ライフサイエンスコーパス: RNA-Seq

1 is to allow for longitudinal RNA sequencing (RNA-seq).

2 of ~550,000 SNPs (Illumina 50 K SNP Chip and RNA-seq).

3 ivated cell sorting for bulk RNA sequencing (RNA-Seq).

4 igated using whole-transcriptome sequencing (RNA-seq).

5 4 versus n = 20) by immunohistochemistry and RNA-Seq.

6 tive splicing biomarkers from LC-MS/MS using RNA-Seq.

7 nes with corresponding expression changes by RNA-seq.

8 as well as 26 additional cultures using bulk RNA-seq.

9 may be applied to both single-cell and bulk RNA-seq.

10 by overdispersion and excessive dropouts in RNA-seq.

11 sorted and their gene expression compared by RNA-Seq.

12 uvenile yellow perch through RNA-sequencing (RNA-seq), after their initial introduction to a formulat

13 icroscopy, transmission electron microscopy, RNA-Seq analyses and RNA in situ hybridisation were used

14 A total of 627 RNA-seq analyses are performed for 224 maize accessions

15 Here, using RNA-Seq analyses of clinical and preclinical samples, al

16 In RNA-Seq analyses of human brain samples from the NYGC AL

17 e of new spinach genome resources to conduct RNA-seq analyses of transcriptomic changes in leaf tissu

18 RNA-Seq analyses revealed that ROCK2 controlled a unique

19 ATAC-seq and RNA-seq analyses showed that CHD1 loss resulted in globa

20 RNA-seq analyses showed that MITF A isoform (MITF-A) was

21 nocarcinoma model through DNA methyl-Seq and RNA-Seq analyses.

22 In conclusion, RNA-seq analysis appears to offer an informative genetic

23 RNA-Seq analysis demonstrates down-regulated expression

24 Results from RNA-seq analysis determined shared and unique functional

25 ingle-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of in

26 RNA-seq analysis identified insulin-like growth factor-b

27 RNA-seq analysis in human lung cancer cell line H1299 re

28 FAP cell type was also found in single cell RNA-seq analysis in mouse.

29 RNA-Seq analysis in untransformed cells showed that they

30 nomic actions of budesonide were analyzed by RNA-Seq analysis of 24 hours treated HASM, with and with

31 RNA-Seq analysis of ASH1L knockout versus WT ATC cell li

32 ary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed ne

33 RNA-Seq analysis of freshly isolated intestinal crypt ce

34 Using RNA-seq analysis of mouse neocortical polysomes, here we

35 RNA-seq analysis of QPM and o2 endosperms reveals a grou

36 We used RNA-Seq analysis of RNA from exhumed seeds and quantitat

37 RNA-Seq analysis of total retinal cells mainly brought t

38 Europe (OPTiMiSE) programme, we conducted an RNA-Seq analysis on 188 subjects with first episode psyc

39 RNA-seq analysis revealed alterations in pathways and ge

40 RNA-seq analysis revealed that NME1(LOW) cells express e

41 Further characterization by RNA-seq analysis showed LOXL2 promotes proteoglycan netw

42 RNA-Seq analysis showed that both ID2 and Cysteine-rich

43 Here we use single cell RNA-seq analysis to characterize latency in monocytes an

44 In this study, RNA-seq analysis was used to assess changes in ET-1 medi

45 Using a time-course RNA-seq analysis we identified transcriptomic changes du

46 tworks behind DB infestation to date palm by RNA-Seq analysis.

47 elated to leukocyte recruitment, and hepatic RNA-seq analysis.

48 Transcriptome sequencing (RNA-seq) analysis of IRAK4 knockout cell lines (IRAK4 KO

49 per-enhancer activities can be quantified by RNA-seq and a user-friendly data portal, enabling a broa

50 as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available.

51 Cross-examination of RNA-seq and ATAC sequencing data obtained at different t

52 -1 mice were chronically fed EtOH, and ileum RNA-seq and bioinformatic analyses were performed.

53 When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average

54 Combined RNA-seq and ChIP-seq analysis of lymphomas from Lck-Dlx5

55 Using RNA-seq and ChIP-seq approaches we identified genes regu

56 ltasigG1 and DeltarsfG strains combined with RNA-seq and ChIP-seq experiments, suggests the involveme

57 Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin an

58 Using RNA-seq and ChIP-seq, we determined that H-NS and LuxR c

59 By generating a multi-stress and species RNA-Seq and experimental evolution dataset, we highlight

60 In this study, RNA-Seq and functional assays were performed to define t

61 Combined with EU-RNA-seq and Hi-C analyses, we found that during prometap

62 We performed RNA-seq and histone mark ChIP-seq to define the transcri

63 RNA-Seq and immunoblotting analyses showed that MEOX1 kn

64 Using a combination of single-cell RNA-seq and lineage-tracing, we find that progenitor cel

65 Second, the combined application of RNA-Seq and MetaRibo-Seq identifies differences in the t

66 and expression of linc-SPRY3-2/3/4 in NSCLC RNA-seq and microarray data revealed a negative correlat

67 ed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, tra

68 e features of readthrough transcription from RNA-seq and other next-generation sequencing (NGS) assay

69 Using transcriptome-wide RNA-Seq and polysome profiling-Seq in halofuginone-treat

70 this approach as CARP: Combined Analysis of RNA-seq and PRO-seq.

71 that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (

72 RNA-Seq and qRT-PCR analyses reveal that auxin-responsiv

73 RNA-seq and Ribotag analyses identified classes of mRNAs

74 Gene expression was analyzed using RNA-Seq and RT-PCR.

75 ic data, including bulk RNA-seq, single-cell RNA-seq and spatial transcriptomic datasets.

76 RNA-seq and systems biology analysis revealed a striking

77 equent analysis of relevant A1CF functional (RNA-seq) and binding data (PAR-CLIP).

78 ta-cells followed by transcriptome analysis (RNA-seq) and immunohistology identified cell- and stage-

79 profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analys

80 n accessibility (ATAC-Seq), gene expression (RNA-Seq), and adipocyte differentiation.

81 ner ears utilizing otoscopy, RNA sequencing (RNA-seq), and histopathological analysis.

82 Integrated analyses of ChIP-seq, RNA-seq, and patient prognosis identified sphingosine ki

83 hensive atlas comprising ATAC-seq, ChIP-seq, RNA-seq, and proteomics datasets.

84 ling pathways were examined by Western blot, RNA-seq, and qPCR.

85 e methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms

86 es of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequ

87 e PCR, immunostaining, reporter gene assays, RNA-Seq, and two-photon glutamate uncaging with calcium

88 With the present study, we used a RiboTag/RNA-Seq approach to explore the timing of maternal mRNA

89 Here, using a high-throughput RNA-Seq approach, we examined genome-wide circadian and

90 Using an RNA-seq approach, we identified the increased expression

91 Using an RNA-seq approach, we show that several members of the ER

92 RNA-seq approaches allowed us to map the details of urid

93 ssumption that mRNA abundance estimates from RNA-seq are reliable estimates of true expression levels

94 9 functional screening that uses single-cell RNA-seq as readout.

95 a cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RN

96 pathways can be revealed by RNA sequencing (RNA-seq) at up to hundreds of folds reduction in convent

97 RNA-seq based analysis of 310 induced pluripotent stem c

98 Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are fac

99 Both short-read RNA-seq-based HLA typing and BCR/TCR repertoire sequenci

100 RNA-Seq-based transcriptome analysis of ask1 uncovered a

101 Thus, dual RNA-seq can provide insight into the biology and host-pa

102 cent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quali

103 Analysis of a combination of RNA-seq, Capture Hi-C, and patient survival data suggest

104 -iChip to purify CTCs from PDAC patients for RNA-seq characterization, we identify three major correl

105 Using reporter assays, RNA-seq, ChIP-seq, and loss-of-function mutations, we ca

106 id wheat nuclear architecture by integrating RNA-seq, ChIP-seq, ATAC-seq, Hi-C, and Hi-ChIP data.

107 RNA-Seq/ChIP-Seq and a subsequent modifier screen reveal

108 ce, C57BL/6J and BALB/cJ, and deploying deep RNA-Seq complemented with quantitative RT-PCR, we found

109 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 addi

110 he dropout pattern by binarizing single-cell RNA-seq count data, and present a co-occurrence clusteri

111 translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rati

112 a single individual and to population-scale RNA-seq data across many individuals, we detect ASAS eve

113 ey four computational strategies for nuclear RNA-seq data analysis and develop a new pipeline, Tuxedo

114 By integrating RNA-seq data and GWAS summary statistics, novel computat

115 from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candi

116 eloped to detect circRNAs from rRNA-depleted RNA-seq data based on back-splicing junction-spanning re

117 xpression levels of APA isoforms from 3'-end RNA-Seq data by exploiting both paired-end reads for gen

118 Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway

119 es, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

120 By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a

121 Using RNA-Seq data from a study of major depressive disorder (

122 We also compare these methods using real RNA-seq data from a study of major depressive disorder.T

123 Further, RNA-Seq data from APP/PS1 hippocampal tissue revealed th

124 We exemplify the method on sets of RNA-seq data from human tissues obtained though the Geno

125 Using rat retina RNA-seq data from ischemic and normal conditions, we sho

126 ementary analysis of endothelial single cell RNA-Seq data identified the molecular signatures shared

127 The raw RNA-Seq data is freely available for download from NCBI

128 Publicly available RNA-seq data is routinely used for retrospective analysi

129 Applying PAIRADISE to replicate RNA-seq data of a single individual and to population-sc

130 yses using chromatin immunoprecipitation and RNA-seq data revealed that the transcriptomic difference

131 ss referencing bulk RNA-seq with single-cell RNA-seq data revealed the CNTF responsive cell types, in

132 al principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the extern

133 ed by joint analysis of large collections of RNA-seq data sets has emerged as one such analysis.

134 Applying PRAM to 30 human ENCODE RNA-seq data sets identified unannotated transcripts wit

135 Our ChIP-seq and RNA-seq data sets provide an excellent resource for comp

136 es in multiple simulated and real biological RNA-seq data sets with positive control outlier samples.

137 e application of PRAM to mouse hematopoietic RNA-seq data sets.

138 RNA-Seq data showed reduced expression of genes associat

139 presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biom

140 e present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to

141 ity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose (56)F

142 Analysis of three independent RNA-seq data verified the XIST-associated skewed AE on X

143 Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specific

144 as then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously re

145 ), we present evidence that subsampled tumor RNA-seq data with a few hundred thousand reads per sampl

146 ch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering

147 common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to

148 By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints,

149 Compared to previous SBT methods, on real RNA-seq data, HowDe-SBT can construct the index in less

150 q library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for disco

151 ods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches tha

152 n the S. lycopersicoides genome sequence and RNA-Seq data, two of the eight genes emerged as the stro

153 on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved perfo

154 Using single-cell RNA-seq data, we showed that the core regulatory modules

155 ce of ssNPA on liver development single-cell RNA-seq data, where the correct cell timing is recovered

156 ion of full-length transcripts in short-read RNA-Seq data, which encourages the development of method

157 nalysis of large collections of CLIP-seq and RNA-seq data.

158 ection and quantification results from small RNA-seq data.

159 malization method, labeled MIXnorm, for FFPE RNA-seq data.

160 tic, germline and artifact indels from tumor RNA-Seq data.

161 oped for differential expression analysis of RNA-seq data.

162 5p and 3p arms based on user-provided small RNA-seq data.

163 ta using standard methods developed for bulk RNA-seq data.

164 ession transformations to robustly decompose RNA-seq data.

165 imensional data with small sample sizes like RNA-seq data.

166 the levels of gene expression inferred from RNA-seq data; (v) validation studies using qRT-PCR were

167 RNA-sequencing (RNA-seq) data enable the quantification of complex trans

168 the p53 pathway, we analyzed RNA sequencing (RNA-seq) data from colorectal cancer cell lines (HCT116,

169 Previous analysis of RNA sequencing (RNA-seq) data from human naive pluripotent stem cells re

170 especially with single-cell RNA sequencing (RNA-seq) data.

171 ans and mice and for a microdissected tubule RNA-seq dataset from rat.

172 Additionally, we scrutinized a large human RNA-Seq dataset of aortic tissue to assess the co-expres

173 We compared our rice RNA-seq dataset with a nodule transcriptome dataset in M

174 e structures (NLS) in rice and compared rice RNA-seq dataset with a nodule transcriptome dataset in M

175 On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent perf

176 ets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expre

177 Rlogreg) were compared for simulated and an RNA-seq dataset.

178 s were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdis

179 ) datasets and technical metadata along with RNA-seq datasets from other studies to understand factor

180 The analyses of bulk RNA-seq datasets of the melanoma samples identify and va

181 Zebrafish RNA-seq datasets show a preponderance of 3' alternative

182 putation tasks (within and across microarray/RNA-seq datasets) establishes that SampleLASSO is the mo

183 tive splicing in human and mouse single-cell RNA-seq datasets, and model them with a probabilistic si

184 control brain and stem cell gene expression RNA-seq datasets, to identify gene network regulatory mo

185 -specific genes in both bulk and single cell RNA-seq datasets, where it also improves resolution of c

186 per we develop methods to add signal to real RNA-seq datasets.

187 nes are complete and only integrate multiple RNA-seq datasets.

188 hese OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets.

189 paring various factor analysis techniques on RNA-seq datasets.

190 brain repositories using (1) RNA sequencing (RNA-seq) datasets and (2) DNA samples extracted from AD

191 lated expression solely from RNA-sequencing (RNA-seq) datasets.

192 RNA-seq demonstrated that even in a healthy CNS, astrocy

193 mission at the cone pedicle, we performed an RNA-seq differential expression analysis between cone-sp

194 provide a framework for power estimation of RNA-Seq differential expression under complex experiment

195 this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing dro

196 Droplet-based single-cell RNA-seq (dscRNA-seq) data are being generated at an unpr

197 RNA-sequencing (RNA-seq) enables global identification of RNA-editing si

198 ed by applying deconvolution methods to bulk RNA-Seq estimates.

199 ChIP-seq, ATAC-seq and RNA-seq experiments reveal that CASZ1 directly up-regula

200 ression files from both bulk and single-cell RNA-Seq experiments, supporting simultaneous queries on

201 ngle cells from TF/pathway perturbation bulk RNA-seq experiments.

202 RNA-Seq expression analysis currently relies primarily u

203 Comparison with RNA-seq expression data reveals a strong overlap between

204 Thus, clinical RNA-Seq extends molecular diagnostics of rare genodermat

205 ive reverse-transcriptase PCR (qRT-PCR), and RNA-Seq for PD-1 expression.

206 and humans at single-cell resolution: RAISIN RNA-seq for profiling intact nuclei with ribosome-bound

207 Single cell RNA-seq from 66 studies shows significant overlap betwee

208 ve therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic p

209 Through integrated RNA-seq, genome-wide ChIP-seq, and CUT&RUN association p

210 Single-cell RNA-seq has been established as a reliable and accessibl

211 While RNA-seq has enabled comprehensive quantification of alte

212 echnologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiment

213 However, RNA-seq has technical features that incumbent tests (e.g

214 Transcriptome analysis by RNA sequencing (RNA-seq) has become an indispensable research tool in mo

215 High-throughput sequencing of RNA (RNA-seq) has expedited the exploration of these regulato

216 Compared to conventional RNA-Seq, hsRNA-Seq increased reads mapping to the Bacter

217 RNA-seq identified marked differences among NM, TC-TAM,

218 RNA sequencing (RNA-seq) identified opsin candidates in both species, in

219 Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the

220 ory CD4(+) T cell activation with high-depth RNA-seq in healthy individuals.

221 RNA-Seq indicated distinct transcriptional responses: ni

222 RNA-seq indicated that among the Wnt ligands with high e

223 RNA sequencing (RNA-seq) is a powerful technology for studying human tra

224 need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-S

225 a against a customized protein database from RNA-Seq may produce a subset of alternatively spliced pr

226 This lets RNA-seq methods developers assess their procedures in no

227 By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two hum

228 with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outpe

229 Using RNA-Seq, no significant upregulation of any Pgp was dete

230 oblastoma, which was verified in single-cell RNA-seq of human glioblastoma samples.

231 Hence, we performed RNA-Seq of leaf infected with or without DB to understan

232 mmunoprecipitation and laser-microdissection RNA-seq of leaf primordial margins to identify gene targ

233 Single-cell RNA-seq of microglia after acute systemic administration

234 d single human muscle fibers and single cell RNA-seq of mononuclear cells from human vastus lateralis

235 scriptional programs of hypoxic ECs by using RNA-Seq of primary cultured human umbilical vein ECs exp

236 sion patterns of skeletal muscle cells using RNA-seq of subtype-pooled single human muscle fibers and

237 RNA-seq of the ventral hippocampus (vHpc) highlights tha

238 Performing single-cell RNA sequencing (RNA-seq) of 179,632 cells across 23 teratomas from 4 cel

239 Transcriptomic analysis (RNA-seq) of tomato demonstrated that biochar had a primi

240 In this study, we performed single-nucleus RNA-Seq on the adult mouse SV in conjunction with sample

241 ne expression analysis using RNA sequencing (RNA-seq) on prenatal (N = 33; 16 smoking-exposed) as wel

242 and able to run on signatures generated from RNA-Seq or ATAC-Seq data.

243 o use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision,

244 ocused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well

245 We detected RNA-seq profiles in 96 of 100 of samples (96%), with 412

246 Sixteen tape strips were collected for RNA-seq profiling from 19 infants/toddlers (<5 years old

247 n cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endoth

248 Using RNA-seq profiling of the intima of lesions, here we iden

249 RNA-Seq profiling shows differential expression of many

250 RNA-seq profiling was done on leaf samples collected at

251 3 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference s

252 nt approaches to single-cell RNA sequencing (RNA-seq) provide only limited information about the dyna

253 g or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA tar

254 cal assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candi

255 y of expressed non-coding RNAs and UTRs from RNA-seq reads mapped to a reference genome.

256 Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal

257 inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic change

258 RNA-Seq results suggested that the metastatic subclones

259 alyses of representative genes validated the RNA-Seq results.

260 Deep RNA-seq revealed a significant derepression of the miR-2

261 RNA-Seq revealed up-regulation of glycolytic enzymes and

262 d from mouse cell types, we deconvolute bulk RNA-seq samples from 28 GTEx tissues to quantify cellula

263 metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources re

264 ssential step in the analysis of single cell RNA-seq (scRNA-seq) data to shed light on tissue complex

265 thods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability.

266 Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellu

267 ion-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq

268 Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated

269 Recent advances in single-cell RNA-seq (scRNA-seq) revolutionized cell type-specific ge

270 The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to m

271 However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high tec

272 el molecules (UBXN4, MFSD12, and ACTR6) from RNA-seq served as potential prognostic markers for lung

273 kinds of transcriptomic data, including bulk RNA-seq, single-cell RNA-seq and spatial transcriptomic

274 e-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference express

275 issociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-

276 RNA-seq studies found that chronic iAs drives the transi

277 RNA-Seq suggested periostin as a potential key factor fo

278 RNA-seq tape strip profiling detected distinct immune an

279 Using RNA-Seq technology, we provide evidence that the loss of

280 ent (TE) RNA expression, such as RT-qPCR and RNA-seq, that do not distinguish between TEs expressed f

281 Using single-cell and bulk RNA sequencing (RNA-seq), the authors compared DMD and control hiPSC-der

282 As determined by RNA sequencing (RNA-Seq), this low amount of IgHC sufficed to initiate P

283 We tested the recommendation system using an RNA-seq time course dataset from differentiation of embr

284 We used Illumina RNA-Seq to analyse cDNA libraries for differential expre

285 ipicephalus microplus transcriptome, we used RNA-seq to carry out a study of expression in (i) embryo

286 by a transcriptomic analysis using unbiased RNA-Seq to identify concomitant changes in the liver tra

287 3 IAV- and IBV-activating proteases, we used RNA-Seq to investigate the protease repertoire of murine

288 e, we use time-lapse imaging and single cell RNA-seq to measure activation trajectories and rates in

289 Here we used multiplexed single-cell RNA-seq to profile 198 cancer cell lines from 22 cancer

290 Here we use single cell RNA-seq to show that murine IFE differentiation is best

291 In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a mari

292 global methodologies (ATAC-seq, ChIP-seq and RNA-seq) to assess the effect of GCN5 loss-of-function o

293 to decipher transcriptome changes under DI, RNA-seq was performed in C-76 and Val-C.

294 eated with LPS at different time points, and RNA-seq was performed on microglia and cerebral endothel

295 hich are challenging to be investigated with RNA-Seq, we accurately defined boundaries of lowly expre

296 Using RNA-seq, we identify B. thetaiotaomicron genes that were

297 Here, by means of RNA-Seq, we monitored the early steps of biofilm product

298 of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker gene

299 Cross referencing bulk RNA-seq with single-cell RNA-seq data revealed the CNTF

300 -transcriptome sequencing by RNA sequencing (RNA-Seq), with appropriate bioinformatics, provides a ro