ライフサイエンスコーパス: SNV

1 SNV patterns resembled those found in cancer cell genome

2 SNV RNA levels were higher in the lungs but not differen

3 SNVs with a frequency of >2% in brain were also present

4 -specific variant panels, which covers 99.0% SNVs of minor allele frequency >/=0.1%, and its value fo

5 were considered reinfection, and those 3-10 SNVs apart (or without whole-genome sequences) were cons

6 were considered relapses, paired samples >10 SNVs apart were considered reinfection, and those 3-10 S

7 c diversity of isolates was between 0 and 15 SNVs during the outbreak; molecular clock calculations e

8 erved a minimum of 7 SNVs and maximum of 153 SNVs between isolates from different individuals.

9 elevated SNV density, ranging from 2 to 16% SNVs.

10 At least 163 SNVs, including 31 synonymous ones, were shown to cause

11 On average, 235 SNVs could be directly confirmed in the original fibrobl

12 We obtained 2640 SNVs of interest, most of which occur rarely in populati

13 of individuals whose isolates were within 40 SNVs of each other.

14 ndividual with multisite colonization was 41 SNVs, with no systematic divergence among body sites.

15 Analysis of 334,652 SNVs that were consistent between informatics pipelines

16 h isolates with identical antibiograms (12.7 SNVs [95% CI, 12.5 to 12.8]) (P < 0.0001).

17 We observed a minimum of 7 SNVs and maximum of 153 SNVs between isolates from diffe

18 resholds differed between 129S-Cdh23(c.753A) SNV and 129S1.B6-Cdh23(ahl) congenic mice, and a linkage

19 mutation rate of approximately 1.5 x 10(-8) SNVs per site per generation with a significantly higher

20 so shed light on the access to more accurate SNV identification in the future.

21 of 51,138 protein functional site affecting SNVs (pfsSNVs), a pan-cancer analysis revealed 142 somat

22 olyclonal antibody administered 5 days after SNV infection conferred significant protection against d

23 s an effort to collect, classify and analyze SNVs that may affect the optimal response to currently a

24 We integrated multiple CNV and SNV analyses and extensive experimental validation to id

25 d for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

26 the mutational signatures, copy number, and SNV mutational profiles reflect the expected heterogenei

27 Probands carry more gene-disruptive CNVs and SNVs, resulting in severe missense mutations and mapping

28 with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits.

29 he batch effect, virus expression level, and SNVs as part of next-generation sequencing (NGS) data an

30 hould be larger for a pathogenic variant and SNVs flanking it than for a random variant.

31 vide valuable benchmark for state-of-the-art SNV calling methods, but also shed light on the access t

32 potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, unc

33 eal a novel mechanism for disease-associated SNVs and provide a platform for modeling morphological c

34 in GWA regions possessing rare PD-associated SNVs, we identified RAD51B.

35 eviously identified schizophrenia-associated SNVs.

36 These associated SNVs are less-common; independent from previous GWAS sig

37 ial tests for genotype and allelic status at SNV positions between compatible sequences.

38 showed that the deleteriousness of autosomal SNVs was significantly higher in female probands (p = 0.

39 stitute of Standards and Technology for both SNVs (>99.99%) and indels (99.92%) and add a validated t

40 For both SNVs and indels, the distributions of coverage depth, ge

41 the first report of lethal disease caused by SNV in an adult small-animal model.

42 Calling SNV on UDT-Seq data, which were of much higher read-dept

43 a stringent GATK-based pipeline for calling SNVs including SNPs and RNA editing events in RNA-seq re

44 VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expan

45 Unlike germline and cancer SNVs, which are often caused by errors in DNA replicatio

46 ), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression

47 e identified less-common and rare non-coding SNVs associated with BMD independently from GWAS common

48 x 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P).

49 n interactions on deleterious protein-coding SNVs; and identify pathways and networks that have likel

50 -of-function effects of multiple rare coding SNVs found in SCZ subjects in the GIT1 (G protein-couple

51 The estimated mean number of real coding SNVs (656 variants, approximately 3% of all coding HQ SN

52 showing exceptional recall in calling common SNVs.

53 NV (G allele), MLEC SNV (T allele), and DDX5 SNV (G allele).

54 analysis identifies potentially deleterious SNVs present on drug-binding residues that are relevant

55 mic depletion in SNV incidence, SNV density, SNVs of coding genes, MHC class I and II genes, and mito

56 biological data, SiNVICT was able to detect SNVs and indels with variant allele percentages as low a

57 ct and quantify SNVs in the RNA and discover SNVs with altered frequencies between distinct cellular

58 ttings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asym

59 class I and II genes, and mitochondrial DNA SNVs.

60 ltering SVs have larger effect sizes than do SNVs and indels.

61 x chromosomes, which have a greatly elevated SNV density, ranging from 2 to 16% SNVs.

62 on competition or amplification and enables SNV detection at 1% abundance.

63 FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types.

64 FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross

65 ve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry

66 res discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area und

67 ulations of different ancestry from non-eQTL SNVs with an AUC of 0.939.

68 identified approximately 900 somatic exonic SNVs that disrupt splicing.

69 e large for a causative variant and flanking SNVs.

70 the AUROCs were lower (0.86 for SE; 0.80 for SNV) when data for all individuals were included, they r

71 urve (AUROC) was high (0.96 for SE; 0.98 for SNV).

72 ar levels of sensitivity and specificity for SNV detection.

73 For SNVs, the proportion of false-positive variants was high

74 For SNVs, we show that MMR deficiency both increases their f

75 encing study of 55 postmortem ASD brains for SNVs in 78 known ASD candidate genes.

76 Although there are additional values for SNVs detection, the assembly-based approach would have g

77 We also found SNVs for which we can anticipate allelic imbalance from

78 factor motif, except the more than 100 GMAS SNVs in linkage disequilibrium with polymorphisms report

79 Intriguingly, GMAS SNVs in general do not alter the strongest consensus pos

80 We predicted that GMAS SNVs often alter binding of splicing factors, with SRSF1

81 identified exclusively by WES whereas 692 HQ SNVs and 105 indels were identified exclusively by WGS.

82 A mean of 105 coding HQ SNVs and 32 indels was identified exclusively by WES whe

83 variants, approximately 3% of all coding HQ SNVs) identified by WGS and missed by WES was greater th

84 , a mean of 74,398 (95.3%) high-quality (HQ) SNVs and 9,033 (70.6%) HQ indels were called by both pla

85 AF V600 mutation, and >80% of the identified SNVs consistent with UV damage.

86 ference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations.

87 nst rapid fibrosis progression for the IL28B SNV (G allele), MLEC SNV (T allele), and DDX5 SNV (G all

88 Importantly, SNV-specific neutralizing polyclonal antibody administer

89 The integrated assay exhibits improved SNV discrimination rather than hybridization probes rely

90 titution sequencing errors not only improves SNV call precision at low mapping quality regions, but a

91 d approach that would recover 99% of imputed SNVs.

92 heetahs reveals extreme genomic depletion in SNV incidence, SNV density, SNVs of coding genes, MHC cl

93 extreme genomic depletion in SNV incidence, SNV density, SNVs of coding genes, MHC class I and II ge

94 elvaraj et al demonstrated single individual SNV phasing is possible with proximity ligated (HiC) seq

95 Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid in

96 ic amplification magnifies only the intended SNV targets.

97 The other novel locus (lead SNV rs56202902; p = 1.54 x 10(-11)) covered a large, gen

98 les here demonstrate that even if the mapped SNVs predicted as deleterious may not result in signific

99 ly distinct subtypes were identified (median SNV difference 273, IQR 162-399) at a rate of 38 (IQR 34

100 Intraclass median SNV differences ranged from 23 to 245.

101 th recurrent MRSA SSTI, the intrahost median SNV difference was 7.5 (1-48).

102 identified genomic variants of 16.3 million SNVs and 2.3 million InDels in mapped regions.

103 identified genomic variants of 18.9 million SNVs and 3.4 million Indels in the mapped regions.

104 dence of an association at a novel, missense SNV, rs7739323, which is located in the ubiquitin protei

105 We propose that rare missense SNVs in DIXDC1 contribute to psychiatric pathogenesis by

106 ogression for the IL28B SNV (G allele), MLEC SNV (T allele), and DDX5 SNV (G allele).

107 a validated truth catalog that has 26% more SNVs and 45% more indels.

108 oplet digital PCR experiments confirmed more SNVs as mosaic with AF as low as 0.01%, suggesting that

109 Mosaic SNVs were distributed uniformly across the genome and we

110 as low as 0.01%, suggesting that 1035 mosaic SNVs per fibroblast cell is the true average.

111 ll in children has 1035 mostly benign mosaic SNVs.

112 Finally, AF distribution of mosaic SNVs had distinct narrow peaks, which could be a charact

113 , suggesting that a major fraction of mosaic SNVs in fibroblasts arises during development.

114 hildren, increasing the proportion of mosaic SNVs to 22%.

115 design and can easily adapt to multianalyte SNV detections.

116 gible numbers of false positive and negative SNV and INDEL calls that were shown to be enriched among

117 Among nine SNVs, we explored the functional impact of the de novo m

118 association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG

119 at the fraction of deleterious nonsynonymous SNVs is higher than previously reported; quantify the ef

120 at the fraction of deleterious nonsynonymous SNVs is significantly higher for Mendelian versus comple

121 Notably, SNVs causing intron retention were enriched in tumor sup

122 SNPs and RNA editing sites as well as novel SNVs, with the majority of DVRs corresponding to known R

123 have great risk of false discovery of novel SNVs.

124 NV hits for genes impacted by autism de novo SNVs (P=0.019 for non-synonymous SNV genes) did not surv

125 otwithstanding this prescreening, 84 de novo SNVs affecting the coding region were identified, which

126 These results suggest that de novo SNVs and CNVs affecting the coding region are a major ca

127 azilian trios showed that cases with de novo SNVs tend not to have de novo CNVs and vice-versa.

128 es with the most significantly associated NS SNVs, while regions associated with PD by a recent Genom

129 nriched in genes containing PD-associated NS SNVs.

130 nor any gene carrying a higher burden of NS SNVs was significantly associated with PD status after m

131 Phasing of single nucleotide (SNV), and structural variations into chromosome-wide hap

132 The constructs of SNV and ANDV NPcore exclude the N- and C-terminal portio

133 human genome and compared the efficiency of SNV calling between the assembly-based and alignment-bas

134 ation and edema within the alveolar septa of SNV-infected hamsters, results which are similar to what

135 increases the sensitivity and specificity of SNV and indel detection at very low variant allele frequ

136 Analysis of SNVs in relation to allele-specific copy-number changes

137 e genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scal

138 As previously reported, the density of SNVs varies along the chromosomes, with the arms of chro

139 We analyzed the effects of SNVs on enzyme active sites, ligand binding sites, and v

140 igned specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and

141 ocused on globally quantifying the impact of SNVs on protein stability.

142 missed by WES was greater than the number of SNVs identified by WES and missed by WGS (26 variants).

143 led no significant increase in the number of SNVs per cell, suggesting that a major fraction of mosai

144 disproportionately increases the numbers of SNVs in genes, rather than in nongenic regions of the ge

145 structural variants into phased scaffolds of SNVs.

146 ous candidate variant together with a set of SNVs flanking it.

147 We determined the spectrum of SNVs in a single human cell after ultraviolet radiation,

148 One SNV with a minor allele frequency <0.01, (rs3025380 at D

149 Twenty-one SNVs were genome-wide significant (P<5x10(-)(8)) for BP,

150 The P2255T SNV directly affected Kal9 protein function, causing inc

151 icipants, 30 of 4300 (0.7%) had a pathogenic SNV and six (0.1%) had a disruptive variant that was exp

152 cipants, six of 2203 (0.3%) had a pathogenic SNV and six (0.3%) had an expected pathogenic disruptive

153 nic, whereas 52 (1.2%) had likely pathogenic SNVs.

154 ant, whereas 13 (0.6%) had likely pathogenic SNVs.

155 The maximum within-patient SNV difference for an individual with multisite coloniza

156 We also compared SiNVICT with other popular SNV callers such as MuTect, VarScan2 and Freebayes.

157 hms designed to help identify and prioritize SNVs across the human genome for further investigation.

158 le up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity.

159 fic perturbation, we can detect and quantify SNVs in the RNA and discover SNVs with altered frequenci

160 The overall median (range) SNV difference between isolates was 173 (1-339).

161 than non-disease related variants, and rare SNVs tend to disrupt local interactions to a larger exte

162 ts of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTL

163 itive results: for instance, disease-related SNVs create stronger changes in localized frustration th

164 of short reads was needed to ensure reliable SNV calling and to generate assembled contigs with a goo

165 s, uncorrelated with the previously reported SNVs.

166 Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts fo

167 among a selection of popular multiple sample SNV callers, while showing exceptional recall in calling

168 e testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC

169 iously published work with the single sample SNV caller genotype model selection (GeMS), a multiple s

170 We refer to scaffolded SNVs as local haplotypes (LH).

171 We developed a robust and sensitive SNV PMM calling approach integrating complementary calle

172 rk to increase sensitivity of calling shared SNVs.

173 To prioritize shortlisted SNVs we consider each homozygous candidate variant toget

174 Vs, we identified 17 loss of functional site SNVs and 60 gain of functional site SNVs which are signi

175 nal site SNVs and 60 gain of functional site SNVs which are significantly enriched in patients with s

176 odulating potential of canonical splice-site SNVs.

177 le-nucleotide polymorphisms/variations (SNPs/SNVs; called SNPs hereafter).

178 For the SOC3 SNV, the minor allele (A) increased the risk for rapid f

179 R-TKI-resistant patients to identify somatic SNVs, small indels, CNVs and gene fusions in 508 tumor-r

180 We identified thousands of somatic SNVs by single-cell sequencing of 36 neurons from the ce

181 Less obviously, we observe that somatic SNVs associated with oncogenes and tumor suppressor gene

182 ll as smaller de novo CNVs and exon-specific SNVs missed by exome sequencing in neurodevelopmental ge

183 One such SNV, KALRN-P2255T, displays a penetrance that greatly ex

184 UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systol

185 fic binding, as compared with non-synonymous SNV derived neoantigens.

186 ism de novo SNVs (P=0.019 for non-synonymous SNV genes) did not survive Bonferroni correction.

187 nders was three times that of non-synonymous SNV mutations.

188 nd 10 002 putatively 'benign' non-synonymous SNVs from UCSC.

189 Both SE and the SNV diversity measures were significantly different for

190 reaction is agnostic to the position of the SNV within the target sequence.

191 most types of protein functional sites, the SNV pattern differs between germline and somatic mutatio

192 Clinical significance of only 9.56% of the SNVs is known in ClinVar, although 79.02% are predicted

193 ecific manner, with approximately 70% of the SNVs occurring on the last base of exons.

194 thresholds and cochlear pathologies of these SNV mice with those of congenic (B6.129S1-Cdh23(Ahl+) an

195 Many of these SNVs alter Wnt/beta-catenin signaling activity of the ne

196 iched in tumor suppressors, and 97% of these SNVs generated a premature termination codon, leading to

197 iants (SNVs); however, the majority of these SNVs have unknown functional consequences, leaving their

198 ted, consistent with their susceptibility to SNV disruption.

199 ithin WES regions, particularly those due to SNVs.

200 We find that private, inherited truncating SNVs in conserved genes are enriched in probands (odds r

201 ssion model, we show that private truncating SNVs and rare, inherited CNVs are statistically independ

202 Two SNVs are associated with expression levels of nearby gen

203 el phylogenetic analysis algorithm that uses SNV positions and can be used to classify the patient po

204 Consistent with this, using SNV variation as a proxy for mutational input, we report

205 ntropy (SE) and a single nucleotide variant (SNV) analysis.

206 analyses for both single nucleotide variant (SNV) and copy number variant (CNV) alleles allowed for i

207 r popular somatic single nucleotide variant (SNV) calling methods (Varscan, SomaticSniper, Strelka an

208 riant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastru

209 orphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-

210 tion of published single-nucleotide variant (SNV) de novo mutations showed evidence consistent with p

211 Single nucleotide variant (SNV) detection procedures are being utilized as never be

212 nstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA

213 o gene-disruptive single-nucleotide variant (SNV) had been detected by microarray or whole-exome sequ

214 number (CNV) and single nucleotide variant (SNV) in a small set of genes from individuals with epile

215 d reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-

216 s (indels) and of single-nucleotide variant (SNV) mutations.

217 n-synonymous (NS) single nucleotide variant (SNV) nor any gene carrying a higher burden of NS SNVs wa

218 A single nucleotide variant (SNV) of the cadherin 23 gene (Cdh23(c.753A)), common to

219 ities caused by a single nucleotide variant (SNV) or riboSNitches.

220 ctional inherited single-nucleotide variant (SNV) that accounts for several breast cancer risk-associ

221 detecting ASE on single nucleotide variant (SNV), exon and gene levels from sequencing data without

222 novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 x 10(-18)) comprised intronic

223 have focused on single nucleotide variants (SNV), which are relatively easy to detect and can be des

224 se nonsynonymous single-nucleotide variants (SNVs) across the whole genome of 216 neovascular AMD cas

225 enes, exons, and single-nucleotide variants (SNVs) all demonstrated positive selection or accelerated

226 nome has 327,050 single nucleotide variants (SNVs) and 79,529 insertion-deletion events that result i

227 udes >59 million single-nucleotide variants (SNVs) and 9,212 private copy number variants (CNVs), of

228 ve set of exonic single-nucleotide variants (SNVs) and copy number variants (CNVs) from 2,377 familie

229 iants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), in the non-coding

230 ate detection of single nucleotide variants (SNVs) and indels from cfDNA is constrained by several fa

231 analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from

232 mean numbers of single-nucleotide variants (SNVs) and small insertions/deletions (indels) detected p

233 red samples </=2 single-nucleotide variants (SNVs) apart were considered relapses, paired samples >10

234 ng the impact of single nucleotide variants (SNVs) are assuming ever increasing importance.

235 and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chi

236 2 million novel, single-nucleotide variants (SNVs) at an estimated false discovery rate of <1.0%.

237 o non-synonymous single-nucleotide variants (SNVs) by conducting whole exome sequencing of 18 trios c

238 a gene can have single nucleotide variants (SNVs) due to single nucleotide polymorphisms (SNPs) in t

239 disease-causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putat

240 han 3 million of single nucleotide variants (SNVs) from the whole human genome and compared the effic

241 Single nucleotide variants (SNVs) identified in cancer genomes can be de-convolved u

242 mately 1 million single nucleotide variants (SNVs) identified in high-coverage exome sequences of 651

243 nable pathogenic single-nucleotide variants (SNVs) in all 4300 European- and 2203 African-ancestry pa

244 entified somatic single-nucleotide variants (SNVs) in cancer, the extent to which these variants trig

245 numbers of rare single-nucleotide variants (SNVs) in coding regions of the genome.

246 Single-nucleotide variants (SNVs) in single cells from both samples occurred sporadi

247 s indicated that single nucleotide variants (SNVs) in the gene encoding the actin cytoskeletal regula

248 scape of somatic single-nucleotide variants (SNVs) in the human brain.

249 uence-disrupting single-nucleotide variants (SNVs) in these individuals compared with psychiatrically

250 which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the reg

251 Discovering single-nucleotide variants (SNVs) is also of great importance for the identification

252 etection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogenei

253 induced de novo single-nucleotide variants (SNVs) is strikingly different; with clustered mutations

254 threshold of 40 single-nucleotide variants (SNVs) or fewer to define subtypes and infer recent trans

255 g of 4.7 million single-nucleotide variants (SNVs) plus 0.7 million small (1-50 bp) insertions and de

256 (<1% frequency) single-nucleotide variants (SNVs) revealed that the gene encoding brain-derived neur

257 hes for multiple single nucleotide variants (SNVs) that are scaffolded by the same reads.

258 Identifying single nucleotide variants (SNVs) that contribute to differences in drug response an

259 o identify known single nucleotide variants (SNVs) using 6 control samples with publically available

260 kage for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples.

261 per patient) and single-nucleotide variants (SNVs) were analyzed, with reference to the index patient

262 Single nucleotide variants (SNVs) were used to reconstruct the phylogeny of sequence

263 nd uncovered 127 single nucleotide variants (SNVs) which are missing from public databases.

264 ating as fast as single nucleotide variants (SNVs), and elevated amounts of deletions.

265 ith all possible single nucleotide variants (SNVs), and measure strong effects on transcript abundanc

266 ations including single-nucleotide variants (SNVs), chromosomal deletions, as well as integration of

267 eterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements.

268 ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences.

269 iants, including single nucleotide variants (SNVs), in glutamatergic synaptic genes are enriched in s

270 the detection of single nucleotide variants (SNVs), information on copy number variants (CNVs) is of

271 Single nucleotide variants (SNVs), particularly loss-of-function mutations, are sign

272 o high frequency single nucleotide variants (SNVs), rs11544484 (V256I, Minor Allele Frequency = 0.27)

273 However, single-nucleotide variants (SNVs), small insertions/deletions (indels), copy-number

274 yses has been on single nucleotide variants (SNVs), with the contribution of small insertions and del

275 we identified 56 Single Nucleotide Variants (SNVs).

276 s in recognizing single nucleotide variants (SNVs).

277 several hundred single-nucleotide variants (SNVs).

278 ed multitudes of single nucleotide variants (SNVs).

279 identified 2719 single nucleotide variants (SNVs).

280 od to prioritize single-nucleotide variants (SNVs).

281 fferentiation of single nucleotide variants (SNVs).

282 covered numerous single-nucleotide variants (SNVs); however, the majority of these SNVs have unknown

283 o non-synonymous single-nucleotide variants (SNVs; P=5.4 x 10(-4)) and targets of the Fragile X menta

284 A total of 4,964 sequence variants (SNVs) were observed and 80% were rare with MAF <1%.

285 rsity (mean 17.5 single nucleotide variants [SNVs] [95% confidence interval {CI}, 17.3 to 17.8]) comp

286 cond derivative and standard normal variate (SNV) transformation pre-treatments were applied to class

287 based on human single nucleotide variation (SNV) analysis of two separate HeLa cell lines and severa

288 traint based on single-nucleotide variation (SNV) and was independently correlated with measures of e

289 ovides reliable single-nucleotide variation (SNV) detection across the single-cell genome, facilitati

290 r, identifying single-nucleotide variations (SNVs) can be accomplished by sequencing kindred cells.

291 Single nucleotide variations (SNVs) can result in loss or gain of protein functional s

292 ted 200 to 400 single-nucleotide variations (SNVs) per cell.

293 same 10 novel single nucleotide variations (SNVs), leading us to hypothesize that one patient was in

294 den of somatic single-nucleotide variations (SNVs), with each case containing a TERT promoter (TERT-p

295 of NP (NPcore) encoded by Sin Nombre virus (SNV) and Andes virus (ANDV), which are two representativ

296 and fluorescently labeled Sin Nombre virus (SNV) to the integrin PSI domain stimulates higher affini

297 the natural reservoirs of Sin Nombre virus (SNV), the etiologic agent of most HCPS cases in North Am

298 total DNA used for the biological data where SNVs and indels could be detected with very high sensiti

299 cyclophosphamide, followed by infection with SNV, results in a vascular leak syndrome that accurately

300 n set) using the LS-SVM and the spectra with SNV pre-treatment.