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- Imanishi, T., et al.
(författare)
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Integrative annotation of 21,037 human genes validated by full-length cDNA clones
- 2004
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Ingår i: PLoS biology. - : Public Library of Science (PLoS). - 1544-9173 .- 1545-7885. ; 2:6, s. 856-875
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Tidskriftsartikel (refereegranskat)abstract
- The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.
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| 3. |
- Carninci, P, et al.
(författare)
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The transcriptional landscape of the mammalian genome
- 2005
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Ingår i: Science (New York, N.Y.). - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 309:5740, s. 1559-1563
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Tidskriftsartikel (refereegranskat)abstract
- This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5′ and 3′ boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
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| 9. |
- Andersson, T., et al.
(författare)
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Monitoring of representational difference analysis subtraction procedures by global microarrays
- 2002
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Ingår i: BioTechniques. - : Future Science Ltd. - 0736-6205 .- 1940-9818. ; 32:6, s. 1348-
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Tidskriftsartikel (refereegranskat)abstract
- Various approaches to the study of differential gene expression are applied to compare cell lines and tissue samples in a wide range of biological contexts. The compromise between focusing on only the important genes in certain cellular processes and achieving a complete picture is critical for the selection of strategy. We demonstrate how global microarray technology can be used for the exploration of the differentially expressed genes extracted through representational difference analysis (RDA). The subtraction of ubiquitous gene fragments from the two samples was demonstrated using cDNA microarrays including more than 32 000 spotted, PCR-amplified human clones. Hybridizations indicated the expression of 9100 of the microarray elements in a macrophage/foam cell atherosclerosis model system, of which many were removed during the RDA process. The stepwise subtraction procedure was demonstrated to yield an efficient enrichment of gene fragments overrepresented in either sample (18% in the representations, 86% after the first subtraction, and 88% after the second subtraction), many of which were impossible to detect in the starting material. Interestingly, the method allowed for the observation of the differential expression of several members of the low-abundant nuclear receptor gene family. We also observed a certain background level in the difference products of nondifferentially expressed gene fragments, warranting a verification strategy for selected candidate genes. The differential expression of several genes was verified by real-time PCR.
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| 10. |
- Burkholz, R., et al.
(författare)
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Using graph convolutional neural networks to learn a representation for glycans
- 2021
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 35:11
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Tidskriftsartikel (refereegranskat)abstract
- As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes—from protein folding to viral cell entry—yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we use glycan-focused machine learning to predict viral glycan binding, which can be used to discover viral receptors. © 2021 The Author(s)
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