ライフサイエンスコーパス: sequence data

1 e to the assembly using additional available sequence data.

2 g closely-related bacteria from whole-genome sequence data.

3 ain the best performance over intergenic DNA sequence data.

4 P yield possible from low-coverage polyploid sequence data.

5 ting risk factors of disease from non-coding sequence data.

6 h as viral genome integration, in paired-end sequence data.

7 s various genomic annotations in addition to sequence data.

8 led to an explosive accumulation of genomic sequence data.

9 ntained even in the context of international sequence data.

10 ulation-based surveillance with accompanying sequence data.

11 ion algorithm to infer indel parameters from sequence data.

12 realistic, individual-level genome-wide SNP/sequence data.

13 from 7 bp to 1 kbp compared with short-read sequence data.

14 roach to reconstruct transmission trees with sequence data.

15 cept of rational vaccine design from genomic sequence data.

16 r of self-sustained epidemics from any viral sequence data.

17 ssifier, UTAX, and SINTAX) handle ITS fungal sequence data.

18 portal for organizing and sharing the viral sequence data.

19 have made it possible to obtain gigabases of sequence data.

20 identify structural variants in linked-read sequencing data.

21 ding characters than can pollute large-scale sequencing data.

22 recover immune receptor alleles from genome sequencing data.

23 ng all studies which include next generation sequencing data.

24 ation analysis with count-based small-sample sequencing data.

25 re variants in heterogeneous next-generation sequencing data.

26 framework for management of next-generation sequencing data.

27 ons at base pair level using next-generation sequencing data.

28 cilitating interpretation of next-generation sequencing data.

29 ariants and is applicable to a wide range of sequencing data.

30 termine HLA allele-specific copy number from sequencing data.

31 lization and interpretability of single-cell sequencing data.

32 sions in standard single- and paired-end RNA-sequencing data.

33 wnstream analysis functions for whole genome sequencing data.

34 esulting in an enormous amount of microbiome sequencing data.

35 difficulties in detecting them in short-read sequencing data.

36 ntegrating whole-genome CNVs and whole-exome sequencing data.

37 e infinite sites assumption with single-cell sequencing data.

38 must be computationally inferred from these sequencing data.

39 ds of thousands of samples with whole-genome sequencing data.

40 ematic to genotype STRs from high-throughput sequencing data.

41 ns impacted by batch effects in whole genome sequencing data.

42 rchromosomal SVs from mate-pair and pair-end sequencing data.

43 structural variants from Illumina short-read sequencing data.

44 ed pre-processing tool for immune repertoire sequencing data.

45 g carriage might limit the interpretation of sequencing data.

46 ction and read coverage from next-generation sequencing data.

47 cific tumor neoantigens from next generation sequencing data.

48 ration such as time-series analysis on cfDNA sequencing data.

49 so determined using paired RNA and small RNA sequencing data.

50 (RGEPs) from tumour-derived single-cell RNA sequencing data.

51 ese variations accurately in next generation sequencing data.

52 e of EP in protein-DNA binding using massive sequencing data.

53 tility of pooled analysis of mouse and human sequencing data.

54 ctural variation breakpoints in whole-genome sequencing data.

55 individuals with both imaging and brain RNA sequencing data.

56 , in particular, for time series metagenomic sequencing data.

57 analyses of variants identified in pathogen sequencing data.

58 y and mitigate batch effects in whole genome sequencing data.

59 ers to collect a large volume of metagenomic sequencing data.

60 Finally, using sequencing data, a computational model was generated to

61 grating parallel (phospho)proteomic and mRNA sequencing data across 12 TCGA tumour data sets to inter

62 MinION long-read sequence data also facilitated the elucidation of comple

63 le enough for routine use in single-cell RNA sequencing data analyses.

64 tochrome c oxidase subunit I) and subsequent sequence data analysis provided experimental evidence of

65 erence database giving global context to DNA sequence data and a framework for incorporating data fro

66 lification introduces redundant reads in the sequence data and estimating the PCR duplication rate is

67 The recent surge in genome sequence data and functional genomics research has usher

68 lve this, we generated the first genome-wide sequence data and mitochondrial genomes from eleven arch

69 However, the massive sequence data and the diverse properties of different ge

70 The detection of RNA 3D modules from sequence data and their automatic implementation belong

71 o automate the screening of large amounts of sequence data and to focus on the most promising strains

72 RNA sequencing data and a uidA reporter assay indicated that

73 t to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other

74 incorporation of -omics and next-generation sequencing data and continual improvement in measures of

75 le biomarker candidates from high throughput sequencing data and could be generalized to other datase

76 physiologically relevant splice forms using sequencing data and demonstrated that the resulting isof

77 on computational analysis of single-cell RNA-sequencing data and discuss underlying assumptions, meth

78 ng need for new data structures to store raw sequencing data and efficient algorithms for population

79 f fusion genes encompassing analysis of deep sequencing data and manual curations.

80 , through January 31, 2017, used DNA and RNA sequencing data and messenger RNA expression results fro

81 to be directly established, the increase in sequencing data and readily available computational powe

82 Technical advances in single-cell sequencing data and their application to greater samples

83 rds: "whole genome", "transcriptome or exome sequencing data", and "genome-wide genotyping array data

84 perating on bacterial IGRs from whole-genome sequence data, and suggests that our current understandi

85 available genotyping tools and whole-genome sequencing data, and argue for a better integration of p

86 updated through the addition of new strains, sequencing data, and association mapping results.

87 fetal and adult human liver single-cell RNA sequencing data, and find a striking correspondence betw

88 ws online browsing of mapped high throughput sequencing data, and its implementation for several RNA-

89 A polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq

90 st, enabling the application to whole-genome sequencing data, and straightforward to implement.

91 ly genotyping and phasing STRs from Illumina sequencing data, and we report a genome-wide analysis an

92 DACE clustered the Lake Taihu 16S rRNA gene sequencing data ( approximately 316M reads, 30 GB) in 25

93 We present deep whole-genome sequencing data ( approximately 38x) from 28 individuals

94 and the Ocean TARA Eukaryotic 18S rRNA gene sequencing data ( approximately 500M reads, 88 GB) into

95 s from 17 populations for which Y-chromosome sequence data are also available.

96 tion of novel alleles discovered from genome sequence data are likely to be particularly significant

97 ilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding

98 As genomic interaction sequencing data are becoming prevalent, a standard file

99 scribe the biological samples from which the sequencing data are derived.

100 High-throughput sequencing data are widely collected and analyzed in the

101 MusiteDeep takes raw sequence data as input and uses convolutional neural net

102 nformation and individual-level genotype and sequence data associated with phenotypic features mainta

103 Among 58 469 participants with CETP gene-sequencing data available, average age was 51.5 years an

104 Here using a large whole-genome sequencing data bank, cancer registry and colorectal tum

105 so correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tum

106 ion of differential mRNA decay rate from RNA-sequencing data by modeling the kinetics of mRNA metabol

107 major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based leve

108 as single-end sequencing data or paired-end sequencing data can accommodate to detect SV.

109 ider how emerging data types, such as genome-sequence data, can serve as proxies for microbial commun

110 From sequence data collected aboard the ISS, we constructed d

111 Sequence data compared well to resistance phenotypic dat

112 fy microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these meth

113 conducted using Sanger population nucleotide sequencing data derived from blood samples from study pa

114 public collection of mutant seed stocks and sequence data enables rapid identification of mutations

115 We analyzed clinical, microbiological, and sequencing data for 451 patients and their clinical isol

116 ail manifestations, we scrutinized the exome sequencing data for additional potentially deleterious g

117 By integrating high-throughput sequencing data for binding and splicing quantification

118 Based on use of next-generation sequencing data for characterizing epigenetic marks and

119 We mined our RNA-sequencing data for differentially up-regulated genes th

120 Pre-processing of high-throughput sequencing data for immune repertoire profiling is essen

121 lysis web server, to analyze next-generation sequencing data for retroviral vector integration sites.

122 been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types o

123 hips among stinging-wasp families, gathering sequence data from >800 UCE loci and 187 samples, includ

124 Analyzing cumulative sequence data from 1,351 blood samples collected from 18

125 Here, we queried whole-genome sequence data from 1,916 patients across 24 cancer types

126 eterious genetic variation using whole-exome sequence data from 262 case subjects with pulmonary fibr

127 Using ultra-low-coverage (0.3x) population sequence data from 488 recombinant inbred Arabidopsis th

128 We demonstrate its use for exome sequence data from 60 706 individuals in the Exome Aggre

129 Here we analyze exome sequence data from 60,706 individuals to make genome-wid

130 Toward this end, the availability of DNA sequence data from 60,706 people through the Exome Aggre

131 systematic review of 11 publications, using sequence data from 863 familial CRC cases and 1604 indiv

132 ch we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 p

133 We analyzed whole-genome sequence data from families affected by Alzheimer diseas

134 00 discrete morphological characters and DNA sequence data from five gene regions.

135 s from new low-coverage whole-genome shotgun sequence data from five hunter-gatherers and five first

136 By analyzing genome sequence data from human populations, including 1269 ind

137 We apply SMC++ to analyze sequence data from over a thousand human genomes in Afri

138 lele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired

139 CryptSplice interrogation of sequence data from six individuals with X-linked dyskera

140 Analysis of simulated data and exome sequence data from the 1000 Genomes project demonstrated

141 icly available version includes pre-analyzed sequence data from the European Molecular Biology Labora

142 ses and 374,939 controls, using whole-genome sequence data from the Icelandic population, and tested

143 Furthermore, analysis of transcriptome sequence data from the starfish Asterias rubens revealed

144 Using RNA-sequencing data from 100 hippocampi from mice with epile

145 orage disorder genes, leveraging whole exome sequencing data from 1156 Parkinson's disease cases and

146 We then mined published RNA sequencing data from 117 hPS cell lines, and observed an

147 Application of the model to sequencing data from 17 cancer types demonstrates an inc

148 We used whole-genome sequencing data from 2,619 individuals through the UK10K

149 We reanalyse sequencing data from 461 samples into a coordinated cata

150 Comparing whole-exome germline sequencing data from 488 TCGA lung cancer samples to ger

151 We analysed whole-exome sequencing data from 5777 solid tumours, spanning 19 can

152 We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare

153 In exome sequencing data from a sister population, the Nunavik In

154 RNA sequencing data from both human fetal ear and mouse seco

155 nonuclear cells as well as 16S ribosomal RNA sequencing data from bronchoalveolar lavage obtained as

156 on we segment a time-series of transcriptome sequencing data from budding yeast, in high temporal res

157 quences are observed in ALS, we analysed RNA sequencing data from C9orf72-positive and sporadic ALS c

158 We integrated sequencing data from chromatin immunoprecipitation, RNA

159 Here, we capitalized on small RNA sequencing data from distinct species such as Arabidopsi

160 ns in the GEF1 domain of Trio in whole-exome sequencing data from individuals with ASD, and confirm t

161 Here, we combine sequencing data from mouse models of IR-induced malignan

162 entify methylation loci from high-throughput sequencing data from multiple experimental conditions.

163 High quality sequencing data from radiotherapy-induced cancers is par

164 ntial expression analysis of the count-based sequencing data from RNA-seq.

165 embedding and clustering of single-cell RNA sequencing data from six biopsy samples showed two major

166 High-throughput sequencing data from TCRs and Igs can provide valuable i

167 By taking advantage of genome sequencing data from the 1001 Genomes Consortium, we cha

168 By analyzing RNA-sequencing data from The Cancer Genome Atlas (TCGA) for

169 between gene age and expression level in RNA sequencing data from The Cancer Genome Atlas for seven s

170 alyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTE

171 iated with coronary heart disease using gene sequencing data from the Myocardial Infarction Genetics

172 ne Mutation Scoring Tool fOr Next-generation sEquencing data (GeMSTONE), a cloud-based variant priori

173 es have been identified from high throughput sequencing data generated from cancer genomes by using n

174 of tools to identify causative variants from sequencing data greatly limits the promise of precision

175 However, analysis of genome/transcriptome sequence data has revealed that PP/OK-type neuropeptides

176 High-quality phylogenetic placement of sequence data has the potential to greatly accelerate st

177 Integrative analysis of whole-genome/exome-sequencing data has been challenging, especially for the

178 Genome-wide sequencing data has enabled modern phylogenomic methods

179 Accurate typing of HLA genes with short-read sequencing data has historically been difficult due to t

180 The use of high-throughput sequencing data has improved the results of genomic anal

181 Extensive DNA sequence data have made it possible to reconstruct human

182 Tremendous amount of RNA sequencing data have been produced by large consortium p

183 tes, and merging of these sites with the RNA sequencing data identified a set of canola genes targete

184 In this paper, we present the evolution of sequence data in a Bayesian framework and the approximat

185 drogram production, genotype imputation from sequence data in linkage studies, and additional tools.

186 of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hy

187 Nevertheless, the paucity of 3'-sequencing data in this species precludes comprehensive

188 -scale mapping of the branchpoints from deep sequencing data in three different species and in the SF

189 , however, requires accurate partitioning of sequence data into B-cell clones and identification of t

190 What does it take to convert a heap of sequencing data into a publishable result?

191 However, converting short-read sequencing data into reliable genotype data remains a no

192 The sequence data is available at www.ncbi.nlm.nih.gov/sra/?

193 relationship between transmission events and sequence data is obscured by uncertainty arising from fo

194 f microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics

195 tion in a more clinical context, where exome sequencing data is abundant and the discovery of retrodu

196 The quality of whole-genomic sequencing data is comparable across all samples regardl

197 age and transmission of such vast amounts of sequencing data is expensive.

198 ction of structural mosaicism using targeted sequencing data is lacking.

199 ing have been published, the noisy nature of sequencing data is still a limitation for accuracy and c

200 ith the availability of massive whole genome sequencing data, it becomes practical to mine STR profil

201 genetic methods for identifying selection in sequence data may allow us to evaluate the roles of muta

202 Since NGS sequencing data may be accompanied by genotype data for

203 Substructures can help make highly diverse sequence data more tractable.

204 With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data stru

205 in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of

206 s been studied for decades, large amounts of sequencing data now available allows us to examine the m

207 lso applied the algorithm to high throughput sequencing data obtained for viruses present in sewage s

208 This study compares whole-genome sequencing data obtained from chemo-naive and chemo-trea

209 amples system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and

210 We generated deep, whole-genome sequence data of 17 individuals in a three-generation pe

211 Samples of molecular sequence data of a locus obtained from random individual

212 Sequence data of three nuclear genes and three plastid D

213 or analyses and visualization of TCR and BCR sequencing data of 13 species.

214 Exome sequencing data of 24 MSI colorectal cancers revealed in

215 20 candidate genes were extracted from exome-sequencing data of 42 subjects with EE and no previous g

216 Exome sequencing data of families were filtered for rare varia

217 on single-cell resolution.In single-cell RNA sequencing data of heterogeneous cell populations, cell

218 simultaneous generation of high-dimensional sequencing data of multiple gene targets.

219 95 samples across 20 cancer types from miRNA sequencing data of The Cancer Genome Atlas and identifie

220 Analysis of next-generation sequencing data often results in a list of genomic regio

221 Using available genomic sequence data on coagulation factor VIII and predictive

222 rmatics solution to understand ASE using RNA sequencing data only.

223 types of sequencing data, such as single-end sequencing data or paired-end sequencing data can accomm

224 r one million reads and several gigabases of sequence data per run.

225 The analysis of human whole-genome sequencing data presents significant computational chall

226 them particularly useful for next-generation sequencing data processing and analysis.

227 The next-generation sequencing data provided a previously unreported (to our

228 Deep-sequencing data provided additional diagnostic informati

229 spect to the human reference, with long-read sequencing data providing a fivefold increase in sensiti

230 ers the assessment and demultiplexing of the sequencing data, read mapping, inference of RAD loci, ge

231 tive detection of CNVs from targeted capture sequencing data remains challenging.

232 Processing of high-throughput sequencing data requires basic bioinformatics skills and

233 However, whole-genome sequencing data revealed at least five independent inser

234 Our RNA-sequencing data set provides a valuable resource for ben

235 We therefore produced an extensive small RNA sequencing data set to analyze male and female miRNA exp

236 e the generation of a comprehensive nanopore sequencing data set with a median read length of 11,979

237 metagenomic (random) and amplicon (targeted) sequence data sets.

238 ating on IGRs in bacteria using whole-genome sequence data sets.

239 SNP influence gene expression using two RNA sequencing data sets (n = 210 and n = 159).

240 on with 101 reduced-representation bisulfite sequencing data sets and 637 methylation array data sets

241 after analysis of 61 whole-genome bisulfite sequencing data sets and validation with 101 reduced-rep

242 nown and putative SSP genes based on 144 RNA sequencing data sets covering various stages of macronut

243 n individual tumors for 11 of 12 single-cell sequencing data sets from a variety of human cancers.

244 rtant, we found signatures of damage in most sequencing data sets in widely used resources, including

245 d for pre-processing large immune repertoire sequencing data sets.

246 itation [ChIP] combined with high-throughput sequencing) data show that YAP impairs SMAD recruitment

247 We generated high-spatial-resolution RNA sequencing data spanning the secondary phloem, vascular

248 Different types of sequencing data, such as single-end sequencing data or p

249 Examination of available sequence data suggests that Arg-135 may have originated

250 a lgorithm for c lustering e xtremely large sequencing data, termed .

251 this correlation as structural semantics of sequence data that allows for a different interpretation

252 nce (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clo

253 The accelerating growth in the corpus of sequencing data that underpins such analysis is making t

254 A-Seq data sets, as well as the whole genome sequencing data that was used in the construction of Ass

255 17 (19%) of the 90 patients (with available sequence data) that were discharged home before the diag

256 based on plastome and nuclear ribosomal DNA sequence data, the temporal history of the family was re

257 of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of mul

258 hat has been successfully applied to protein sequence data to extract evolutionary signals that provi

259 The entire information flow, from raw sequence data to hypothesis testing, can be accomplished

260 et of newly generated and publicly available sequence data to infer the HCV4a and HCV4d evolutionary

261 sed to computationally process and interpret sequence data to inform medical or preventative action.

262 We investigated the potential of deep sequence data to provide greater resolution on transmiss

263 By comparing ancient sequence data to that of modern specimens, we determine

264 RNA editing studies should complement genome sequence data to understand the full impact of nucleic a

265 Mirabello and colleagues use high-throughput sequencing data to assess the diversity of HPV16 isolate

266 h whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific eff

267 irst report of the use of transcriptome-wide sequencing data to identify molecular markers of antihyp

268 n data as well as experimental microarray or sequencing data to illustrate the usefulness of our meth

269 Here, we use orthogonal RNA-sequencing data to quantify mtDNA expression (mtRNA), an

270 ion of chromosome (Hi-C) and single-cell RNA sequencing data together with discrete stochastic simula

271 for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model.

272 nted, as well as the generation of simulated sequencing data under either negative binomial or compou

273 Simulated real-time analyses of in-flight sequence data using an automated bioinformatic pipeline

274 We analyzed both mitochondrial and nuclear sequence data using neutrality test and Bayesian analysi

275 coverage exome and low-coverage whole-genome sequencing data, utilizing information from both exon-ex

276 Genomic sequence data was contextualised through comparison with

277 integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize

278 From small RNA sequence data, we identified a set of reads with well-de

279 ately predicting gene fusion candidates from sequencing data, we are still faced with the critical ch

280 inctive signals of duplication in short-read sequencing data, we identified 744 duplicated loci in H.

281 action analysis by paired-end tag (ChIA-PET) sequencing data, we used CRISPR-Cas9 gene editing to tar

282 CDC for confirmatory agar dilution testing; sequence data were sent to CDC for analysis.

283 Sequencing data were also examined from an additional 39

284 Whole genome sequencing data were analyzed for rare pLoF variants (fr

285 Sequencing data were available for 254 (56%) of the NeoA

286 Next-generation sequencing data were generated using the restriction-sit

287 Analyses of the Cancer Genome Atlas HCC RNA-sequencing data were performed by using Ingenuity Pathwa

288 ly on genomic context rather than the actual sequencing data which manifests in high recurrence of re

289 sing the availability of human mitochondrial sequencing data, which called for a cogent and significa

290 ndicate that haploid resolution of long-read sequencing data will significantly increase sensitivity

291 es, variety or accession, from all available sequence data, will immediately allow more robust analys

292 analysis and visualization of user-provided sequence data with associated metadata, predictions of n

293 m that combines a probabilistic model of DNA sequencing data with a enumeration algorithm based on th

294 l-length 16S gene sequences from metagenomic sequencing data with high accuracy.

295 many applications, clustering of very large sequencing data with high efficiency and accuracy is ess

296 etween two long sequences or Next-Generation Sequencing data with the Markov models of the background

297 novel cancer risk loci from next-generation sequencing data, with iterative data analysis from targe

298 arkers directly from high-throughput shotgun sequencing data without a reference genome, and an appro

299 ent in cfDNA from 0.1x coverage whole-genome sequencing data without prior knowledge of tumor mutatio

300 SynthEx robustly identifies CNAs using sequencing data without the additional costs associated