next few year

81 strated by applications ranging from disease gene discovery and comparative genomics to species conse

82 nks are free, open-source software tools for gene discovery and comprehensive expression analysis of

83 ailable sequence data but also a gap between gene discovery and crucial mechanistic insights provided

84 Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases.

85 This has, in turn, accelerated the pace of gene discovery and disease diagnosis on a molecular leve

86 ively small chemical libraries to accelerate gene discovery and disease study.

87 tive area of research for both novel disease gene discovery and drug repositioning.

88 tive area of research for both novel disease gene discovery and drug repurposing.

89 this newly created knowledge base in disease gene discovery and drug repurposing.

90 g-specific mechanisms that may guide disease gene discovery and drug repurposing.

91 platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human

92 nome-, transcriptome-, and metabolome-guided gene discovery and enzyme characterization identified no

93 resented here represent a large resource for gene discovery and for confirmation of results obtained

94 also discuss key challenges that remain for gene discovery and for moving from genomic localization

95 t will also serve as a valuable resource for gene discovery and for unraveling the fundamental mechan

96 e as a resource to accelerate the process of gene discovery and function in this model organism.

97 ion atlas represents a valuable resource for gene discovery and functional characterization in maize.

98 Here, we describe the gene discovery and functional characterization of the fi

99 sily quantifiable biochemical markers to aid gene discovery and functional characterization.

100 transcripts will be particularly useful for gene discovery and gene expression analysis of nonmodel

101 alternative to the transgenic approach, for gene discovery and gene function analysis in cassava.

102 sequence tag (EST), which is instrumental in gene discovery and gene sequence determination.

103 ly investigated two approaches to accelerate gene discovery and genome analysis in maize: methylation

104 aboration have led to remarkable progress in gene discovery and have revealed the diverse array of ge

105 ne/signaling protein interaction network for gene discovery and hypothesis generation in plants and o

106 ion, assist genome assembly projects and aid gene discovery and identification.

107 riation, provide a resource for accelerating gene discovery and improving this major crop.

108 ets generated here provide new resources for gene discovery and marker development in this orphan cro

109 urodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease.

110 To facilitate gene discovery and molecular breeding in sorghum, we hav

111 ologies has altered the landscape of current gene discovery and mutation detection approaches.

112 the utility of mouse models for MPNST driver gene discovery and provide new insights into the complex

113 e the power of transcriptional profiling for gene discovery and provide opportunities for investigati

114 r data established an excellent resource for gene discovery and provide useful information for functi

115 there is no single reference system to guide gene discovery and rapid annotation of specialized diter

116 oss multiple traits can improve the power of gene discovery and reveal pleiotropy.

117 egrative modeling approach for both reliable gene discovery and robust GP.

118 at promise to speed up the process of cancer-gene discovery and should be considered to complement ti

119 review the current and future bottlenecks to gene discovery and suggest strategies for enabling progr

120 nduced mutations are important resources for gene discovery and the elucidation of genetic circuits.

121 ess the usefulness of mouse models in cancer gene discovery and the extent of cross-species overlap i

122 While complicating the prospects for gene discovery and the feasibility of mechanistic studie

123 and is therefore a valuable tool for use in gene discovery and the interpretation of personal genome

124 plied at scale have dramatically accelerated gene discovery and transformed genetic medicine.

125 a unique opportunity for additional disease gene discovery and understanding of this pathology.

126 henotypes can serve as phenotypes for future gene discovery and understanding.

127 vels of tolerance to submergence stress, but gene discovery and utilization of these resources has be

128 e promise of many more cancer predisposition gene discoveries, and greater and broader clinical appli

129 Next-generation sequencing has increased gene discovery, and mutations in more than 40 genes have

130 oaches have been used for disease diagnosis, gene discovery, and studying complex traits are provided

131 sposon is an emerging tool for transgenesis, gene discovery, and therapeutic gene delivery in mammals

132 these derived components may prove useful in gene discovery applications.

133 expect that TEPSS will be useful for various gene discovery applications.

134 We used a gene-discovery approach to identify additional candidate

135 Modern gene discovery approaches have identified defective ion

136 ncipally by using linkage-based or candidate gene discovery approaches.

137 involvement in leukaemia or via post-genomic gene discovery approaches.

138 In many instances, these gene discoveries are being rapidly translated into meani

139 Here, we approach autism risk gene discovery as a machine learning problem, rather tha

140 n this article we present a new strategy for gene discovery based on the production of ESTs from seri

141 This strategy of gene discovery, based on the identification of a gene se

142 locus heterogeneity constitute a problem for gene discovery because the usual criterion of finding mo

143 ave been missed by traditional approaches to gene discovery but can be identified by their evolutiona

144 or structural gene annotation have propelled gene discovery but face certain drawbacks with regards t

145 yl-N-nitrosourea) mutagenesis can facilitate gene discovery, but mutation identification is often dif

146 such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few me

147 ion sequencing technologies are accelerating gene discovery by combining multiple steps of mapping an

148 demonstrate the utility of this approach for gene discovery by identifying numerous previously unchar

149 study showed the cFDR approach could improve gene discovery by incorporating GWAS datasets of two rel

150 d a novel framework to improve the power for gene discovery by incorporating prior information of sin

151 vides a cost- and time-efficient approach to gene discovery by integrating chemical mutagenesis and w

152 g has demonstrated great potential for novel gene discovery, confirming disease-causing genes after i

153 study of females with NDDs leads to greater gene discovery consistent with the female-protective eff

154 Despite rapid advances in gene discovery, details concerning the altered protein p

155 n ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL p

156 ished data concerning prevalence, phenotype, gene discovery, disease mechanisms, diagnostic tools and

157 While particularly true for gene discovery, each of these efforts requires substanti

158 ES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

159 that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS suscept

160 ore at an exciting inflection point at which gene discovery efforts are transitioning toward the func

161 Recent gene discovery efforts have expanded the number of known

162 Gene discovery efforts in monogenic diabetes have identi

163 Indeed, gene discovery efforts over the last decade elucidated m

164 d(4,5) behaviors that have been resistant to gene discovery efforts(6-11).

165 more complex brain regions and contribute to gene discovery efforts.

166 , Kif12, fulfills the major criteria for QTL gene discovery established by the Complex Trait Consorti

167 es that group genes for the purpose of novel gene discovery fail to acknowledge the dynamic nature of

168 rms to help bridge the gaps among individual gene discovery, field-level phenotypic plasticity, and g

169 large team science (TS) consortia focused on gene discovery, fine mapping of loci, and functional gen

170 ument the power of whole-exome sequencing in gene discoveries for rare disorders, and illustrate the

171 that it can be used to improve the power of gene discovery for both common and rare diseases.

172 cores may improve fine-mapping and candidate gene discovery for common disease.

173 improve the power of conventional methods of gene discovery for complex diseases should be investigat

174 chnologies are also accelerating the pace of gene discovery for deafness.

175 ican-admixed individuals and will facilitate gene discovery for diseases disproportionately affecting

176 r approach may substantially improve disease gene discovery for diseases with many known risk variant

177 Furthermore, we performed iterative gene discovery for glioblastoma, meningioma and breast c

178 Gene discovery for IVF is important as it enables the id

179 Gene discovery for Mendelian conditions (MCs) offers a d

180 ations, has become increasingly important in gene discovery for schizophrenia.

181 ease genes have been identified for CMT, the gene discovery for some complex form of CMT has lagged b

182 ncrease BE and/or EA risk, greatly expanding gene discovery for these traits.

183 t genome sequence aids mapping and candidate-gene discovery for traits such as seed size and color, f

184 ariants at the transcription level, into the gene discovery framework for a unique human disease, mic

185 re, we review recent developments in disease gene discovery, functional characterization, and shared

186 To advance gene discovery further, we combined data from three stud

187 This large-scale gene discovery gives the broadest depth yet to the annot

188 The pace of gene discovery has quickened due to advances in sequenci

189 Cancer gene discovery has relied extensively on analyzing tumor

190 Applications include gene discovery, high-throughput drug screens or systemat

191 ermore, we propose some new strategies for R gene discovery, how to balance resistance and yield, and

192 unctional annotation and auxiliary metabolic gene discovery imply the potential to influence microbia

193 mouse models of human cancer and for cancer gene discovery in a wide variety of tissues.

194 nism is likely to be a powerful approach for gene discovery in AD and other complex genetic disorders

195 ic aortic aneurysm has been established, and gene discovery in affected families has identified sever

196 opulations are already available for disease-gene discovery in African Americans.

197 However, the past decade of gene discovery in ASD has been most notable for the appl

198 detection have proven a powerful approach to gene discovery in complex neurodevelopmental disorders.

199 ay will accelerate hypothesis generation and gene discovery in disease defense pathways, responses to

200 nd a potentially powerful tagging system for gene discovery in eukaryotes.

201 ing known Mendelian genes, in PhenIX, versus gene discovery in Exomiser) is perhaps not fully appreci

202 significantly enhance the accuracy of cancer gene discovery in forward genetic screens and provide in

203 ies (GWAS) have become the standard tool for gene discovery in human disease research.

204 tify such loci in flies as well as promoting gene discovery in humans.

205 ble PAH, but whether WES can also accelerate gene discovery in IPAH remains unknown.

206 r guiding strain selection to maximize novel gene discovery in large-scale genome sequencing projects

207 s, hPSCs have the potential to revolutionize gene discovery in mammalian development.

208 of the human genome and describe functional gene discovery in mammals not recognized in human EST pr

209 e PiggyBac transposon can be used for cancer gene discovery in mice.

210 outgrowth represents a powerful platform for gene discovery in neuronal regeneration.

211 addresses the particular issues that attend gene discovery in neuropsychiatric and neurodevelopmenta

212 to develop a general strategy for diterpene gene discovery in nonmodel systems.

213 ur findings show a pathway toward systematic gene discovery in OCD via identification of DN damaging

214 In recent years, risk gene discovery in other complex psychiatric disorders ha

215 as an inexpensive and efficient solution for gene discovery in parasitic nematodes.

216 y Pickard et al., entitled "Cytogenetics and gene discovery in psychiatric disorders," highlighted th

217 Gene discovery in psychiatry is, on its own, unlikely to

218 facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-

219 cause of CJS and illustrates strategies for gene discovery in the context of low-level tissue-specif

220 amples of cutaneous mosaicism, approaches to gene discovery in these disorders, and insights into mol

221 To expedite gene discovery in this mouse model of childhood cancer,

222 Gene discovery in this way suggests that we are far from

223 ratory whole-transcriptome approach to virus gene discovery in three different Symbiodinium cultures.

224 lution genome-screens will continue to drive gene discovery in years ahead.

225 lar diseases, key challenges have emerged in gene discovery, in understanding how DNA variants connec

226 This breaks with the past pattern of gene discovery, in which the information flow was most o

227 ders as part of the Brigham Genomic Medicine gene discovery initiative.

228 Validating these gene discoveries is critical because, while PTEN wildtyp

229 roach being applied extensively in candidate gene discovery is gene expression analysis of human and

230 By downsampling, we find that DNM gene discovery is greatest when studying affected female

231 LRR-RLK gene tree, we developed an improved gene discovery method based on iterative hidden Markov m

232 Here, by use of gene discovery methods in the green alga Chlamydomonas,

233 emonstrate the utility of applying proteomic gene discovery methods to a specific biological process

234 architecture of blood pressure, and whether gene discoveries might influence cardiovascular risk ass

235 study on autism using two Chinese cohorts as gene discovery (n=2150) and three data sets of European

236 lls, in silico variant modeling and modifier gene discovery, now in their earliest stages, will help

237 netics and genomics offer new approaches for gene discovery of adult cardiac phenotypes to identify e

238 in consideration of (a) rapid development in gene discovery of important traits, (b) deepened underst

239 sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian

240 t of high-throughput sequencing (HTS) on ASD gene discovery, outline a consensus view for leveraging

241 made in computational approaches for fusion gene discovery over the past 3 years due to improvements

242 al development, introduced a period of rapid gene discovery over the past decade.

243 This approach increased the yield of gene discovery over what would be obtained if each disor

244 to continue in research settings for causal gene discovery, pharmacogenetic purposes, and gene-gene

245 of a state-of-the-art toolbox in the driver gene discovery pipeline.

246 To speed the gene discovery process up, a few network-based ASD gene

247 ime to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to a

248 n plants through a massive transcriptome and gene discovery project involving Triphysaria versicolor

249 ore the utility of SB for large-scale cancer gene discovery projects, we have generated mice that car

250 Using data from Mendelian disease-gene discovery projects, we show that ALoFT can distingu

251 rough a convergence of data involving mutant gene discovery, proteomics, and cell biology, more than

252 Gene discovery provides the basis for neurobiological in

253 l FDR improved power of traditional GWAS for gene discovery providing a useful framework for the anal

254 methods provided a 7- to 8-fold increase in gene discovery rates as compared to random sequencing.

255 There is widespread agreement that cancer gene discovery requires high-quality tumor samples.

256 s for constitutive or tissue-specific cancer gene discovery screening.

257 Proteomic-based de novo gene discovery should be especially useful for sets of g

258 ells for preclinical applications, including gene discovery, simultaneous multiplexed genome modifica

259 son insertional mutagenesis to enable cancer gene discovery starting with human primary cells.

260 data are essential for modeling studies and gene discovery strategies needed to introduce aspects of

261 This review focuses on gene discovery strategies used to identify monogenic for

262 ved has significant implications for ongoing gene discovery strategies.

263 ptional profiling with multiple tissues as a gene discovery strategy for low-abundance proteins.

264 We analyze the potential of the gene discovery strategy that combines multiple rare vari

265 Here we introduce a three-step gene discovery strategy to identify genetic factors modi

266 Gene discovery studies focused on these strains will gre

267 hese novel findings highlight a new role for gene discovery studies in furthering our understanding o

268 While many gene discovery studies in the past were led by knowledge

269 trong motivation for undertaking psychiatric gene discovery studies is to provide novel insights into

270 is, treatment, and selection of patients for gene discovery studies.

271 mportant for beta-cell function prior to the gene discovery study.

272 We report a gene discovery system for poplar trees based on gene and

273 Applications of SSA for gene discovery, target discovery, and generation of muta

274 the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions th

275 third, we propose an iterative procedure for gene discovery that operates via successful augmentation

276 ly, hexanucleotide expansions in the C9orf72 gene, discoveries that highlight the overlapping pathoge

277 very, the uses of GxE research as a tool for gene discovery, the importance of construct validation i

278 rried out before as well as after replicated gene discovery, the uses of GxE research as a tool for g

279 these large-scale analyses in the context of gene discovery, therapeutic application and building a m

280 at non-allelic genetic heterogeneity hampers gene discovery, this study demonstrates the utility of r

281 decade, limma has been a popular choice for gene discovery through differential expression analyses

282 ders (ASDs), is to advance the findings from gene discovery to an exposition of neurobiological mecha

283 past decade, we outline achievements in rat gene discovery to date, show how these findings have bee

284 grated developmental transcriptome data with gene discovery to generate testable hypotheses about whe

285 receptor PD-1 in cancer immunotherapy, from gene discovery to patient benefit, have created a paradi

286 This approach was also applied to ab initio gene discovery to support the identification of a de nov

287 Using yeast as an effector gene discovery tool allows for a powerful, genetic appro

288 However, their use as a cancer gene discovery tool has been limited to only a few tissu

289 lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS

290 Phenotype-driven approaches to gene discovery using inbred mice have been instrumental

291 are discussed for new innovations in drought gene discovery using platforms targeting the extracellul

292 hese disorders, a forward genetics method of gene discovery was used to identify additional affected

293 Using a systematic approach toward modifier gene discovery, we have found five chromosome I genes th

294 In order to further accelerate clock gene discovery, we utilized a computer-assisted approach

295 ucleotide polymorphism data set tailored for gene discovery, well-documented analytical strategies, a

296 Our study shows gains in gene discovery when using dense imputation from multi-et

297 Two types of antibiotic resistance gene discoveries will be discussed: the use of classic m

298 curate map of broad causal pathways to SUDs, gene discovery will be needed to identify the specific b

299 of this RNAi library approach for functional gene discovery within a predefined protein family.

300 ses the power of exome sequencing in disease gene discovery within the rare genodermatoses and the ro