ライフサイエンスコーパス: sequence data

1 smid epidemiology based solely on short-read sequence data.

2 , and disseminate NCBI and custom biomedical sequence data.

3 analyses that discarded up to 92.3% of total sequence data.

4 l resistance (AMR) phenotypes from bacterial sequence data.

5 cting AMR phenotypes using incomplete genome sequence data.

6 using historical glycoprotein hemagglutinin sequence data.

7 of segments in that network from full-genome sequence data.

8 nts using epigenome, replication timing, and sequence data.

9 , to measure their prominence in less deeply sequenced data.

10 on from different tissues in single-cell RNA-sequencing data.

11 ecifically for or adapted to single-cell DNA sequencing data.

12 putational demultiplexing of single-cell RNA-sequencing data.

13 tional studies and human single-cell (sc)RNA-sequencing data.

14 f genes and genomic regions from whole exome sequencing data.

15 indels detected in human brain whole-genome sequencing data.

16 l RBPs and ERVs from single-cell or bulk RNA-sequencing data.

17 l variant (SV) breakpoints from whole-genome sequencing data.

18 thway via in situ hybridization maps and RNA sequencing data.

19 ised cell type annotation of single-cell RNA sequencing data.

20 assemble organelle genomes from whole genome sequencing data.

21 between two conditions from high-throughput sequencing data.

22 are gene rearrangements from next-generation sequencing data.

23 thylation patterns in whole genome bisulfite sequencing data.

24 lico tool that infers HLA genotypes from RNA-sequencing data.

25 oblem due to the short-read length of common sequencing data.

26 utionary history of tumors from tumor biopsy sequencing data.

27 cient detector of PDEs using high-throughput sequencing data.

28 assigned, are found in genome and metagenome sequencing data.

29 were defined using mouse single-nucleus RNA sequencing data.

30 eline that processes raw ribosomal profiling sequencing data.

31 ation of single-cell chromatin accessibility sequencing data.

32 l Taxonomic Units from16S ribosomal RNA gene sequencing data.

33 te copy number profiles from single-cell DNA sequencing data.

34 ces of EC heterogeneity from single-cell RNA sequencing data.

35 f these SNPs cannot be determined from exome-sequencing data.

36 ion fields available for the study of cancer sequencing data.

37 aightforward solution for the compression of sequencing data.

38 ve and selection models from high-throughput sequencing data.

39 ith continuous long-read or high-fidelity(3) sequencing data.

40 ng and quantifying small RNAs from small RNA sequencing data.

41 - in particular the analysis of metagenomic sequencing data.

42 and developed custom software that analyzes sequencing data.

43 urately and reliably process high-throughput sequencing data.

44 ct amplification quality in shallow coverage sequencing data.

45 ptomic landscape of single cell and bulk RNA sequencing data.

46 etions, and translocations using linked-read sequencing data.

47 d compatible pipeline for analyzing Nanopore sequencing data.

48 mors from 38 cancer types using whole-genome sequencing data.

49 bundances of short-read shotgun metagenomics sequencing data.

50 ion of mitochondrial genomes and related RNA sequencing data.

51 nd their insertion sites by using short-read sequencing data.

52 opular method for annotating high-throughput sequencing data.

53 s detected in high-depth, targeted, clinical sequencing data.

54 oped two phylogenetic methods based on virus sequence data: 1) to generally detect if significant tra

55 ta set of published and newly generated sRNA sequencing data (1333 sRNA-seq libraries containing more

56 phylogenomic analysis based on genotyping-by-sequencing data [6] of the 15 species of Scalesia (Darwi

57 onstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at mo

58 Such cannot be achieved by utilizing capsid sequence data alone but requires harnessing the 3D struc

59 profile, such as copy number changes and RNA-sequence data along with their survival response.

60 ic variants were examined using whole-genome sequencing data among survivors of African ancestry, fir

61 RNA-sequencing data analysis shows that Lbs are expressed as

62 Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading con

63 nted in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easil

64 a wide range and large volume of biological sequence data and literature.

65 Meta-analysis of 16S rRNA amplicon sequence data and metagenome-assembled genomes reveal pr

66 l genes by integrating features derived from sequence data and protein-protein interaction (PPI) netw

67 Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training ste

68 ohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding.

69 n in brain mural cells using single-cell RNA sequencing data and confirm perivascular staining at the

70 ancer karyotypes that better explain the DNA sequencing data and conform to a reasonable evolutionary

71 incorporate different types of evidence from sequencing data and employ complex filtering strategies

72 th human idiopathic ASD postmortem brain RNA-sequencing data and found significant enrichment of over

73 g algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconst

74 nies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large

75 To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose

76 By combining our sequencing data and mathematical modeling of transcripti

77 n, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS da

78 communication networks from single-cell RNA sequencing data and present a practical step-by-step gui

79 for potential SV breakpoints in whole genome sequencing data and proposes a probabilistic formulation

80 G) Consortium, which aggregated whole-genome sequencing data and RNA sequencing from a common set of

81 automate gene tree inference from simulated sequence data, and visualization tools for analyzing res

82 lls, process and analyze high-throughput RNA-sequencing data, and define sets of genes that accuratel

83 nd alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal inform

84 a coalescence measurement, is efficient when sequence data are collected in an ongoing surveillance s

85 e of CNVs from SNP array and next-generation sequencing data are available.

86 Short-read whole-genome sequencing data are often applied to large-scale bacteri

87 Sequencing data are often summarized at different annota

88 pipelines for analyzing Nanopore metagenomic sequencing data are still lacking.

89 Among amphibians, high-throughput sequencing data are very limited for Caudata, due to the

90 With the large amount of genome sequence data available today, particularly on basal lan

91 As multi-region, time-series and single-cell sequencing data become more widely available; it is beco

92 a promising approach in cancer prognosis as sequencing data becomes more easily and affordably acces

93 resulting in a wealth of publicly available sequence data but also a gap between gene discovery and

94 used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all

95 ial genomes can be assembled from short-read sequencing data, but the assembly contiguity of these me

96 obiology, the interpretation of whole-genome sequence data by nonspecialists becomes essential.

97 investigators understand data from targeted sequencing data by displaying the information through a

98 Evolutionary analyses of well-annotated HIV sequence data can provide insights into viral transmissi

99 Tumor DNA sequencing data can be interpreted by computational meth

100 Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richnes

101 -read Illumina and long-read Oxford Nanopore sequence data circumvented the expected error rate of th

102 oduce clone phylogenies from population bulk sequencing data collected from multiple tumor samples fr

103 stribution conditions to investigate whether sequencing data could provide a basis for future quality

104 Using whole exome sequencing data derived from a cohort of 17 unrelated CO

105 ng clustering techniques applied to targeted sequencing data derived from a large unselected populati

106 Examining whole-genome sequence data describing a chronic case of influenza B i

107 specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic g

108 Whole genome sequencing data did not identify a strain genotype-disea

109 e quantification of strains from metagenomic sequencing data, enabling the identification of genes th

110 g both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand th

111 ion that the inference of selection from DNA sequence data first requires a robust model of populatio

112 ach derives a set of peptide masses from the sequence data for comparison with the sample data, which

113 ine this question through the lens of genome sequence data for five species of southern capuchino see

114 Despite the growth in the availability of sequence data for honey bees, the phylogeny of the speci

115 on GRCh38 and the addition of high coverage sequence data for the 2504 samples in the 1000 Genomes P

116 Our interrogation of the whole-genome sequencing data for 215 breast tumors catalogued 99 recu

117 We generated exome sequencing data for 246 stillborn cases and followed est

118 We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 spec

119 We analyzed exome sequencing data for de novo variants (DNVs) in a new sam

120 ailable esophageal CD3(+) T-cell single-cell sequencing data for expression of LIGHT.

121 Clinical and RNA-sequencing data for patients with newly diagnosed GBM we

122 We analyzed whole-genome sequence data from 165 primary membranoproliferative GN

123 Sequence data from 2 HCM cohorts (n=5393) was analyzed t

124 al sclerosis (ALS) by analyzing whole-genome sequence data from 2,442 FTD/ALS patients, 2,599 Lewy bo

125 , we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of indi

126 macaque (Macaca mulatta) using whole-genome sequence data from 32 individuals in four large pedigree

127 integrate this information with whole-genome sequence data from 375 individual mosquitoes to identify

128 By analysing high-coverage genome sequence data from 4 major colour pattern loci sampled f

129 ifference in host virus fitness by analyzing sequence data from all of the viruses detected during th

130 flowering mutant was done using whole-genome sequence data from bulked DNA from a segregating F2 mapp

131 red a phylogeny of the two species using DNA sequence data from four nuclear genes (Abd-A, EF1alpha-F

132 species (genus Anguilla) and use genome-wide sequence data from more than 450 individuals sampled acr

133 rol tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple

134 In an analysis of clinical and DNA sequence data from patients with Lynch syndrome from 3 c

135 Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosqu

136 We also analyzed the sequence data from the Dallas Heart Study, a population-

137 ricans and for African Americans from TOPMed sequence data from the Framingham Heart Study (1,626 unr

138 im to solve this issue by exploring the rich sequence data from the metagenome sequencing projects.

139 s, with integrated transcriptome and genomic sequence data from the same lines.

140 We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and ident

141 Here, we used plasma cfDNA sequencing data from 2,208 samples sent for non-invasive

142 Using public whole-genome sequencing data from 2,606 samples from different cohort

143 -genome and-for a subset-whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types

144 Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types

145 es Consortium, which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types

146 , and then integrate these with whole genome sequencing data from 232 OCs.

147 G) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types,

148 G) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types

149 We analyze whole-genome sequencing data from 268 patients to catalog gene-interg

150 Combined with re-sequencing data from 48 strains, our results offer insig

151 erformed gene set enrichment analysis of RNA-sequencing data from 498 patients with neuroblastoma and

152 ied this method to published single-cell RNA sequencing data from 74 human embryos, spanning the moru

153 equencies and immune escape in exome and RNA sequencing data from 879 colon, stomach and endometrial

154 Sequencing data from a single biopsy represent a snapsho

155 y integrating whole-genome and transcriptome sequencing data from a single cancer sample.

156 condary analyses of brain MRI GWAS and exome sequencing data from adults in the UK Biobank.

157 r-friendly platform that can process raw RNA-sequencing data from any organism with an existing refer

158 differential expression analysis on the RNA-sequencing data from both cell types.

159 Whole genome sequencing data from cases and controls were compared fo

160 Whole exome sequencing data from child-parent trios were interrogate

161 ive PCR and also analyzed in single-cell RNA-sequencing data from control and IPF lungs.Measurements

162 We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated)

163 Furthermore, The Cancer Genome Atlas RNA-sequencing data from HNSCC patients also showed a positi

164 GnRH-1ns migration, we examined whole-exome sequencing data from KS subjects.

165 Advanced publicly available sequencing data from large populations have enabled info

166 present-day human contamination in low-depth sequencing data from male individuals.

167 ished and previously unpublished MPL exon 10 sequencing data from MPN patients, demonstrating that so

168 al entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human

169 Using genetic sequencing data from nearly 200,000 individuals, we unco

170 By overlaying tissue-specific RNA-sequencing data from pancreas, small intestine, ovary, k

171 Through analysis of high depth-of-coverage sequencing data from samples from 91 individuals with in

172 n of gametophyte functions, we generated RNA sequencing data from seven reproductive and two vegetati

173 atosus lesional skin microarray data and RNA sequencing data from SLE keratinocytes identified repres

174 Using high-throughput amplicon sequencing data from the Earth Microbiome Project, we pr

175 ays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son

176 Next-generation sequencing data from the hypervariable region 1 of HCV w

177 provide detailed analyses of single cell RNA sequencing data from the hypothalamus, a region with par

178 EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated

179 applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained

180 on this theme have relied on high-throughput sequencing data from uncultured species without the abil

181 Blood sequencing data from ~50,000 individuals reveal how muta

182 The DNA sequencing data generated by our simulator is representa

183 caller utilizing low-depth (8X) whole-genome sequencing data generated by Oxford Nanopore Technologie

184 ulti-tissue gene expression and whole genome sequencing data generated by the Genotype-Tissue Express

185 We used single-cell RNA sequencing data generated by the Tabula Muris consortium

186 highly imbalanced, targeted next-generation sequencing data generated using molecular inversion prob

187 Ribo-seq and the relatively small amount of sequencing data greatly facilitates the development of s

188 n recent years, phylogenetic analysis of HIV sequence data has been used in research studies to inves

189 Genome sequence data have been used to evaluate the size of the

190 obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected

191 Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences.

192 Analysis using ENCODE ChIP-sequencing data identified CTCF as the common transcript

193 Bioinformatics analysis of RNA sequencing data identifies non-productive splicing event

194 factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and op

195 d tool that can handle an enormous amount of sequencing data in a timely manner.

196 se imputation from multi-ethnic whole-genome sequencing data in admixed Hispanics/Latinos.

197 alysis of Pol I native elongating transcript sequencing data in Saccharomyces cerevisiae suggests tha

198 onstrate the power of large-scale population sequencing data in studying non-coding variant classes.

199 lyzing 16S rRNA and whole metagenome shotgun sequencing data in tandem with culture-based methods, we

200 ow for better interpretation of whole genome sequencing data in the large number of patients affected

201 ht include reduction of systematic errors in sequencing data, incorporation of other data types such

202 Human immunodeficiency virus sequence data increased the case count by 55% and expand

203 ce ambiguity in the deconvolution of admixed sequencing data into multiple haplotype-specific cancer

204 st that the contribution from the metagenome sequence data is significant with P-values less than 4.0

205 RNA-sequencing data is widely used to identify disease bioma

206 cing, and even chromatin immunoprecipitation sequencing data; it also provides information about pote

207 ndard of data sharing of metagenomes and DNA sequence data more broadly.

208 to categorize reads from massively parallel sequencing data not based on the expected properties and

209 ausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patte

210 Cross-examination of RNA-seq and ATAC sequencing data obtained at different time points reveal

211 gnostic workflow for analysis of metagenomic sequencing data obtained from clinical samples using R9.

212 e used a combination of long- and short-read sequence data of Klebsiella pneumoniae isolates (n = 1,7

213 gene detection directly from next generation sequence data of microbial pathogens.

214 cohort, we modeled maturation using 16S rRNA sequence data of the human gut microbiome in infants fro

215 In total, RNA-sequencing data of 332 samples were used for this analys

216 ylvestris, onto which we map whole-genome re-sequencing data of a cross to locate the sex locus.

217 typing and analysis of available whole-exome sequencing data of additional case/control samples from

218 In the present study, exome sequencing data of cancer patients and analysis of disti

219 ical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inf

220 By integrating single-cell RNA-sequencing data of mouse hearts at multiple postnatal st

221 of using Oxford Nanopore MinION whole-genome sequencing data of Mycobacterium tuberculosis isolates f

222 Sequencing data of the phage panning experiment are depo

223 Analysis of whole-exome sequencing data of three PRA-affected LA and three LA wi

224 The current wealth of genome sequence data offers an opportunity to better understand

225 Further, RNA sequence data on individual patients obtained from multi

226 ork inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases i

227 se-relevant STRs from whole-genome long-read sequencing data on patients with undiagnosed diseases.

228 cell compartments (also consistent with RNA sequencing data) or early blastema tissue.

229 customization options, lack of standard raw sequence data pre-processing, and insufficient capabilit

230 However, modern genetic and sequencing data present new challenges to access and sha

231 Three of five participants with available sequencing data presented compartmentalized viral reboun

232 aracterized using high throughput microbiome sequence data processed via DADA2 error correction combi

233 as a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical m

234 large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be c

235 imulation of bacterial pangenomes using real sequence data, providing a valuable tool for benchmarkin

236 ata Bank entries and the availability of new sequence data published by the WHO.

237 ction of antimicrobial susceptibilities from sequencing data remain challenges.

238 e and structural diversity from whole-genome sequencing data remains highly challenging.

239 Analysis of transcriptome/genome sequence data revealed loss of NPY/NPF-type signalling,

240 d for each species separately and metagenome sequencing data revealing adaptive mutations during the

241 Mining of genome sequence data reveals a selective sweep near the SAP2 lo

242 Analysis of RNA-sequencing data reveals that genes for inflammatory cyto

243 leotide polymorphism (hqSNP) analysis of the sequence data separated the isolates into the same two c

244 DENT-seq produces a single deep sequence data set enriched for reads near nick sites and

245 a Bayesian statistical framework and a large sequence data set from bat-CoVs (including 630 novel CoV

246 ainst comparable existing tools on small RNA sequencing data set from serum samples of 12 healthy hum

247 High-throughput sequencing data sets are usually deposited in public rep

248 medically important mites based on total RNA sequencing data sets generated in this study as well as

249 come possible to collect next-generation DNA sequencing data sets that are composed of multiple sampl

250 Using simulated and experimental RNA-sequencing data sets, we show that GSECA provides higher

251 of origin of genetic variants in large-scale sequencing data sets.

252 The comparison with neonatal single-nucleus sequencing data showed a different cellular composition

253 Our single-cell RNA-sequencing data showed that EMP-derived osteoclast precu

254 termining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across

255 Comparison with sequence data suggest that this consistent behaviour is

256 hylogenetic analysis of thrips mitochondrial sequence data supports the monophyly of two suborders, a

257 obtain genotype data and indeed whole genome sequence data, the question then becomes to define wheth

258 miRNA-mRNA interactions using miRNA and mRNA sequencing data, the complexity of the change of the cor

259 ation, compression and management of genomic sequencing data: the Moving Picture Experts Group (MPEG)

260 Single-cell RNA sequencing data, therefore, need to be carefully process

261 isrupting variants encoded from whole genome sequence data to ASD; however, this previous approach ca

262 del parameters, which was applied to genetic sequence data to estimate the fitness landscape for the

263 c-health agencies are using pathogen genomic sequence data to support surveillance and epidemiologica

264 e used the feline SNV array and whole genome sequence data to undertake a genome wide-association stu

265 ay, HLA high-resolution typing and AQP4 gene sequencing data to analyze genetic ancestry and to seek

266 integrated NDD genetics with single-cell RNA sequencing data to assess coexpression enrichment patter

267 We then collected RNA-sequencing data to assess how organismal thermal stress

268 onary simulations with an analysis of cancer sequencing data to explore WGD during cancer evolution.

269 ve successfully been applied to whole-genome sequencing data to identify genetic determinants of anti

270 ystematic approach guided by single-cell RNA-sequencing data to map the organizational structure of t

271 nalyzed and categorized with single-cell RNA sequencing data to perform cluster identification.

272 ntegrated deep phenotyping with whole-genome sequencing data using Bayesian statistics.

273 Sequencing data was analyzed by commercial software solu

274 cancer genes.Measurements and Main Results: Sequencing data was analyzed for associations among tumo

275 Using long-read sequence data we obtained complete sequences of two clos

276 improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning

277 Using matched whole-genome sequencing data, we associated several categories of RNA

278 Through integration of mRNA and miRNA sequencing data, we created networks of miRNA-mRNA inter

279 Using single-cell sequencing data, we demonstrate that ACE2 is expressed i

280 and statistical analyses of T cell receptor sequencing data, we develop a quantitative theory of hum

281 Using our previously obtained RNA sequencing data, we found that AHR mediates the expressi

282 Analyzing human whole-exome sequencing data, we identified a GLI3 loss-of-function v

283 Using RNA-sequencing data, we identified elevated mRNA levels of p

284 Using the GTEx whole genome sequencing data, we identify 20,545 high-quality pMEIs f

285 nother study, and new single-cell/nuclei RNA-sequencing data, we investigated the expression of ACE2

286 Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-cl

287 pplication of machine learning algorithms to sequencing data, we trained a 'miRNA classifier' that co

288 DNA- and RNA-sequencing data were integrated to assess the effects of

289 Next-generation sequencing data were used to reclassify tumors by moving

290 in capacity to generate large volumes of DNA sequence data, which has spurred a rapid growth in the u

291 Nubeam is also useful in analyzing 16S rRNA sequencing data, which is a more prevalent type of data

292 me 16S rRNA amplicon and shotgun metagenomic sequence data with quantification of pathogen burden and

293 integrative analysis of our single-cell RNA sequencing data with public genome-wide association stud

294 integrative analysis of our single-cell RNA sequencing data with publicly available data from genome

295 we combine long- and short-read whole-genome sequencing data with recent assembly approaches into a d

296 We combined genome sequencing data with single-cell mRNA sequencing of the

297 , we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequen

298 ans from collecting tissues to obtaining raw sequencing data, with additional time required for data

299 c insights in protein-DNA readout modes from sequencing data without available structures.

300 analysis tools for antibody next-generation sequencing data work with primary sequence descriptors,