Investigation of protein functions through data-mining on integrated human transcriptome database, H-Invitational database (H-InvDB)

Yamasaki, Chisato; Koyanagi, Kanako O.; Fujii, Yasuyuki; Itoh, Takeshi; Barrero, Roberto A.; Tamura, Takuro; Yamaguchi-Kabata, Yumi; Tanino, Motohiko; Takeda, Jun; Fukuchi, Satoshi; Miyazaki, Satoru; Nomura, Nobuo; Sugano, Sumio; Imanishi, Tadashi; Gojobori, Takashi

doi:10.1016/j.gene.2005.05.036

Cited by 17 publications

(17 citation statements)

References 35 publications

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“…The data of human gene structure were obtained from H-InvDB ver3.8 (http://www.h-invitational.jp/), created by the annotation project of human genes (H-Invitational project) [23], [45]. Our analysis of all human genes that corresponds to H-InvDB (ver 3.8) predicted 36,712 protein coding loci.…”

Section: Methodsmentioning

confidence: 99%

Distribution and Effects of Nonsense Polymorphisms in Human Genes

et al. 2008

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BackgroundA great amount of data has been accumulated on genetic variations in the human genome, but we still do not know much about how the genetic variations affect gene function. In particular, little is known about the distribution of nonsense polymorphisms in human genes despite their drastic effects on gene products.Methodology/Principal FindingsTo detect polymorphisms affecting gene function, we analyzed all publicly available polymorphisms in a database for single nucleotide polymorphisms (dbSNP build 125) located in the exons of 36,712 known and predicted protein-coding genes that were defined in an annotation project of all human genes and transcripts (H-InvDB ver3.8). We found a total of 252,555 single nucleotide polymorphisms (SNPs) and 8,479 insertion and deletions in the representative transcripts in these genes. The SNPs located in ORFs include 40,484 synonymous and 53,754 nonsynonymous SNPs, and 1,258 SNPs that were predicted to be nonsense SNPs or read-through SNPs. We estimated the density of nonsense SNPs to be 0.85×10−3 per site, which is lower than that of nonsynonymous SNPs (2.1×10−3 per site). On average, nonsense SNPs were located 250 codons upstream of the original termination codon, with the substitution occurring most frequently at the first codon position. Of the nonsense SNPs, 581 were predicted to cause nonsense-mediated decay (NMD) of transcripts that would prevent translation. We found that nonsense SNPs causing NMD were more common in genes involving kinase activity and transport. The remaining 602 nonsense SNPs are predicted to produce truncated polypeptides, with an average truncation of 75 amino acids. In addition, 110 read-through SNPs at termination codons were detected.Conclusion/SignificanceOur comprehensive exploration of nonsense polymorphisms showed that nonsense SNPs exist at a lower density than nonsynonymous SNPs, suggesting that nonsense mutations have more severe effects than amino acid changes. The correspondence of nonsense SNPs to known pathological variants suggests that phenotypic effects of nonsense SNPs have been reported for only a small fraction of nonsense SNPs, and that nonsense SNPs causing NMD are more likely to be involved in phenotypic variations. These nonsense SNPs may include pathological variants that have not yet been reported. These data are available from Transcript View of H-InvDB and VarySysDB (http://h-invitational.jp/varygene/).

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Section: Methodsmentioning

confidence: 99%

Distribution and Effects of Nonsense Polymorphisms in Human Genes

et al. 2008

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“…5.0 [53], miRBase v10.0 [66], NONCODE v1.0 [54], Rfam v8.1 [48], RNAdb v2.0 [51], snoRNA-LBME-db rel. 3 [67], and Gene Expression Omnibus (GEO) [68], another Japanese group generated a platform for mining/annotating functional RNA candidates from non-coding RNA sequences, that they called fRNAdb (http://www.ncrna.org/frnadb) [57].…”

Section: Functional Analysis Of Long Non-coding Rnas (Lncrnas)mentioning

confidence: 99%

Bioinformatics Tools and Novel Challenges in Long Non-Coding RNAs (lncRNAs) Functional Analysis

Sacco¹,

Baldassarre²,

Masotti³

2011

IJMS

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The advent of next generation sequencing revealed that a fraction of transcribed RNAs (short and long RNAs) is non-coding. Long non-coding RNAs (lncRNAs) have a crucial role in regulating gene expression and in epigenetics (chromatin and histones remodeling). LncRNAs may have different roles: gene activators (signaling), repressors (decoy), cis and trans gene expression regulators (guides) and chromatin modificators (scaffolds) without the need to be mutually exclusive. LncRNAs are also implicated in a number of diseases. The huge amount of inhomogeneous data produced so far poses several bioinformatics challenges spanning from the simple annotation to the more complex functional annotation. In this review, we report and discuss several bioinformatics resources freely available and dealing with the study of lncRNAs. To our knowledge, this is the first review summarizing all the available bioinformatics resources on lncRNAs appeared in the literature after the completion of the human genome project. Therefore, the aim of this review is to provide a little guide for biologists and bioinformaticians looking for dedicated resources, public repositories and other tools for lncRNAs functional analysis.

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“…From early estimates in excess of 80,000 protein coding genes in the human genome based on quantitative measurements of CpG islands [71], estimates have dropped repeatedly, to 28-34,000 based on exon comparison between the pufferfish Tetraodon nigroviridis and human genomes [72], to 21,037 based on large scale cDNA library sequencing [73, 74], to 20,488 based on comparison of proposed human genes to sequences of other primate genomes [75]. Currently RefSeq, a widely used NCBI library of gene annotations, includes 21,515 unique entries.…”

Section: How Many Genes Are There?mentioning

confidence: 99%

Saturation of the Human Phenome

Samuels

2010

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Abstract:The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits.

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Investigation of protein functions through data-mining on integrated human transcriptome database, H-Invitational database (H-InvDB)

Cited by 17 publications

References 35 publications

Distribution and Effects of Nonsense Polymorphisms in Human Genes

Distribution and Effects of Nonsense Polymorphisms in Human Genes

Bioinformatics Tools and Novel Challenges in Long Non-Coding RNAs (lncRNAs) Functional Analysis

Saturation of the Human Phenome

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