ライフサイエンスコーパス: gene prediction

1 y of model reconstruction and, subsequently, gene prediction.

2 to contain relevant information for ASD risk gene prediction.

3 ay probe selection to de novo non-coding RNA gene prediction.

4 ful and general approach for microRNA target gene prediction.

5 s is a useful and general approach for miRNA gene prediction.

6 del implementations for ab initio eukaryotic gene prediction.

7 rotein sequences of related genes to aid the gene prediction.

8 ction missed by conventional mutagenesis and gene prediction.

9 le, powerful way to increase the accuracy of gene prediction.

10 become a practical and powerful strategy for gene prediction.

11 was isolated, and its sequence confirmed the gene prediction.

12 ul genome analysis depends on the quality of gene prediction.

13 rious computing techniques are available for gene prediction.

14 designing a new combined tool for automatic gene prediction.

15 performance of their network-based candidate gene predictions.

16 ials and few species-specific protein-coding gene predictions.

17 equence information and to verify or improve gene predictions.

18 class are a valuable source of evidence for gene predictions.

19 tructure of genes can improve the quality of gene predictions.

20 additional criteria to refine many existing gene predictions.

21 logs in Homo for 82% (15,250) of Monodelphis gene predictions.

22 an 20% would have been detected by ab initio gene predictions.

23 ally combine multiple probe predictions into gene predictions.

24 standing tool that identifies such erroneous gene predictions.

25 or exons, leading to biologically irrelevant gene predictions.

26 validation together with gradually improving gene predictions.

27 y of using RT-PCR as a method for confirming gene predictions.

28 luding MEGF1, G3BP, and several of the novel gene predictions.

29 den input/output Markov models for combining gene predictions.

30 logy studies, domain searches, and ab initio gene predictions.

31 pecific HMMs that are able to offer unbiased gene predictions.

32 ired genes, functionally validating enhancer-gene predictions.

33 raining and subsequently generates ab initio gene predictions.

34 y in mutation rates on false-positive driver gene predictions.

35 e the quality and biological context of tRNA gene predictions.

36 portal for interactive exploration of these gene predictions.

37 tegrates RNA-Seq read information into final gene predictions.

38 ssembly scaffolds, including eight annotated gene predictions.

39 via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicti

40 nown genes, JIGSAW can substantially improve gene prediction accuracy as compared with existing metho

41 at this combined approach appears to improve gene prediction accuracy compared with current methods t

42 tegrative framework to improve the candidate gene prediction accuracy for anatomical entities by comb

43 Gene prediction accuracy improves steadily from 1X throu

44 This experiment establishes a baseline of gene prediction accuracy in Caenorhabditis genomes, and

45 odels with cross-species comparison improves gene prediction accuracy.

46 annotation to generate and modify ab initio gene predictions across the whole genome.

47 se of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predict

48 upported the intron-exon boundaries of their gene predictions, adding only 5'- and 3'-untranslated re

49 m are generated by GeneMark-ES, an ab initio gene prediction algorithm based on unsupervised training

50 in coding by an MCMV-specific version of the gene prediction algorithm GeneMarkS.

51 The TWINSCAN gene prediction algorithm was adapted for the fungal pat

52 Using the GENSCAN gene prediction algorithm, we generated predicted transc

53 y runs are used to automatically retrain its gene-prediction algorithm, producing higher-quality gene

54 formance of 780 distinct classifiers (set of genes + prediction algorithm) in full cross-validation.

55 Gene prediction algorithms (or gene callers) are an esse

56 edictions remains challenging: even the best gene prediction algorithms make substantial errors and c

57 Computational gene prediction algorithms were used to query the sequen

58 time and has provided the basis for powerful gene prediction algorithms, its origins are still not fu

59 Despite advances in computational gene prediction algorithms, most eukaryotic genomes stil

60 gene boundaries from three different protein gene prediction algorithms, tRNAscan-SE gene predictions

61 iption units and new genes not identified by gene prediction algorithms.

62 e to detect novel exons not predicted by any gene prediction algorithms.

63 s, which had been undetected by conventional gene-prediction algorithms, are identified by the codon-

64 ase homology searches, and computer-assisted gene prediction analyses.

65 e leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area

66 lysis of microarrays) and minimal subsets of genes (prediction analysis for microarrays) that succinc

67 order to achieve fast and reliable automatic gene prediction and annotation.

68 We have combined computational gene prediction and comparative sequence analysis to cha

69 mber of bioinformatics tools that facilitate gene prediction and cross- species comparisons.

70 for genome annotation, structural genomics, gene prediction and domain-based genomic studies.

71 By systematic defense gene prediction and heterologous reconstitution, here we

72 petitive elements is a key step for accurate gene prediction and overall structural annotation of gen

73 Target gene prediction and pathway mapping were performed using

74 gene prediction improves substantially when gene prediction and pseudogene masking are interleaved.

75 First, 16S rRNA gene prediction and the inclusion of ultrafast exact ali

76 The gene predictions and accompanying functional assignments

77 Public access to our RNA gene predictions and an interface for user predictions i

78 pproach to validate and refine computational gene predictions and define full-length transcripts on t

79 Target gene predictions and experimental verification indicate

80 gastric niche and demonstrate that in silico gene predictions and in vitro tests have limitations for

81 The resulting report identifies problematic gene predictions and includes extensive statistics and g

82 (OPTIC) database currently provides sets of gene predictions and orthology assignments for three cla

83 embly in the context of externally available gene predictions and other features.

84 g expression-based validation for 84% of the gene predictions and providing clues as to the functions

85 r (GV) to automatically identify problematic gene predictions and to aid manual curation.

86 oducts from five DEFL clusters confirmed our gene predictions and verified expression.

87 the region containing KRIT1/Krit1 using exon/gene-prediction and comparative alignment programs revea

88 tion to protein function annotation, disease gene prediction, and drug discovery.

89 and genetic maps, genome sequence assembly, gene prediction, and integration of EST data.

90 Combining RT-PCR, in silico gene prediction, and paralogy analysis, we can identify

91 EST alignments, ab initio gene prediction, and sequence similarity searches of the

92 tially sequenced genome, mostly by in silico gene prediction, and there has been no major improvement

93 and proteins to a genome, produces ab initio gene predictions, and automatically synthesizes these da

94 uction, protein-based anchoring of ab-initio gene predictions, and constraints derived from a global

95 ene annotations extended by similarity-based gene predictions, and identifying and excluding paralogs

96 d PPFINDER to remove pseudogenes from N-SCAN gene predictions, and show that gene prediction improves

97 Our gene prediction approach prioritized druggable genes tha

98 Correct gene predictions are crucial for most analyses of genome

99 Problematic gene predictions are flagged and can be reannotated usin

100 Consensus gene predictions are then derived by maximum likelihood

101 on of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al.

102 In addition, we used PPFINDER with gene predictions as a parent database, eliminating the n

103 featuring transcript alignments to validate gene predictions as well as motif and similarity analyse

104 s' (TOGA) software with de novo and homology gene predictions as well as short- and long-read transcr

105 a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data

106 SMCRFs are a promising framework for gene prediction because of their highly modular nature,

107 ur models within each cluster on the initial gene predictions before making final predictions.

108 on and homology analyses to produce reliable gene predictions but they often fail to detect many actu

109 Our approach was to produce gene predictions by algorithms that rely on comparative

110 ture of the method is the ability to enhance gene predictions by finding the best alignment between t

111 ral, these results showed that computational gene prediction can be a reliable tool for annotating ne

112 y insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short r

113 le cDNA and expressed sequence tag data with gene predictions, clarifying single nucleotide polymorph

114 w GC content can make both better and unique gene predictions compared to gene prediction programs th

115 ows that Seqping was able to generate better gene predictions compared to three HMM-based programs (M

116 encing in combination with RACE on ab initio gene predictions could be used to define the transcripto

117 t the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identi

118 expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucle

119 isive for discriminating between alternative gene predictions derived from computational sequence ins

120 Ab initio gene prediction enables gene annotation of new genomes r

121 expression profiles indicate that 5% of the gene predictions encode mRNAs that are found only in the

122 Our results show that combining gene prediction evidence consistently outperforms even t

123 VIGOR produces four output files: a gene prediction file, a complementary DNA file, an align

124 l Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an aut

125 We are able to rank gene predictions for a significant portion of the diseas

126 The gene predictions for human can be browsed and downloaded

127 trees of species and comparisons of several gene predictions for sensitivity and specificity in find

128 The gene predictions for these viruses have been evaluated b

129 pecifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a met

130 nput a genomic sequence and the locations of gene predictions from ab initio gene finders, protein se

131 igation indicates that many of the incorrect gene predictions from GeneWise were due to transposons w

132 f Takifugu Toll-like receptor (TLR) loci and gene predictions from many draft genomes enable comprehe

133 e a Bayesian network framework for combining gene predictions from multiple systems.

134 ouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA.

135 iocurators and researchers who need accurate gene predictions from newly sequenced genomes.

136 ents and multi-genome alignments, as well as gene predictions from other gene-finders.

137 a sets, the inclusion of genome sequence and gene predictions from related species and active literat

138 and eight other eukaryote model genomes, and gene predictions from several groups.

139 s, Exegesis was used to process the original gene predictions from the automated Ensembl annotation p

140 steps in microbial genome analysis-assembly, gene prediction, functional annotation-in a way that all

141 f solution exists for the combined assembly, gene prediction, genome annotation and data presentation

142 atter of debate; the number of computational gene predictions greatly exceeds the number of validated

143 Since the release of WS9, gene predictions have improved continuously.

144 9B, and CrSUMO-like90) were found by diverse gene predictions, hidden Markov models, and database sea

145 We report a strategy of focused candidate gene prediction, high-throughput sequencing and experime

146 We present 'gene prediction improvement pipeline' (GenePRIMP), a com

147 from N-SCAN gene predictions, and show that gene prediction improves substantially when gene predict

148 Accurate ab initio gene prediction in a short nucleotide sequence of anonym

149 at this protocol will also be beneficial for gene prediction in any organism with bimodal or other un

150 U, has been developed for plant miRNA target gene prediction in any plant, if a large number of seque

151 We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and Glimm

152 n of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform

153 We show that TWINSCAN improves gene prediction in human using intermediate products fro

154 Gene prediction in metagenomic sequences remains a diffi

155 d for miRNA and precursor mining, and target gene prediction in non-model plants.

156 halum using several different approaches for gene prediction in organisms with insertional RNA editin

157 rk family of programs designed and tuned for gene prediction in prokaryotic, eukaryotic and viral gen

158 We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the ca

159 ent program to allow for rapid alignment and gene prediction in user submitted sequences.

160 this activity, we identified several hundred gene predictions in mouse with varying levels of support

161 Existing computational methods for gene prediction include ab initio methods which use the

162 Gene prediction is a ubiquitous step in sequence analysi

163 Correct gene prediction is impaired by the presence of processed

164 Gene prediction is one of the most important steps in th

165 However, in the absence of transcript data, gene prediction is still challenging.

166 We have developed a novel gene prediction method FragGeneScan, which combines sequ

167 pathogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from sever

168 The new gene prediction method, called GeneMarkS, utilizes a non

169 evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns

170 rences in the genomic sequences or different gene prediction methodologies, we analyzed both genome a

171 the assemblies and especially the different gene-prediction methodologies.

172 se-negative rate, lower than most of current gene prediction methods and a false-positive rate lower

173 nome analysis and the limitations of current gene prediction methods and understanding.

174 Computational gene prediction methods are an important component of wh

175 most of the machine learning based essential gene prediction methods are lack of skills to handle the

176 f MRIII was analysed using the computational gene prediction methods NIX and TAP to identify putative

177 Thus, gene prediction methods that explicitly take into accoun

178 he genome sequence was analyzed with several gene prediction methods to produce a comprehensive gene

179 tion framework that can be applied to driver gene prediction methods.

180 n be used in combination with other existing gene-prediction methods.

181 rom healthy human brain to develop a disease gene prediction model and this generic methodology can b

182 Gene prediction modeling identified 34,809 genes, with m

183 Gene predictions, mRNA alignments, epigenomic data from

184 egrates assembly data, comparative genomics, gene predictions, mRNA and EST alignments and physiologi

185 mosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparati

186 sembly data, sequence composition, genes and gene predictions, mRNA and expressed sequence tag eviden

187 ave made another approximately 10,000-20,000 gene predictions of lower confidence, supported by vario

188 We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimme

189 The existing methods for enhancer-target gene prediction often require many genomic features.

190 We investigated the accuracy of gene prediction on different levels and designed several

191 Gene predictions on the assembled sequence suggest that

192 erent workers, from a bioinformatician doing gene prediction or a bench scientist designing primers f

193 en made to integrate pseudogene removal with gene prediction, or even to provide a freestanding tool

194 ave been integrated into WormBase, including gene predictions, ortholog assignments and a new synteny

195 y improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding

196 bled' RNA-Seq reads improves the accuracy of gene prediction; particularly, for the 1.3 GB genome of

197 on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain det

198 According to evaluations of candidate gene prediction performance tested under four different

199 ra) produced using BRAKER2 semi-unsupervised gene prediction pipeline and additional tools.

200 We present an automated gene prediction pipeline, Seqping that uses self-trainin

201 that integration of such information into a gene-prediction pipeline is feasible and doing so may im

202 tive analysis tools, such as genes missed by gene prediction pipelines or genes without an associated

203 This allows us to run the gene prediction/PPFINDER procedure on newly sequenced ge

204 ced learning issue inherent in the essential gene prediction problem, which might be one factor affec

205 ncorporation of additional evidence into the gene prediction process, we show how it can be used to b

206 s with the syntenic human locus at 13q22 and gene prediction program analysis, we found a single clus

207 The output of any external gene prediction program can be easily converted to a gen

208 The Ensembl gene prediction program has been used for the complete g

209 t MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify gene

210 BLAST searches using a gene prediction program revealed that 86% of TNP flankin

211 We present here a homology-based gene prediction program to accurately predict small pept

212 A gene prediction program, VIGOR (Viral Genome ORF Reader)

213 ould be obtained by creating a more accurate gene-prediction program and then amplifying and sequenci

214 Annotation tools such as optimized gene prediction programs are being developed for rice to

215 Training data for gene prediction programs are often chosen randomly from

216 When gene prediction programs are trained on a subset of gras

217 Gene prediction programs frequently mistake processed ps

218 hich allows users to query multiple exon and gene prediction programs in an automated fashion.

219 ne pipeline to several widely used ab initio gene prediction programs in rice; this comparison shows

220 The main challenge in combination of gene prediction programs is the fact that the systems ar

221 Gene prediction programs offer an efficient way to gener

222 better performance than all general-purpose gene prediction programs surveyed.

223 the lowest false-positive rate of the coding gene prediction programs tested, and Infernal has a low

224 tter and unique gene predictions compared to gene prediction programs that are trained on genes with

225 We show that gene prediction programs that are trained with grass gen

226 We find that gene prediction programs trained on grass genes with ran

227 By separately training gene prediction programs with genes from multiple GC ran

228 sed or assigned incorrect start positions by gene prediction programs, and suggest corrections to imp

229 were identified by homology searches and by gene prediction programs, and their gene structures and

230 tilizing homology to improve the accuracy of gene prediction programs.

231 lly but not completely with predictions from gene prediction programs.

232 combining predictions from already existing gene prediction programs.

233 plice groups, which can be incorporated into gene prediction programs.

234 tructural annotation depends on well-trained gene prediction programs.

235 In addition, RGD provides tools for gene prediction, radiation hybrid mapping, polymorphic m

236 mapped to all organisms, RefSeq alignments, gene predictions, regulatory elements, gene expression d

237 Existing methods for ncRNA gene prediction rely mostly on homology information, thu

238 However, obtaining accurate gene predictions remains challenging: even the best gene

239 imilarity, RNA sequencing [RNA-Seq] results, gene predictions, repeats) and necessitates many volunte

240 karyote genomes that facilitate assembly and gene prediction, resulting in thousands of complete geno

241 In this Perspective, I review the state of gene prediction roughly 10 years ago, summarize the prog

242 The increased gene prediction sensitivity results in part from novel s

243 species Schmidtea mediterranea, we provide a gene prediction set that now assign existing transcripts

244 an assembly, hg38/GRCh38, to include updated gene prediction sets from GENCODE, more phenotype- and d

245 compared with the introns of other ab initio gene prediction sets.

246 tively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply thi

247 Most current gene prediction software, whether based on ab initio or

248 roach is developed that largely improves the gene prediction specificity.

249 e the supervised model training precedes the gene prediction step.

250 a widely used phenotype database in disease gene prediction studies.

251 teractions does not markedly improve disease gene predictions, suggesting that many of the disease ge

252 In this work, we developed a metagenomics gene prediction system Glimmer-MG that achieves signific

253 Our approach was to adapt the TWINSCAN gene prediction system to C. elegans and C. briggsae and

254 tical, and is suitable for use in a combined gene prediction system where other methods identify well

255 TWINSCAN is a gene-prediction system that combines the methods of sing

256 and implemented them in the TWINSCAN/N-SCAN gene-prediction system.

257 as accelerated, the need for highly accurate gene prediction systems has grown.

258 orporating multiple sources of evidence into gene prediction systems.

259 However, popular similarity search tools and gene prediction techniques generally fail to identify mo

260 The steady improvements in gene prediction that have occurred over the last 10 year

261 predicted by comparative mapping, indicated gene predictions that are likely to be incorrect, and id

262 Exegesis is a procedure to refine the gene predictions that are produced for complex genomes,

263 used to validate novel UTR introns in human gene predictions that do not overlap any RefSeq gene and

264 We have assembled a collection of >10,000 gene predictions that do not overlap existing gene annot

265 of the human genome sequence, with confirmed gene predictions that have been integrated with external

266 to predict new genes, including two GenScan gene predictions that overlapped ESTs and were shown to

267 thin this region, which contains 255 Ensembl gene predictions, the aligned sequences clustered into 5

268 ith known model organism gene homologies and gene predictions to provided basic comparative data.

269 GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into

270 ew hidden Markov model (HMM)-based ab initio gene prediction tool, which is optimized for finding gen

271 There exist a number of ab initio gene prediction tools and they have been widely used for

272 While gene prediction tools have similar accuracies predicting

273 ers" in proteomics, improves on the existing gene prediction tools in genomics, and allows identifica

274 Computational gene prediction tools routinely generate large volumes o

275 in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are

276 which is not carefully modeled by available gene prediction tools.

277 addition to the narrow arsenal of automatic gene prediction tools.

278 Some of the best methods for gene prediction use either sequence composition analysis

279 tational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and re

280 ated genome structure makes it difficult for gene prediction using the currently available gene annot

281 nd Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1

282 pipeline predictions are more accurate than gene predictions using the other three approaches with t

283 ost computational methodologies for microRNA gene prediction utilize techniques based on sequence con

284 Insertional RNA editing renders gene prediction very difficult compared to organisms wit

285 Among 23,021 gene predictions we identified 0.2% strong candidates fo

286 that may not satisfy existing heuristics for gene prediction, we developed a computational and experi

287 Using de novo gene prediction, we identified 6,996 protein-encoding ge

288 Applying this method to the human Ensembl gene predictions, we discovered that 2011 (9% of total)

289 In testing experimentally unsupported gene predictions, we were able to identify 58 that are n

290 Gene predictions were 88-95% in agreement with the avail

291 Ab initio gene predictions were selected for high-throughput valid

292 61 (46%) overlapped 184 (72%) of the Ensembl gene predictions, whereas 307 were unique to Incyte.

293 Our studies combining computational gene prediction with genetic and comparative genomic ana

294 ogress, there is a growing need to integrate gene prediction with metabolic network analysis.

295 The suggested method of parallelization of gene prediction with the model parameters estimation fol

296 This program provides rRNA gene predictions with high sensitivity and specificity o

297 has been established to validate novel human gene predictions with no prior experimental evidence of

298 tein gene prediction algorithms, tRNAscan-SE gene predictions with RNA secondary structures and CRISP

299 ession profiles of sequences found in 35,282 gene predictions within the sea urchin genome.

300 on of the same genome, derived from distinct gene prediction workflows.