ライフサイエンスコーパス: RNA-Seq

1 e compared using next-generation sequencing (RNA-Seq).

2 nt normal tissues using deep RNA-sequencing (RNA-seq).

3 d MisR-regulated genes using RNA sequencing (RNA-Seq).

4 red using next-generation sequencing of RNA (RNA-seq).

5 verse transcription (RT) such as RT-qPCR and RNA-Seq.

6 ysis of the count-based sequencing data from RNA-seq.

7 se during the preparatory steps required for RNA-seq.

8 q data, and 44 were confirmed by single cell RNA-seq.

9 els from ripe and overripe mango fruit using RNA-Seq.

10 platform for massively parallel single-cell RNA-seq.

11 wing methyl jasmonate (MeJA) treatment using RNA-seq.

12 ctor (SRF)-regulated TSPANs in VSMC by using RNA-seq analyses and RNA-arrays.

13 r matrix remodeling genes, while single-cell RNA-seq analyses showed increased expression of genes re

14 small RNAs regulate mRNA fate, we conducted RNA-Seq analyses to determine not only the levels of bot

15 Through RNA-Seq analyses, we identified 137 genes that are missi

16 ital ELISA, enhanced interferon signaling by RNA-Seq analysis and constitutive upregulation of phosph

17 ew modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining.

18 RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction.

19 Quantifying the RNA-seq analysis by smFISH reveals that only 10% of bulk

20 RNA-seq analysis demonstrates differential gene expressi

21 RNA-seq analysis detected aberrant splicing in DONSON du

22 Riborex directly leverages established RNA-seq analysis frameworks for all parameter estimation

23 Single cell RNA-Seq analysis holds great promise for elucidating the

24 ion program in breast cancer, a pipeline for RNA-seq analysis in 780 breast cancer and 101 healthy br

25 retinide target genes, we performed unbiased RNA-seq analysis in liver from mice fed high-fat diet +/

26 ompasses a subset of transcripts detected by RNA-Seq analysis of in vitro-derived MK cells and that t

27 RNA-seq analysis of peripheral blood samples collected j

28 Single-cell RNA-seq analysis of pre- and postnatal endolymphatic sac

29 Second, RNA-Seq analysis of squid exposed to modeled microgravit

30 RNA-seq analysis of uhrf1 and dnmt1 mutants revealed wid

31 RNA-seq analysis of vehicle and STAT3 inhibitor treated

32 RNA-seq analysis of whole skin identified a larger numbe

33 KEY MESSAGE: RNA-seq analysis on whitebark pine needles demonstrated

34 the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with

35 RNA-seq analysis revealed 268 and 829 genes were differe

36 RNA-seq analysis revealed an inverse relationship betwee

37 RNA-Seq analysis revealed that extracellular matrix (ECM

38 In addition, RNA-seq analysis revealed that genes involved in hormone

39 Coupling the genome-wide occupancy data with RNA-seq analysis revealed that UzcR is a global regulato

40 RNA-seq analysis reveals a divergent role of PI3K/AKT/mT

41 RNA-seq analysis reveals that PHF5A-Y36C has minimal eff

42 RNA-seq analysis showed that the expression of over thou

43 RNA-seq analysis showed that the ref4-3 mutation causes

44 We performed RNA-seq analysis using Achilles tendons to investigate t

45 Additional RNA-seq analysis was carried out in an independent cohor

46 Using RNA-seq analysis, we showed that the expression of a gen

47 mRNA ratios and higher rRNA carryover during RNA-seq analysis.

48 ource for future development of software for RNA-Seq analysis.

49 outperform other available transcriptomes in RNA-seq analysis.

50 High-throughput RNA sequencing (RNA-seq) analysis of HIF-2alpha-overexpressing mice in c

51 d this, we investigated transcriptomes using RNA-seq and amino acid levels with N treatment in tea (C

52 Finally, in RNA-seq and ChIP-seq experiments, EBF3 acted as a transc

53 c treatment on gene expression, we performed RNA-seq and ChIP-seq for H3K27ac on HepG2 cells, a human

54 Using RNA-seq and ChIP-seq we show that BMP/Smad1 regulates do

55 irst involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and

56 RNA-Seq and DNA methylation analyses showed that Natur-I

57 nd epigenomic datasets from these mice using RNA-Seq and DNase-Seq.

58 ll intestine and colon organoids, along with RNA-Seq and gene ontology methods, to characterize the e

59 es from closely related species, followed by RNA-seq and in silico species separation.

60 expression impacts other metabolic pathways, RNA-Seq and metabolite profiling were performed on stalk

61 Finally, combining RNA-seq and miRNA-seq data, we define miRNA cocktails th

62 Using single-cell RNA-Seq and multiplexed in situ hybridization, we show h

63 e gene body, as evidenced by tissue-specific RNA-seq and other DNA-encoded splice signals.

64 annotation combines strand-specific Illumina RNA-seq and Pacific Biosciences (PacBio) full-length cDN

65 iptome-wide PE analyses to date (microarray, RNA-Seq and PAS-Seq) are NRIP1 (RIP140), a transcription

66 ression in SLE by maximising the leverage of RNA-Seq and performing integrative GWAS-eQTL analysis ag

67 RNA-seq and proteomics data together with yeast two-hybr

68 R and found a strong correlation between the RNA-Seq and qPCR results.

69 RNA-seq and qRT-PCR expression analysis showed that over

70 ly expressed in MDV-infected CD4+ T cells by RNA-Seq and qRT-PCR.

71 ar models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately,

72 UDS on a set of published microbiome 16S and RNA-seq and roll call data.

73 RNA-seq and RT-qPCR identified potential downstream gene

74 tergenic RNAs in the mature mouse brain with RNA-Seq and validation with independent methods.

75 ofiled the transcriptomes (using GRO-seq and RNA-seq) and epigenomes (using ChIP-seq) of 11 different

76 e-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied

77 RNA sequencing (RNA-seq) and real-time polymerase chain reaction (PCR) w

78 Using RNA sequencing (RNA-Seq) and ribosome profiling of primary human platele

79 Using ChIP-seq, RNA-seq, and GRO-seq, here we demonstrate a global overl

80 zygous mice compared to wild-type mice using RNA-seq; and (iii) morphological and functional conseque

81 The wide range of RNA-seq applications and their high-computational needs

82 to develop a highly scalable single-nucleus RNA-seq approach (sNucDrop-seq), which is free of enzyma

83 d-factor protein capture and RNA-sequencing (RNA-seq) approach, we have assessed how mRNA association

84 The use of RNA-seq as the preferred method for the discovery and va

85 vity and high technical noise of single-cell RNA-seq assays.

86 b/ ), which stores and facilitates search of RNA-Seq based expression profiles available from the mod

87 An RNA-Seq based transcriptome was developed from a pool of

88 Here, we used RNA-Seq-based analysis of patient leukemic cells and fou

89 ome-wide association studies (GWAS) in which RNA-seq-based measures of transcript accumulation are us

90 nced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current an

91 Single-cell RNA-seq can precisely resolve cellular states, but apply

92 RNA-sequencing (RNA-seq) can deliver both transcriptional and AS informa

93 g exome sequencing, whole-genome sequencing, RNA-seq, ChIP-seq, targeted sequencing and single-cell w

94 ary genomic analyses such as RNA sequencing (RNA-Seq), chromatin immunoprecipitation, and ribosome pr

95 ity between iCLIP replicates and single-cell RNA-seq clustering are both improved using our proposed

96 nd eQTL data from the TwinsUK microarray and RNA-Seq cohort in lymphoblastoid cell lines.

97 We conducted a global RNA-seq comparison between a resistant genotype (S54) an

98 The data generated by sci-RNA-seq constitute a powerful resource for nematode biol

99 nt idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics

100 Both NanoString and RNA-Seq could be used to predict relative abundances of

101 Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular

102 Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet p

103 ing the rate of differential mRNA decay from RNA-seq data and model mRNA stability in the brain, sugg

104 l splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and q

105 e methods to analyse the added complexity in RNA-seq data are needed.

106 Separation of RNA-seq data by sex revealed underlying negative binomia

107 t combining statistical modeling with public RNA-seq data can be powerful for improving our understan

108 e wild strawberry Fragaria vesca genome with RNA-seq data derived from different stages of fruit deve

109 ociated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes P

110 From single-cell RNA-Seq data for a case of melanoma and the specificity

111 Analysis of ribosome profiling and RNA-seq data for endogenous miRNA targets revealed trans

112 Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified an

113 ws the strength of combining QTL mapping and RNA-Seq data from a mouse model with association studies

114 lncRNAs based on analysis of strand-specific RNA-seq data from cassava shoot apices and young leaves

115 RNA-Seq data from corneal epithelium were compared to ep

116 We combined RNA-seq data from gingival tissues with quantitative tra

117 quantitative trait locus analysis, utilizing RNA-seq data from human skin and found that LCE3B/C-del

118 generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, ofte

119 Using RNA-seq data from molecular degradation experiments of h

120 gene expression based on the strand-specific RNA-seq data from seedling, floral bud, and root of 19 A

121 Integration of RNA-seq data from TCGA and LC-MS/MS proteomics from WS r

122 In silico analysis of RNA-seq data from The Cancer Genome Atlas further demons

123 cultures and a computational analysis of SCI RNA-seq data further supported the possibility that a re

124 Here, we present stranded RNA-seq data generated directly from a small volume of b

125 t and the quantification of transcripts from RNA-Seq data in order to develop novel methods for rapid

126 ow meta-analysis and integration of existing RNA-seq data into transcriptional atlas projects.

127 xisting normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream a

128 ped to automatically identify TUs with given RNA-seq data of any bacterium using a machine-learning a

129 ata of multiple cancer types and single-cell RNA-seq data of lung adenocarcinoma, we confirmed an ant

130 We applied rMATS-DVR to RNA-seq data of the human chronic myeloid leukemia cell

131 Lastly, single-nucleotide resolution of RNA-Seq data revealed 15 bicistronic and tricistronic me

132 Examination of the TCGA RNA-seq data set also revealed widespread activation of

133 downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data f

134 ogenitor and committed states in single-cell RNA-seq data sets in a non-biased manner.

135 We find that analysis of large RNA-Seq data sets requires both careful quality control

136 ing data, and its implementation for several RNA-Seq data sets, as well as the whole genome sequencin

137 By analyzing over 23,000 publicly available RNA-Seq data sets, we show that Tradict is robust to noi

138 ed conditions and real iCLIP and single-cell RNA-seq data sets.

139 ruct a consensus transcriptome from multiple RNA-seq data sets.

140 Examples using RNA-Seq data show that the Bon-EV procedure has higher s

141 line (fibroblast) and tumor (leiomyosarcoma) RNA-seq data to compare Oncopig and human STS expression

142 We generated 27 Gb DNase-seq and 67.6 Gb RNA-seq data to investigate chromatin accessibility chan

143 cript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and

144 address challenges of analyzing large-scale RNA-seq data via several new developments to provide a c

145 Stranded paired read RNA-seq data was used for evaluation purposes.

146 RNA-seq data were used to study host gene expression, B-

147 ng bio-informatic comparisons of Tag-seq and RNA-seq data, and 44 were confirmed by single cell RNA-s

148 Here we analyzed recent RNA-seq data, and found that, except for few mammalian c

149 ruses, and papillomaviruses were detected in RNA-seq data, but proportions were similar (P = .73) acr

150 in vitro experiments with bioinformatic and RNA-seq data, metabolic responses to nitrate or NO and h

151 ng known lncRNAs is not trivial from massive RNA-seq data.

152 provide a framework for power estimation of RNA-Seq data.

153 most of the genes are validated by cDNA and RNA-seq data.

154 cohort of the tumor and normal samples with RNA-Seq data.

155 model has not been adequately addressed for RNA-seq data.

156 biology of gene expression in microarray and RNA-Seq data.

157 and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery.

158 bp and with this information an online Mango RNA-Seq Database which is a valuable genomic resource fo

159 We report here the first full RNA-Seq dataset representing host-associated V. fischeri

160 half of the reads were missed by HMMER for a RNA-Seq dataset.

161 Using these approaches, previous RNA-seq datasets could potentially be processed and inte

162 Finally, analysis of RNA-seq datasets from individuals in the 1000 Genomes pr

163 We found that multiple small RNA-seq datasets from the worm Caenorhabditis elegans ha

164 raining samples, we propose to use simulated RNA-seq datasets to train our model.

165 real ribosomal RNA-depleted (rRNA-depleted) RNA-seq datasets.

166 allele-specific expression (ASE) in complex RNA-seq datasets.

167 It is very challenging to estimate power for RNA-Seq differential expression under complex experiment

168 We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data

169 Single-cell RNA-seq enables the quantitative characterization of cel

170 suitable for single-cell RT-qPCR as well as RNA-Seq, enabling the reliable detection of cancer-speci

171 ay using a combination of mass spectrometry, RNA-seq, enzyme assays, RNAi and phylogenomics in differ

172 All of these genes have RNA-seq evidence and 87% were confirmed by proteomics.

173 treated with or without UVR were analyzed by RNA-seq, exome-seq, and H3K27ac ChIP-seq at 4 h and 72 h

174 This difference allowed us to carry out RNA-seq experiments and identify a limited number of gen

175 Data mining of RNA-Seq experiments with mouse models of intestinal HIF-

176 splicing efficiency, as demonstrated through RNA-seq experiments.

177 RNA sequencing (RNA-seq) experiments now span hundreds to thousands of s

178 A RNA-seq expression analysis in different Arachis species

179 ded support for new data types such as CRAM, RNA-seq expression data and long-range chromatin interac

180 uce the Census algorithm to convert relative RNA-seq expression levels into relative transcript count

181 nd their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched no

182 ting matched whole-transcriptome sequencing (RNA-seq) from the BrainSpan project revealed varied patt

183 onal activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 can

184 e approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histon

185 genome-wide analyses utilizing ChIP-Seq and RNA-Seq, GOF p53-induced origin firing, micronuclei form

186 RNA-seq has been mainly used for testing differential ex

187 RNA-seq has emerged as a powerful technology for the det

188 RNA-Seq has supplanted microarrays as the preferred meth

189 RNA-Seq has the potential to discover a more comprehensi

190 Generation Sequencing (NGS) strategies, like RNA-Seq, have revealed the transcription of a wide varie

191 ise and lower coverage than traditional bulk RNA-seq, hence bringing in new computational difficultie

192 cP-RNA-seq identified the two tRNAs as major substrates for

193 nted transcript expression using Tag-seq and RNA-seq in female rat Ventral Mesenchymal Pad (VMP) as w

194 hroid terminal differentiation, we conducted RNA-seq in human reticulocytes and identified nuclear re

195 plexed in situ hybridization and single-cell RNA-Seq in male and female mice to provide a more compre

196 ssibility by ATAC-seq and gene expression by RNA-seq in pancreatic cancer and control samples.

197 large amount of information obtained through RNA-Seq in S. mansoni (88 libraries).

198 Researchers who work with ovary RNA-seq in these taxa should realize that insufficient d

199 y (MS) based proteomics and mRNA sequencing (RNA-Seq) in comparison to non-infectious procyclic trypa

200 zed bulk and single-cell RNA transcriptomes (RNA-seq) in SSEA4(+) hSSCs and differentiating c-KIT(+)

201 RNA-Seq is a powerful tool in transcriptomic profiling o

202 RNA-seq is a useful tool for detecting and characterizin

203 RNA-seq is a useful tool for detecting novel transcripts

204 Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of

205 RNA sequencing (RNA-seq) is a powerful approach for measuring gene expre

206 To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes

207 Using RNA sequencing (RNA-seq), it has now become possible to sequence and qua

208 he epigenomic (ATAC-seq) and transcriptomic (RNA-seq) landscapes of alphaTC1 and betaTC6 cells.

209 ation pipeline, we assembled tissue-specific RNA-Seq libraries from 113 datasets and constructed 48 3

210 Our RNA-Seq libraries met high quality control standards and

211 sulating and barcoding cells (1 d); and (iv) RNA-seq library preparation (2 d).

212 mated from Salmon and Kallisto are the ideal RNA-seq measurements for deconvolution studies.

213 tes, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcr

214 ased on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of sign

215 mmonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq dat

216 q, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hyp

217 cells from healthy skin samples, followed by RNA-seq of each cell population.

218 ential expression analysis following nuclear RNA-seq of neutrophil active transcriptomes reveals a si

219 cellular adaptation to hypoxia, we performed RNA-Seq of normoxic and hypoxic head and neck cancer cel

220 We use RNA-Seq of synchronized populations of in vitro-derived

221 KEY MESSAGE: RNA-seq of Vitis during early stages of bud development,

222 Using unbiased single-cell RNA sequencing (RNA-seq) of 2400 cells, we identified six human DCs and

223 To test this method, we carried out RNA-seq on 20,424 single cells from postnatal day 1 mous

224 Here the authors performed RNA-seq on actively translated mRNAs in hippocampal CA3

225 We performed RNA-seq on purified peripheral afferent neurons, but fou

226 We performed RNA-Seq on T1 and T2 bladder cancer tissues and used pub

227 city, we performed transcriptome sequencing (RNA-seq) on two GBS strains grown under stringent respon

228 ith other types of experimental data such as RNA-seq or ChIP-seq.

229 gene fingerprint (as k-mers) profiles of the RNA-Seq paired-end reads.

230 ngle-molecule, real-time (SMRT) and Illumina RNA-seq platform.

231 ssion data generated by either microarray or RNA-seq platforms.

232 s included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metr

233 We have developed RNA-Seq procedures, as well as a 1200 bp 5 RACE product

234 ) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples.

235 Here, comparing single-cell RNA-Seq profiles of CTCs from breast, prostate and lung

236 combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-

237 Population and single-cell RNA sequencing (RNA-seq) profiling combined with bulk assay for transpos

238 hroughput transcriptomic techniques, such as RNA-seq, provides an opportunity for the identification

239 RNA-seq/PVG PCR detected previously missed, putative pat

240 ify boundaries of expressed transcripts from RNA-seq reads alignment.

241 od for quantifying transcript abundance from RNA-seq reads.

242 The RNA-seq results and cohort studies indicated a relativel

243 RNA-seq reveals almost all subunits in the two morphofun

244 RNA-seq reveals that MMPs express a number of marker gen

245 Single-cell RNA sequencing (RNA-seq) reveals enrichment of homeostatic modules in mo

246 We provide an RNA-seq roadmap for the stress-sensitive vDG.

247 The pipeline can process a typical RNA-seq sample in a matter of minutes and complete hundr

248 DTU events for 8148 genes across 206 public RNA-Seq samples, and find that protein sequences are aff

249 On 10 human RNA-seq samples, Scallop produces 34.5% and 36.3% more c

250 By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression

251 ific mouse reporter strains, we performed an RNA-seq screen, identifying tip- and stalk-enriched gene

252 a novel algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular d

253 Methods and We performed RNA (RNA-seq) sequencing and analyzed the transcriptomes of 6

254 uced representation bisulfite sequencing and RNA-seq show that dCas9-SunTag-DNMT3A methylates regions

255 Differential gene expression analysis using RNA-Seq showed consistent expression of six hydrogenase

256 e accommodates both un-stranded and stranded RNA-seq so that lncRNAs overlapping with other genes can

257 d genome-wide ChIP-sequencing (ChIP-seq) and RNA-seq studies extended these findings to the in vivo s

258 nscriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples,

259 Yet, despite an increasing number of RNA-seq studies, comparative evaluation of bacterial rRN

260 een ectomycorrhizal partners, we performed a RNA-Seq study of transcriptome reprogramming of the basi

261 We present here a comprehensive RNA-seq study that covers multiple tissues in an SMA mou

262 ically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in 12

263 Illumina high throughput RNA-Seq technology was employed and generated more than

264 To gain insights into these mechanisms, RNA-Seq technology was utilized to sequence RNA derived

265 ial infection and nodule initiation by using RNA-seq technology.

266 tory network, we performed a high-resolution RNA-seq time series of methyl JA-treated Arabidopsis tha

267 ifferent routes with the intent of comparing RNA-Seq to a NanoString nCounter codeset targeting 769 n

268 ile cis-regulatory elements (CREs) and using RNA-seq to characterize gene expression in the same indi

269 We performed genome-wide RNA profiling using RNA-Seq to compare the RR group and the complete remissi

270 ter) to F. pseudograminearum infection using RNA-seq to determine whether Brachypodium can be exploit

271 We employed ChIP-seq and 4sU-RNA-seq to identify aberrant DNA-binding events genome w

272 ompared to epidermal hair follicle stem cell RNA-Seq to identify genes representing common putative s

273 Here, we used RNA-seq to identify transcriptome changes from late embr

274 This study used RNA-seq to investigate the transcriptome of primary mono

275 NA degradation in vivo was examined by using RNA-seq to search the H. pylori transcriptome for RNAs w

276 oal of this study was to use RNA-sequencing (RNA-seq) to analyze the host transcriptome in response t

277 e timing of zygote development and generated RNA-seq transcriptome profiles of gametes, zygotes, and

278 Here, we used shotgun proteomics, OxICAT and RNA-seq transcriptomics to analyse protein S-mycothiolat

279 h CsrA in vivo, while ribosome profiling and RNA-seq uncover the impact of CsrA on translation, RNA a

280 Further RNA-Seq using specifically defined tissues of the base o

281 RNA-seq virus detection achieved 86% sensitivity when co

282 in late events during the viral life cycle, RNA-Seq was carried out on triplicate differentiated pop

283 In this study, RNA-seq was employed to comprehensively understand the r

284 Using RNA sequencing (RNA-Seq), we compared the expression patterns of circula

285 Here, using de novo dual-RNA seq, we compared the host salamander cells that harb

286 Using small RNA-seq, we also examined miRNA expression (nine samples

287 Using RNA-seq, we examined whole-exome gene and exon expressio

288 Here, using protrusion-isolation schemes and RNA-Seq, we find that RNAs localized in protrusions of m

289 By RNA-seq, we found a JAZ gene, NaJAZi, specifically expre

290 Using a combination of DamID-seq and RNA-seq, we identified a set of Yki targets that play a

291 including in situ Hi-C, DamID, ChIP-seq, and RNA-seq, we investigated roles of the Heterogeneous Nucl

292 l ribosome affinity purification followed by RNA-Seq, we profiled astroglial ribosome-associated (pre

293 Using 4sU RNA labelling and RNA-seq, we show this competition results in reciprocal

294 Here, using single-cell RNA-seq, we unearth unexpected heterogeneity among SCs a

295 Transcriptome analyses by RNA-seq were conducted as a functional readout of the ep

296 Expression changes identified by RNA-Seq were validated by qRT-PCR open arrays.

297 predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to captu

298 have become the approach of choice prior to RNA-seq, with their efficiency varying in a manner depen

299 nificant heterogeneity in the performance of RNA-Seq workflows to identify differentially expressed g

300 xtensive study analysing a broad spectrum of RNA-seq workflows.