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1.
Background
While the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided.Results
DNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation.Conclusions
Our novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1496-2) contains supplementary material, which is available to authorized users. 相似文献2.
Background
With the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression data.Results
We propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of annotations.Conclusions
A novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.3.
Background
Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.Results
We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.Conclusions
By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users. 相似文献4.
Background
Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).Results
The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.Conclusions
We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users. 相似文献5.
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Background
Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.Results
We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.Conclusions
We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users. 相似文献7.
Background
Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent–child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.Results
We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington’s disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.Conclusion
Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner. 相似文献8.
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Background
Plant resistance genes (R genes) exist in large families and usually contain both a nucleotide-binding site domain and a leucine-rich repeat domain, denoted NBS-LRR. The genome sequence of cassava (Manihot esculenta) is a valuable resource for analysing the genomic organization of resistance genes in this crop.Results
With searches for Pfam domains and manual curation of the cassava gene annotations, we identified 228 NBS-LRR type genes and 99 partial NBS genes. These represent almost 1% of the total predicted genes and show high sequence similarity to proteins from other plant species. Furthermore, 34 contained an N-terminal toll/interleukin (TIR)-like domain, and 128 contained an N-terminal coiled-coil (CC) domain. 63% of the 327 R genes occurred in 39 clusters on the chromosomes. These clusters are mostly homogeneous, containing NBS-LRRs derived from a recent common ancestor.Conclusions
This study provides insight into the evolution of NBS-LRR genes in the cassava genome; the phylogenetic and mapping information may aid efforts to further characterize the function of these predicted R genes.Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1554-9) contains supplementary material, which is available to authorized users. 相似文献10.
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Background
As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data.Methodology/Principal Findings
The proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete.Conclusions/Significance
It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable to a genome-wide application. 相似文献13.
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Background
DAVID is the most popular tool for interpreting large lists of gene/proteins classically produced in high-throughput experiments. However, the use of DAVID website becomes difficult when analyzing multiple gene lists, for it does not provide an adequate visualization tool to show/compare multiple enrichment results in a concise and informative manner.Result
We implemented a new R-based graphical tool, BACA (Bubble chArt to Compare Annotations), which uses the DAVID web service for cross-comparing enrichment analysis results derived from multiple large gene lists. BACA is implemented in R and is freely available at the CRAN repository (http://cran.r-project.org/web/packages/BACA/).Conclusion
The package BACA allows R users to combine multiple annotation charts into one output graph by passing DAVID website.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-015-0477-4) contains supplementary material, which is available to authorized users. 相似文献17.
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Konstantinos C Tsolis Ekaterini S Bei Ioanna Papathanasiou Fotini Kostopoulou Vassiliki Gkretsi Kalliopi Kalantzaki Konstantinos Malizos Michalis Zervakis Aspasia Tsezou Anastassios Economou 《Clinical proteomics》2015,12(1):12
Background
Osteoarthritis (OA) is a multi-factorial disease leading progressively to loss of articular cartilage and subsequently to loss of joint function. While hypertrophy of chondrocytes is a physiological process implicated in the longitudinal growth of long bones, hypertrophy-like alterations in chondrocytes play a major role in OA. We performed a quantitative proteomic analysis in osteoarthritic and normal chondrocytes followed by functional analyses to investigate proteome changes and molecular pathways involved in OA pathogenesis.Methods
Chondrocytes were isolated from articular cartilage of ten patients with primary OA undergoing knee replacement surgery and six normal donors undergoing fracture repair surgery without history of joint disease and no OA clinical manifestations. We analyzed the proteome of chondrocytes using high resolution mass spectrometry and quantified it by label-free quantification and western blot analysis. We also used WebGestalt, a web-based enrichment tool for the functional annotation and pathway analysis of the differentially synthesized proteins, using the Wikipathways database. ClueGO, a Cytoscape plug-in, is also used to compare groups of proteins and to visualize the functionally organized Gene Ontology (GO) terms and pathways in the form of dynamical network structures.Results
The proteomic analysis led to the identification of a total of ~2400 proteins. 269 of them showed differential synthesis levels between the two groups. Using functional annotation, we found that proteins belonging to pathways associated with regulation of the actin cytoskeleton, EGF/EGFR, TGF-β, MAPK signaling, integrin-mediated cell adhesion, and lipid metabolism were significantly enriched in the OA samples (p ≤10−5). We also observed that the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP, show distinct synthesis levels, characteristic for OA or control chondrocytes.Conclusion
In this study we compared the quantitative changes in proteins synthesized in osteoarthritic compared to normal chondrocytes. We identified several pathways and proteins to be associated with OA chondrocytes. This study provides evidence for further testing on the molecular mechanism of the disease and also propose proteins as candidate markers of OA chondrocyte phenotype.Electronic supplementary material
The online version of this article (doi:10.1186/s12014-015-9085-6) contains supplementary material, which is available to authorized users. 相似文献20.
Michael Cantor Henrik Nordberg Tatyana Smirnova Matthias Hess Susannah Tringe Inna Dubchak 《BMC bioinformatics》2015,16(1)