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Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses.

ABSTRACT: Human gene annotation is crucial for conducting transcriptomic and genetic studies; however, the impacts of human gene annotations in diverse databases on related studies have been less evaluated. To enable full use of various human annotation resources and better understand the human transcriptome, here we systematically compare the human annotations present in RefSeq, Ensembl (GENCODE), and AceView on diverse transcriptomic and genetic analyses. We found that the human gene annotations in the three databases are far from complete. Although Ensembl and AceView annotated more genes than RefSeq, more than 15,800 genes from Ensembl (or AceView) are within the intergenic and intronic regions of AceView (or Ensembl) annotation. The human transcriptome annotations in RefSeq, Ensembl, and AceView had distinct effects on short-read mapping, gene and isoform expression profiling, and differential expression calling. Furthermore, our findings indicate that the integrated annotation of these databases can obtain a more complete gene set and significantly enhance those transcriptomic analyses. We also observed that many more known SNPs were located within genes annotated in Ensembl and AceView than in RefSeq. In particular, 1033 of 3041 trait/disease-associated SNPs involved in about 200 human traits/diseases that were previously reported to be in RefSeq intergenic regions could be relocated within Ensembl and AceView genes. Our findings illustrate that a more complete transcriptome generated by incorporating human gene annotations in diverse databases can strikingly improve the overall results of transcriptomic and genetic studies.

SUBMITTER: Chen G

PROVIDER: S-EPMC3677258 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses.

Chen Geng G Wang Charles C Shi Leming L Qu Xiongfei X Chen Jiwei J Yang Jianmin J Shi Caiping C Chen Long L Zhou Peiying P Ning Baitang B Tong Weida W Shi Tieliu T

RNA (New York, N.Y.) 20130219 4

Human gene annotation is crucial for conducting transcriptomic and genetic studies; however, the impacts of human gene annotations in diverse databases on related studies have been less evaluated. To enable full use of various human annotation resources and better understand the human transcriptome, here we systematically compare the human annotations present in RefSeq, Ensembl (GENCODE), and AceView on diverse transcriptomic and genetic analyses. We found that the human gene annotations in the ...[more]

PMID: 23431329

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Project description:BackgroundGenes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.ResultsIn this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.ConclusionGEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Project description:BackgroundThe success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.Methods & resultsWe conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with 'nonsynonymous' and 'loss of function' consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.ConclusionIncorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.

Project description:BACKGROUND:Pinus koraiensis is an evergreen tree species with strong cold resistance. However, the transcriptomic patterns in response to cold stress are poorly understood for P. koraiensis. In this study, global transcriptome profiles were generated for P. koraiensis under cold stress (-?20?°C) over time by high-throughput sequencing. RESULTS:More than 763 million clean reads were produced, which assembled into a nonredundant data set of 123,445 unigenes. Among them, 38,905 unigenes had homology with known genes, 18,239 were assigned to 54 gene ontology (GO) categories and 18,909 were assigned to 25 clusters of orthologous groups (COG) categories. Comparison of transcriptomes of P. koraiensis seedlings grown at room temperature (20?°C) and low temperature (-?20?°C) revealed 9842 differential expressed genes (DEGs) in the 6?h sample, 9250 in the 24?h sample, and 9697 in the 48?h sample. The number of DEGs in the pairwise comparisons of 6?h, 24?h and 48?h was relatively small. The accuracy of the RNA-seq was validated by analyzing the expression patterns of 12 DEGs by quantitative real-time PCR (qRT-PCR). In this study, 34 DEGs (22 upregulated and 12 downregulated) were involved in the perception and transmission of cold signals, 96 DEGs (41 upregulated and 55 downregulated) encoding 8 transcription factors that regulated cold-related genes expression, and 27 DEGs (17 upregulated and 10 downregulated) were involved in antioxidant mechanisms in response to cold stress. Among them, the expression levels of c63631_g1 (annexin D1), c65620_g1 (alpha-amylase isozyme 3C), c61970_g1 (calcium-binding protein KIC), c51736_g1 (ABA), c58408_g1 (DREB3), c66599_g1 (DREB3), c67548_g2 (SOD), c55044_g1 (CAT), c71938_g2 (CAT) and c11358_g1 (GPX) first increased significantly and then decreased significantly with the extension of stress time. CONCLUSIONS:A large number of DEGs were identified in P. koraiensis under cold stress, especially the DEGs involved in the perception and transmission of cold signals, the DEGs encoding TFs related to cold regulation and the DEGs removing ROS in antioxidation mechanisms. The transcriptome and digital expression profiling of P. koraiensis could facilitate the understanding of the molecular control mechanism related to cold responses and provide the basis for the molecular breeding of conifers.

Project description:MotivationOntologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product's function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology's representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible.ResultsTo do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed.ConclusionThis standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse.AvailabilityThe GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal.

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Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses.

Publications

Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses.

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