ライフサイエンスコーパス: SNV

1 SNV-trait associations with P < 5 x 10(-8) in either ana

2 children, isotypic isolates differed by <=2 SNVs; an isotypic isolate in the remaining child differe

3 A further 35 SNVs were associated with smoking behaviour traits in th

4 for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populat

5 ns, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at >

6 Around 1.5% SNVs were discordantly converted between HG19 or HG38.

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

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

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

10 increasing the mutation rate to 12 x 10(-9) SNV frequency is approximately two- to threefold higher

11 change that could not have been created by a SNV.

12 re likely to alter an encoded protein than a SNV, which has important implications in disease as well

13 this approach is sensitive enough to achieve SNV discrimination in mixtures of sequences and even ena

14 Moreover, many newly acquired SNVs are associated with a mutational signature related

15 As a result, multiple adjacent SNVs are called individually instead of as a multi-nucle

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

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

18 Ongoing improvements to base-calling and SNV-calling methodology must continue for nanopore seque

19 level and focal copy number (CN) events, and SNV and CN signatures.

20 Indel and SNV formation are associated with both copy-number gains

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

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

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

24 ecovery of new peaks enriched for indels and SNVs.

25 ion confined to the locus and manifesting as SNVs and indels predominantly within genes.

26 rogression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-qua

27 Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate

28 NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-spec

29 or EKG traits, many of which were NKX2-5 ASE-SNVs.

30 associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding.

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

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

33 We corrected 3,388 disease-associated SNVs with >=90% precision, including 675 alleles with by

34 tured by the other three strongly associated SNVs, we focus on the other three.

35 repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations

36 By designing novel suffix-array based SNV calling methods, we have developed a practical SNV c

37 improvement over previous suffix-array based SNV calling methods.

38 ion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the dis

39 ty challenge the ability to confidently call SNVs.

40 t, two tools were used to convert the called SNVs between HG19 and HG38.

41 alling software, GeDi, that can characterise SNVs at complex variant loci and at low allele frequency

42 FGF10 also harbored at least one non-coding SNV in the predicted lung-specific enhancer region, whic

43 F10 with the putative hypomorphic non-coding SNVs implies a complex compound inheritance of these pul

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

45 based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstre

46 SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C

47 Whilst consensus SNV-calling error rates from ONT data remain substantial

48 s in CYP3A catalytic activity: three CYP3A28 SNVs reduced TST 6beta-hydroxylation; one CYP3A38 varian

49 ed TST 16beta-hydroxylation, while a CYP3A48 SNV showed enhanced NIF oxidation.

50 s between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-codi

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

52 idization probes that can selectively detect SNVs isothermally.

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

54 t rely on read mapping coordinates to detect SNVs and is therefore capable of reference-free and mapp

55 thus increasing the repertoire of detectable SNVs in tumour genomes.

56 very low allele frequency (<1%), and detects SNVs with high sensitivity at complex variant loci, dram

57 SDA differentiates SNV in the inhA gene of Mycobacterium tuberculosis at am

58 which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38).

59 Most of the discordant SNVs had low read depth, were low confidence SNVs as def

60 19 or HG38, and characterized the discordant SNVs.

61 We find that interaction-disruptive SNVs are prevalent at both rare and common allele freque

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

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

64 We found 30 CRMs with enriched SNVs and indels (FDR < 0.05).

65 We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare

66 thods for annotating and predicting cis-eQTL SNVs are under-developed.

67 en shown to accurately discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to other m

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

69 rable to characterize the impact of cis-eQTL SNVs in a context-specific manner.

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

71 n trees, to predict tissue-specific cis-eQTL SNVs.

72 ely discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to other methods by obtaining

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

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

75 RNA genetic variation at expressed SNV loci can be estimated using the proportion of allele

76 for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA).

77 Analysis of expressed SNVs in the scRNA-seq data set distinguished recipient v

78 We used the feline SNV array and whole genome sequence data to undertake a

79 ely Parallel Reporter Assays reveal that few SNVs can alter the transactivation potential of individu

80 tings to determine the accuracy of the final SNV call set and provide practical recommendations for a

81 Finally, SNVs functional impact on TST hydroxylation was measured

82 First, SNVs were called using 26 different bioinformatics pipel

83 hat Longshot achieves very high accuracy for SNV detection using whole-genome Pacific Biosciences dat

84 (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing

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

86 ge genome sequencing comparable to those for SNVs.

87 e capable of reference-free and mapping-free SNV detection.

88 s150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2.

89 e that up to ~5% of neoepitopes arising from SNVs and indels may require variant phasing for their ac

90 mentary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq stu

91 re clonal (i.e., same ST and <=2 core genome SNVs) to other isolates in stool culture.

92 eotide variants (SNVs), recessive/homozygous SNVs, or de novo copy number variants (CNVs); however, m

93 suggest caution when translating identified SNVs between different versions of the human reference g

94 and computational predictions, we identified SNVs within putative regulatory regions in promoters, tr

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

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

97 showed similar sensitivity and precision in SNV detection.

98 h, the amino acid change from the individual SNV within a codon could be different from the amino aci

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

100 By contrast, cytosine base editing induced SNVs at more than 20-fold higher frequencies, requiring

101 Among these were several rare inherited SNVs found in the mature sequence of microRNAs predicted

102 ic amplification magnifies only the intended SNV targets.

103 Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-in

104 The key SNV associated with powdery mildew resistance will be us

105 demonstrated the accurate detection of known SNVs at 0.1-0.2% allele fractions, aided by duplex UMI.

106 Later SNVs such as G28881A, G28882A, and G28883C have emerged

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

108 Intraclass median SNV differences ranged from 23 to 245.

109 ler were used to develop a solution to merge SNV to MNV.

110 PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient

111 From a total of 5.9 million SNVs detected, over 200,000 were not identified by 1000G

112 112,811 participants, a further one million SNVs were also genotyped and tested for association with

113 CYP3A28, CYP3A38 and CYP3A48 missense SNVs were identified in 300 bulls of Piedmontese breed t

114 Thirteen missense SNVs were identified and validated.

115 nymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins.

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

117 The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs th

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

119 roach, we previously detected 200-400 mosaic SNVs per cell in three human fetal brains (15-21 wk post

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

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

122 design and can easily adapt to multianalyte SNV detections.

123 y number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the signifi

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

125 e recommend HG38 (the newer version) for NGS SNV analysis.

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

127 specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated.

128 Finally, 3877 novel SNVs including the mtDNA ones, were submitted to EBI (PR

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

130 viduals can identify a large number of novel SNVs and aid in functional characterisation of the genom

131 Ten novel SNVs, including rs12616219 near TMEM182, were followed-u

132 Of the observed SNVs, no associations with KRd therapy response were fou

133 memory resource requirements, is capable of SNV detection at very low allele frequency (<1%), and de

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

135 of scRNA-seq for the future investigation of SNV function.

136 Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the adva

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

138 emented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improves the identification of soma

139 structural variants, but low conservation of SNVs.

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

141 e provide a blueprint to study the impact of SNVs derived from genetic variation or disease associati

142 Molecular modelling showed that most of SNVs were distal to CYP3A active site, suggesting indire

143 A significant number of SNVs could not be converted between HG19 and HG38.

144 Practices resulted in the highest number of SNVs identified with a high concordance.

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

146 is capable of calling a reasonable number of SNVs with high accuracy.

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

148 ease in data volume, direct visualization of SNVs together with associated protein sequences/structur

149 To date, the impact of genome version on SNV identification has not been rigorously assessed.

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

151 ons (SNVs) in protein coding regions and one SNV in the 5' untranslated region (UTR) were identified

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

153 For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80

154 nymous single-nucleotide variants (SNVs), or SNV neoantigens.

155 nce implementation of the described pairwise SNV matrix (Hansel) and greedy haplotype path traversal

156 PGG.SNV archives 265 million SNVs across 220,147 present-day

157 evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.

158 pared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients s

159 lling methods, we have developed a practical SNV calling software, GeDi, that can characterise SNVs a

160 We present SNVs discovered by whole genome sequencing (WGS) of thre

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

162 cular, we can analyze VAFs from newly probed SNVs to improve existing estimates, an attribute not pre

163 ne conveniently handles any number of probed SNVs in the samples.

164 le cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capab

165 up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs

166 e likely to alter TF binding sites than rare SNVs in the human population.

167 ss the ability of Meltos to correctly refine SNV trees with SV information, we tested Meltos on two s

168 s, uncorrelated with the previously reported SNVs.

169 of chromosomal segments at high resolution, SNVs and Indels (corrected for aneuploidy), regions with

170 loid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regi

171 Individuals homozygous for rs384467435 SNV showed a reduced TST 6beta-hydroxylation.

172 This method, implemented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improv

173 type VI, characterized by the four signature SNVs C241T (5'UTR), C3037T (nsp3 F924F), C14408T (nsp12

174 trains missing one or two of these signature SNVs fail to persist implies possible interactions among

175 f the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228

176 ness gain conferred by the type VI signature SNVs.

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

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

179 Sixteen SNVs were associated with at least one of the smoking be

180 cus harboring both risk variants and somatic SNVs in cis-regulatory elements upregulating MYC express

181 lling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.

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

183 ddition to demonstrated increases in somatic SNVs during aging in normal brains, somatic mutation may

184 cistromes reveals the enrichment of somatic SNVs in prostate tumors as opposed to adjacent normal ti

185 geted sequencing for a collection of somatic SNVs.

186 or phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somati

187 ignal to readily detect and quantify somatic SNVs in cfDNA.

188 ss-contamination by tracking source-specific SNVs.

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

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

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

192 is shows that MNVs can be more damaging than SNVs even when both induce missense changes, and are an

193 In addition, we found that SNV calling quality varies across different functional g

194 This study suggests that SNVs may become an important consideration in SARS-CoV-2

195 script used the phasing information from the SNV VCF and determined whether SNVs were at the same cod

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

197 Also, the SNVs from BGISEQ data is highly consistent with Hiseq re

198 We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole

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

200 We summarise the SNVs by genomic position, type of sequence gene context

201 st implies possible interactions among these SNVs.

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

203 The specific combination of these SNVs suggests that Takabuti belonged to mitochondrial ha

204 Furthermore, this SNV was significantly associated with AgP in a populatio

205 late in the remaining child differed by 3 to SNVs relative to the other isolates from that child.

206 Although relatively rare compared to SNVs and present in ~10% of neurons, SVs in developing h

207 known pathogenic genetic variants are due to SNVs, base editing holds great potential for the treatme

208 ing sequences of 174 pathogenic transversion SNVs with >=90% precision.

209 sive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally des

210 Two SNVs are associated with expression levels of nearby gen

211 ntified a single rare nonsynonymous variant (SNV) rs142946965 [p.R215I] in ADAM17 co-segregating with

212 ray-based somatic single nucleotide variant (SNV) calling algorithm that does not rely on read mappin

213 on which improves single-nucleotide variant (SNV) calling performance from otherwise modest levels.

214 ns for a pairwise single nucleotide variant (SNV) co-occurrence matrix and greedy graph traversal alg

215 rate of clustered single-nucleotide variant (SNV) mutation in cis with non-recurrent rearrangements w

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

217 d with a distinct single nucleotide variant (SNV) profile.

218 are nonsynonymous single nucleotide variant (SNV) within the C-terminal leucine rich repeat (LRR) dom

219 resis [PFGE], and single-nucleotide variant [SNV] analysis).

220 etion [indel] vs. single nucleotide variant [SNV]) are unknown.

221 ble granularity, single-nucleotide variants (SNV).

222 few subconsensus single nucleotide variants (SNVs) above ~0.5%, and experimental passages demonstrate

223 of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generati

224 ied 85.7 million single-nucleotide variants (SNVs) and 10.5 million indel variants, including potenti

225 ted ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indel

226 the C->T type of single-nucleotide variants (SNVs) and appear to be enriched in genic regions.

227 ployed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV

228 bCYP3A missense single nucleotide variants (SNVs) and evaluated their functional effects.

229 for engineering single nucleotide variants (SNVs) and has been used to create targeted mutations in

230 s this issue for single nucleotide variants (SNVs) and insertions/deletions (indels).

231 ent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.

232 ucity of somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) compar

233 ly 2% of de novo single-nucleotide variants (SNVs) appear as part of clustered mutations that create

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

235 riants, cis-eQTL single nucleotide variants (SNVs) are of particular interest for their crucial role

236 many of the same single nucleotide variants (SNVs) are shared between germline and somatic mutation d

237 e evidence of 45 single-nucleotide variants (SNVs) associated with human diseases that substantially

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

239 as 3 core genome single-nucleotide variants (SNVs) discriminate between genetically distinct isolates

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

241 al and subclonal single nucleotide variants (SNVs) encoding putative HLA class I-restricted neoantige

242 ing able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and

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

244 comparisons used single nucleotide variants (SNVs) identified in the exome sequences across all sampl

245 sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of

246 detect and phase single-nucleotide variants (SNVs) in diploid genomes.

247 Single nucleotide variants (SNVs) in intronic regions have yet to be systematically

248 relation of five single nucleotide variants (SNVs) in the CRBN gene with clinical response and outcom

249 driver status of single nucleotide variants (SNVs) in the human cancer genome.

250 ecting ~1 aM RAS single nucleotide variants (SNVs) in the plasma of CRC patients.

251 Indeed, somatic single-nucleotide variants (SNVs) increase with age in the human brain, in a somewha

252 introduction of single-nucleotide variants (SNVs) into DNA or RNA in living cells - is one of the mo

253 racterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert D

254 ant deletions or single-nucleotide variants (SNVs) involving TBX4 (n = 8 and n = 2, respectively) or

255 Single nucleotide variants (SNVs) located in transcriptional regulatory regions can

256 how thousands of single nucleotide variants (SNVs) may affect gene expression.

257 ified non-coding single-nucleotide variants (SNVs) near (e.g., rs10166942[C]) or within (rs17862920[T

258 oncoding somatic single-nucleotide variants (SNVs) of unknown function are reported in tumors.

259 ed up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking in

260 ovo heterozygous single-nucleotide variants (SNVs) or single-base insertions/deletions, 3 siblings ha

261 cell-free DNA or single-nucleotide variants (SNVs) out of reach.

262 (<1% frequency) single-nucleotide variants (SNVs) revealed that the gene encoding brain-derived neur

263 dentify the rare single nucleotide variants (SNVs) that occur in non-coding regions and determined th

264 built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comp

265 ring analysis of single nucleotide variants (SNVs) uncovered a distinct oncogenic clone of cells carr

266 10(-10) de novo single-nucleotide variants (SNVs) were generated per cell division, which is compara

267 that off-target single-nucleotide variants (SNVs) were rare in embryos edited by CRISPR-Cas9 or aden

268 nterpretation of single-nucleotide variants (SNVs)(5).

269 least 99.91% for single-nucleotide variants (SNVs), 95.98% for insertions and deletions <50 bp (indel

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

271 ow recurrence of single-nucleotide variants (SNVs), but showed prevalent genomic instability caused b

272 tions, including simple nucleotide variants (SNVs), copy-number variations (CNVs), and structural var

273 ls), followed by single nucleotide variants (SNVs), have the highest probability of modifying peak ca

274 , manifesting as single nucleotide variants (SNVs), mobile element insertions, and structural changes

275 m non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens.

276 ions via de novo single nucleotide variants (SNVs), recessive/homozygous SNVs, or de novo copy number

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

278 iants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nu

279 types, including single-nucleotide variants (SNVs), small insertions and deletions (indels), copy-num

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

281 identified 2719 single nucleotide variants (SNVs).

282 hundred-million single nucleotide variants (SNVs).

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

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

285 y (KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and

286 ns [CNAs], SNPs, single nucleotide variants [SNVs], CpG methylation).RESULTSWe documented greater tha

287 Instead, similar to inherited risk variants, SNVs accumulate in cistromes of master transcription reg

288 e detection of single nucleotide variations (SNV) in DNA and RNA sequences in the mix-and-read format

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

290 ber of somatic single nucleotide variations (SNVs) in AD brain specimens increases significantly with

291 in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5' un

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

293 The missense single nucleotide variations (SNVs) rs142548867 in EEFSEC (c.668C>T), rs574301770 in Z

294 Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as

295 , ranging from single nucleotide variations (SNVs) to large, complex copy number variations (CNVs), h

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

297 tion from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into

298 RNA also results in the rise of genome-wide SNVs.

299 nd rarely resulted in death as compared with SNV-driven tumors.

300 r stronger purifying selection compared with SNV-induced missense changes.