ライフサイエンスコーパス: gene annotation

1 R-RTs is critical for achieving high-quality gene annotation.

2 he training set based only on prior computer gene annotation.

3 isoforms from 44 968 gene models and updated gene annotation.

4 how proteogenomics can substantially improve gene annotation.

5 rams, and suggest corrections to improve the gene annotation.

6 ing and non-coding transcripts, facilitating gene annotation.

7 ow that functional modules can be useful for gene annotation.

8 genomes generated using multiple sources of gene annotation.

9 genes based on the recently released tomato gene annotation.

10 and to develop tools to predict 3'-ends for gene annotation.

11 s heavily on the completeness and quality of gene annotation.

12 ntial improvement of the currently available gene annotation.

13 ne finding to generate accurate and complete gene annotation.

14 ed evidence codes, phenotype ontologies, and gene annotation.

15 mputational analysis of mRNA-ends to improve gene annotation.

16 works, offering opportunities for regulatory gene annotation.

17 dance of genome alignment and independent of gene annotation.

18 nctions is a powerful approach of functional gene annotation.

19 including the alignments of the hits to the gene annotation.

20 en clustered and filtered using the optional gene annotation.

21 level will substantially advance Arabidopsis gene annotation.

22 f enrichment in sequencing reads relative to gene annotations.

23 gside foundation datasets, such as reference gene annotations.

24 ty that can be applied to assemblies lacking gene annotations.

25 aromyces pombe, independently from available gene annotations.

26 of processed pseudogene finding in mammalian gene annotations.

27 redictive accuracy analysis through verified gene annotations.

28 ression sequences to improve the accuracy of gene annotations.

29 ions greatly exceeds the number of validated gene annotations.

30 predicted ORFs that did not overlap WormBase gene annotations.

31 d to determine biological relevance from the gene annotations.

32 to provide direct experimental validation of gene annotations.

33 results of automated updates to Arabidopsis gene annotations.

34 s data visualization and curation of current gene annotations.

35 uilding phylogenies to predicting functional gene annotations.

36 n junctions with junctions in several recent gene annotations.

37 provements in sequence matching will improve gene annotations.

38 s, and created an additional 2,522 noncoding gene annotations.

39 a whole-community profile down to individual gene annotations.

40 ur approach does not depend on transcript or gene annotations.

41 to iteratively refine and improve structural gene annotations across multiple Aspergillus species, an

42 Functional gene annotation analysis indicated predominant effects o

43 n addition, ANISEED provides full functional gene annotation, anatomical ontologies and some gene exp

44 pped reads, balanced utilization of existing gene annotation and a reduced false discovery rate for s

45 accurate matching to reference sequences for gene annotation and allow in-depth analysis of sequence

46 ted, and combines the traditionally distinct gene annotation and alternative splicing detection proce

47 ble resource for knowledge representation in gene annotation and analysis in the areas of immunology

48 ing pipelines produce consistent protein and gene annotation and capture sequence descriptors from se

49 expression datasets to perform comprehensive gene annotation and differential expression analysis.

50 (i) visualizing gene annotation and DNA sequence data from a GenBank fla

51 the 3' end of genes, which is important for gene annotation and elucidating gene regulation mechanis

52 ge patterns with genomic information such as gene annotation and evolutionary conservation.

53 The DIG system collects and organizes gene annotation and functional information, and includes

54 a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating t

55 Furthermore, in some cases, gene annotation and in aqua assays disagree by describin

56 analysis of genome structure and preliminary gene annotation and interpretation.

57 work has been focused on sequence assembly, gene annotation and metabolic network reconstruction.

58 m the fact that our current knowledge of the gene annotation and of the ontology structure is incompl

59 n protein structure and function prediction, gene annotation and phylogenetic tree construction.

60 GeneSense server was developed to integrate gene annotation and PPI networks in an expandable archit

61 ch demonstrates broad utility for eukaryotic gene annotation and suggests an intriguing new decomposi

62 omics) designed to facilitate the process of gene annotation and the discovery of functional context.

63 sessment of the completeness and accuracy of gene annotation and thus allows computational identifica

64 curacy in many applications, such as de novo gene annotation and transcript quantification.

65 At the gene level users can view the gene annotation and underlying evidence.

66 pecies include comprehensive, evidence-based gene annotations and a selected set of genomes includes

67 chin genome, associated expressed sequences, gene annotations and accessory resources.

68 These results greatly enhance sorghum gene annotations and aid in studying gene regulation in

69 imental datasets in consistency, recovery of gene annotations and enrichment for disease-associated v

70 ene predictions that do not overlap existing gene annotations and have developed a process for their

71 Functional protein association networks, gene annotations and localization of identified proteins

72 ERGR provides gene annotations and orthologs, detailed gene study info

73 lization of these occurrences with regard to gene annotations and other families of transposable elem

74 this capability has the potential to improve gene annotations and our understanding of the regulation

75 Over 10,000 gene annotations and phenotype descriptions from partici

76 algorithms that are not restricted by prior gene annotations and that account for alternative transc

77 In order to validate gene annotations and to identify pseudogenes that are po

78 According to genes annotation and functional prediction, such as Wnt1

79 embly and gene model set, refined functional gene annotation, and anatomical ontologies, and a new co

80 scuss progress in Dictyostelium genomics and gene annotation, and highlight the primary portals for s

81 an independent measure of the efficiency of gene annotation, and indicates that this analysis may ac

82 rse MHC class II region with rigorous manual gene annotation, and it will serve as an important resou

83 Our model does not rely on existing gene annotations, and model selection is performed autom

84 ding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures,

85 es of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic

86 Databases collecting drug targets and gene annotations are growing and can be harnessed to she

87 down to examine specific matrix entries, and gene annotations are linked to relevant genomic database

88 Two zebrafish gene annotations are presented in Ensembl version 62 bui

89 Search mechanisms for the sequences and the gene annotations are provided.

90 and variable quality of evidence relevant to gene annotation argues for a probabilistic framework tha

91 of rapidly evolving genes, probably owing to gene annotation artifacts.

92 tations in the current sequence assembly and gene annotation, as well as approaches to address these

93 raw RNA-seq reads without prior knowledge of gene annotations, as well as for determining the dominan

94 es, expression analysis for microRNAs, basic gene annotation, batch analysis and linking between mous

95 e approach that reliably transfers essential gene annotations between distantly related bacteria.

96 quence similarity to improve the transfer of gene annotations between organisms.

97 Interestingly, semantic similarity between gene annotations (Biological Process) is much better ass

98 T utilizes a database that is preloaded with gene annotation, BLAST hit results, and gene-clustering

99 enic regions using Ensembl, UCSC and AceView gene annotations but they were not annotated into corres

100 Gene annotation by ab initio prediction supported by RNA

101 The accuracy of gene annotation by this database was largely confirmed b

102 Interacting PROTEINS: Furthermore, suitable gene annotations can also be captured.

103 ral schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annota

104 same genes, as indicated in a compendium of gene annotation data from numerous different sources.

105 SIM works well even for genomes with sparser gene annotation data.

106 ess (OA) subset of PubMed Central (PMC) as a gene annotation database and have developed an R package

107 roach can be easily generalized to any other gene annotation database.

108 Gene annotation databases (compendiums maintained by the

109 Several functional gene annotation databases have been developed in the rec

110 analysis of gene ontology from fly or human gene annotation databases points to four significant com

111 onal inference for non-coding elements using gene annotation databases requires a special correction.

112 ction that is used in many organism-specific gene annotation databases.

113 method and web-based tool, with 16 available gene annotation databases.

114 Current gene annotation efforts focus on centralized curation re

115 A gene annotation enrichment analysis using DAVID showed t

116 The gene-annotation enrichment analysis is a promising high-

117 e ongoing efforts to increase the quality of gene annotations, especially transcriptional start sites

118 Large-scale expression data, functional gene annotations, experimental protein-protein interacti

119 Our findings provide additional candidate-gene annotation for 37 disease susceptibility loci for h

120 s probably reflects the lesser refinement of gene annotation for chimpanzees.

121 To meet the needs of gene annotation for newly sequenced organisms, optimized

122 expression to tissue level, the accuracy of gene annotation for the nonspecific SAGE tags should be

123 We provide complete gene annotations for all supported species in addition t

124 users to automatically extract sequences and gene annotations for any recorded locus.

125 It combines gene annotations from GenBank files and other sources wi

126 The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset and

127 Gene annotation further revealed four genes with functio

128 tools that enable flexible queries based on gene annotation, gene family, synteny and relative gene

129 By bringing together existing gene annotations, gene expression data, multiple-genome

130 The zebrafish gene annotation has been enhanced by the incorporation o

131 Computational methods for structural gene annotation have propelled gene discovery but face c

132 nd automatically synthesizes these data into gene annotations having evidence-based quality indices.

133 based transcriptome profiling to structural gene annotation helped correct existing annotation error

134 le data consist of structural and functional gene annotations, homologous gene families, multiple seq

135 nction that is used as a common language for gene annotation in many organisms.

136 catula, and the difficulties associated with gene annotation in plant secondary metabolism.

137 gain-of-function mutagenesis for functional gene annotation in vertebrate models, including zebrafis

138 Gene annotation in viruses often relies upon similarity

139 lows one to build an initial set of reliable gene annotations in potentially any eukaryotic genome, e

140 oach), for building a highly reliable set of gene annotations in the absence of experimental data.

141 The Proteome Browser also provides links to gene annotations in the Genome Browser, the Known Genes

142 re able to support or correct more than 6000 gene annotations including 80 novel gene structures and

143 mic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis

144 ng with Noise set (DWCN), which makes use of gene annotation information and allows for a set of scat

145 he functional similarities of genes based on gene annotation information from heterogeneous data sour

146 Using currently available genome data and gene annotation information, we systematically examined

147 onjunction with BLAST searches and available gene annotation information.

148 Comprehensive gene annotation is an essential aspect of genomic and pr

149 Prokaryote gene annotation is complicated by large numbers of short

150 Since gene annotation is incomplete for even the best studied

151 ext based methods using gene names; however, gene annotation is neither complete, nor fully systemati

152 Gene annotation is the final goal of gene prediction alg

153 with four newly sequenced genomes (where the gene annotation is unavailable), we show that the gene p

154 ete and accurate set of human protein-coding gene annotations is perhaps the single most important re

155 es, ORFans are not attributable to errors in gene annotation, limitations of current databases, or to

156 cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation

157 of the human and mouse genomes and improved gene annotation methods.

158 general mass spectrometry-based approach for gene annotation of any organism and demonstrate its effe

159 Here we report the finished sequence and gene annotation of human chromosome 18, which will allow

160 We report a second-generation gene annotation of human chromosome 22.

161 Gene annotation of the identified proteins was corrected

162 We have generated an improved assembly and gene annotation of the pig X Chromosome, and a first dra

163 Here we present the genome and gene annotations of two such free-living Bradyrhizobium

164 ineages share a large set of nonhousekeeping genes, annotation of lineage-restricted genes shows that

165 ing-array experiments agree with established gene annotation on human chromosome 22.

166 d and cost-effective way to provide reliable gene annotations on newly sequenced genomes.

167 ntropy within a selected region versus using gene annotation or known promoters as positives for tran

168 We did gene annotation, pathway, and gene-set-enrichment analys

169 e a previously unexplored HAD family member (gene annotation, phosphoglycolate phosphatase), which we

170 and a previously proposed hybrid functional gene annotation pipeline, we developed an online pipelin

171 r plant biology related pathways, KEGG based gene annotation pointed out active presence of an array

172 BioGPS is a centralized gene-annotation portal that enables researchers to acces

173 l, variance stabilization, normalization and gene annotation portions.

174 identification is an important aspect of the gene annotation process, requisite for the accurate deli

175 Despite advances in the gene annotation process, the functions of a large portio

176 Evigan is an automated gene annotation program for eukaryotic genomes, employin

177 Ensembl gene annotation provides a comprehensive catalog of tran

178 en the abundance of new genomic projects and gene annotations, researchers trying to pinpoint causal

179 Many gene annotation resources and software platforms for ORA

180 dreds, possibly even thousands, of web-based gene annotation resources available, but it quickly beco

181 terature, gene transcriptional profiles, and gene annotation resources support our prediction.

182 ized gene portal for aggregating distributed gene annotation resources, emphasizing community extensi

183 at enables researchers to access distributed gene annotation resources.

184 evolutionary signatures to evaluate existing gene annotations, resulting in the validation of 87% of

185 to genome-wide RNA interference data and to gene annotations revealed distinguishable levels of expr

186 that the Open World Assumption as applied to gene annotations rules out many traditional validation m

187 e sequence from which a core human reference gene annotation set can be derived.

188 experience gained in generating the GENCODE gene annotation set.

189 emblies, including bonobo and zebrafish; new gene annotation sets; improvements to track and assembly

190 teins hampers their prediction with standard gene annotation software.

191 Gene annotations, such as those in GENCODE, are derived

192 algorithms are part of the Ensembl automatic gene annotation system, and its results, using ESTs, are

193 ve response, Cr1 We describe new markers and gene annotation that are both tightly linked to Cr1 in a

194 rsede the limited and static forms of single-gene annotation that are now the norm.

195 ,686 were sufficiently distant from existing gene annotations to be considered a novel conserved unit

196 suite of tools seamlessly integrates a novel gene annotation tool, known as GOby, which identifies st

197 Gene annotation underpins genome science.

198 ) expanded support for genome sequence; (iv) gene annotation using the standardized vocabulary of Gen

199 Users can query the gene annotations using simple and powerful full text sea

200 Except for the latter, monocistronic gene annotation was expanded using the above criteria al

201 e semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure

202 Analogous to the use of RNA-seq for gene annotation, we propose a new method for de novo TE

203 sufficient for full antimicrobial resistance gene annotation were obtained with as few as 2,000 to 5,

204 Gene annotations were updated using 111,000 full-length

205 nces, transcript fragmentation and incorrect gene annotation, which we suggest that de novo assembly

206 on and in aqua assays disagree by describing gene annotation without enzyme activity and enzyme activ