Gene Ontology Tree Research Articles

A genome-wide study of gastric intramucosal neoplasia based on somatic copy number alterations, gene mutations, and mRNA expression patterns.

We performed comprehensive analyses of somatic copy number alterations (SCNAs) and gene expression profiles of gastric intramucosal neoplasia (IMN) using array-based methods in 97 intestinal-type IMNs, including 39 low-grade dysplasias (LGDs), 37 high-grade dysplasias (HGDs), and 26 intramucosal carcinomas (IMCs) with stromal invasion of the lamina propria to identify the molecular mechanism of IMN. In addition, we examined gene mutations using gene panel analyses. We used cluster analyses for exclusion of arbitrariness to identify SCNA patterns and expression profiles. IMNs were classified into two distinct subgroups (subgroups 1 and 2) based on SCNA patterns. Subgroup 1 showed a genomic stable pattern due to the low frequency of SCNAs, whereas subgroup 2 exhibited a chromosomal instability pattern due to the high frequencies of SCNAs and TP53 mutations. Interestingly, although the frequencies of LGD and HGD were significantly higher in subgroup 1 than in subgroup 2, IMC was commonly found in both types. Although the expression profiles of specific mRNAs could be used to categorise subgroups 1 and 2, no clinicopathological findings correlated with either subgroup. We examined signalling pathways specific to subgroups 1 and 2 to identify the association of each subgroup with signalling pathways based on gene ontology tree visualisation: subgroups 1 and 2 were associated with haem metabolism and chromosomal instability, respectively. These findings reveal a comprehensive genomic landscape that highlights the molecular complexity of IMNs and provide a road map to facilitate our understanding of gastric IMNs.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation.

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Functional Annotation and Analysis of Genetic Variants

The process of functional annotation of human genetic variants is important for some significant biomedical tasks like understanding functional genomics and gene-disease associations. Moreover, genetic variants that lead to pathogenic mutations can have harmful consequences which may lead to certain medical conditions or diseases. This paper investigates and presents two methodologies utilizing the Gene Ontology (GO) for functional annotation of genetic variants (and human gene mutations): (1) using the concept of term enrichment from the biological process taxonomy in the gene ontology, and (2) using the concept of least common subsumer (LCS) within the gene ontology tree. The second methodology induces the most significant and accurate functions for a given set of variants having one common aspect, e.g. specific disease. This method is based on the structure of the directed acyclic graph of GO to induce and identify the most significant LCS’s to be used for functionally annotating the variants under investigation. We applied the methods on a large sets of genetic variants causing mutations. Our method can determine important functions and with significance level of p<0.02, the outcomes and results of the method are biologically significant. We found that certain ontology annotations are more related to mutations through certain genes compared with normal bp functions (biological process terms) in GO; for example, one of these bp functions is {GO:0031325; positive regulation of cellular metabolic process}. The results are basically important and suggest that a mutation can be annotated with functions from the gene ontology, e.g. biological process aspect, just like the genes. This outcome may contribute into a more complete understanding of mutation-gene-disease relationships, mutation pathogenicity, and understanding disease mechanisms..

Bioinformatic analysis reveals novel hub genes and pathways associated with hypertensive nephropathy.

Hypertensive nephropathy (HTN) is one of the leading causes of end-stage renal disease and is closely associated with inflammation and tubule-interstitial fibrosis. The molecular mechanism underlying HTN remains unclear. This study used bioinformatic analysis to identify the novel gene targets for HTN. We downloaded the microarray data of GSE99325 and GSE32591 from Gene Expression Omnibus. The dataset comprised 20 HTN and 15 normal samples. The differentially expressed genes (DEG) were identified, and then gene ontology (GO) enrichment was performed, and a GO tree was constructed by using clusterProfiler and ClueGO. In addition, a protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape. The novel hub genes were validated in in vitro experiments. A total of 267 genes (117 up-regulated and 150 down-regulated genes) were identified as DEG. GO analysis and the GO tree indicated that the DEG were mainly associated with steroid hormone response and the extracellular matrix. Based on the protein-protein interaction network, we screened out several novel hub genes. Considering the findings and the literature review, we focused on and validated the dual specificity phosphatase 1, tissue inhibitor of matrix metalloproteinases 1, fos proto-oncogene and jun proto-oncogenes, which may play significant roles in the pathogenesis of HTN. These findings were consistent with the bioinformatic results for the in vitro validation. This study identified for the first time novel hub genes with microarray data in HTN by using bioinformatic analysis and provided novel evidence and clues for future works.

Abstract WP136: Grafted Human Neural Stem Cell-Secreted Matrilin-2 Improves Functional Recovery in Ischemic-Stroke Rats

Identifying the secretome of grafted stem cells is an important first step to elucidate how the graft communicates with the host brain, at the molecular level , to enhance post-stroke recovery. Here we use Translating Ribosome Affinity Purification (TRAP) to uncover the secretome of grafted human neural stem cells (hNSCs) and identify potential efficacious stem cell factors. Nude rats were subjected to permanent distal cerebral artery occlusion; 7d later hNSCs expressing GFP under a ribosomal-specific promoter were transplanted into the cortex of stroke (S) and naive (N) rats. The graft site was dissected 7d later and processed using TRAP to isolate ribosome-bound RNA from the grafted hNSCs. TRAP-isolated RNA was subjected to RNA sequencing and 479 differentially expressed genes were found between hNSC grafts in S and N brains. Of these, 21 genes encode secreted proteins, 14 of which were upregulated in hNSCs grafts in the stroke-injured brain. Pearson Correlation analysis revealed that the expression level of 9 of the 14 genes positively correlated with functional recovery. Gene Ontology (GO) analysis of the 14 genes identified the top enriched biological process as Response to Wounding which is related (i.e. a direct ancestor in the GO tree) to inflammatory response and axon injury. Three genes were present in this GO term: NOG, MATN2 and WNT1. MATN2 encodes matrilin-2, a protein known to be involved in axon regeneration and inflammation. In situ hybridization and immunohistochemistry showed high expression of matrilin-2 in grafted hNSCs. To test if matrilin-2 could affect post-stroke recovery we infused human recombinant matrilin-2 or vehicle into the cortex of stroke-injured rats. Seven days post-infusion, rats receiving matrilin-2 showed greater behavior recovery than the vehicle group. As matrilin-2 promotes axon outgrowth we investigated if it activated Rac-1 GTPase, which is associated with axon sprouting. Recombinant matrilin-2 increased activation of Rac-1 in primary mouse neural cultures, compared to the control. Our data show that grafted hNSCs in the stroke-injured brain express MATN2 and that secreted matrilin-2 may be involved in hNSC-induced stroke recovery potentially via activation of Rac-1 GTPase and axon sprouting.

Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions.

The gene ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. Under this paradigm, the locations of two GO terms in the tree dictate their similarity score. In this article, we introduce two new solutions for this problem by focusing instead on the definitions of the GO terms. We apply neural network-based techniques from the natural language processing (NLP) domain. The first method does not rely on the GO tree, whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO definitions by treating them as two unordered sets of words. The word similarity is estimated by a word embedding model that maps words into an N-dimensional space. In our second approach, we account for the word-ordering within a sentence. We use a sentence encoder to embed GO definitions into vectors and estimate how likely one definition entails another. We validate our methods in two ways. In the first experiment, we test the model's ability to differentiate a true protein-protein network from a randomly generated network. In the second experiment, we test the model in identifying orthologs from randomly matched genes in human, mouse, and fly. In both experiments, a hybrid of NLP and GO tree-based method achieves the best classification accuracy.

Multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene Clustering.

To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component, and Molecular-function. It has several applications in the field of bioinformatics like annotating/measuring gene-gene or protein-protein semantic similarity, identifying genes/proteins by their GO annotations for disease gene and target discovery, etc. To determine semantic similarity between genes, several semantic measures have been proposed in literature, which involve information content of GO-terms, GO tree structure, or the combination of both. But, most of the existing semantic similarity measures do not consider different topological and information theoretic aspects of GO-terms collectively. Inspired by this fact, in this article, we have first proposed three novel semantic similarity/distance measures for genes covering different aspects of GO-tree. These are further implanted in the frameworks of well-known multi-objective and single-objective based clustering algorithms to determine functionally similar genes. For comparative analysis, 10 popular existing GO based semantic similarity/distance measures and tools are also considered. Experimental results on Mouse genome, Yeast, and Human genome datasets evidently demonstrate the supremacy of multi-objective clustering algorithms in association with proposed multi-factored similarity/distance measures. Clustering outcomes are further validated by conducting some biological/statistical significance tests. Supplementary information is available at https://www.iitp.ac.in/sriparna/journals.html.

Abstract 839: Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer

Abstract Introduction Neoadjuvant chemoradiotherapy (nCRT) plays a central role in the management of locally advanced rectal cancer. For many, nCRT leads to clinically meaningful tumour regression. However, up to 20% exhibit little to no response and, in this group, nCRT results in unnecessary delays to definitive treatment. There is a critical need for development of robust molecular methods to predict response to nCRT, to allow for more precise treatment stratification. Although numerous molecular pathways and biomarkers have been implicated in radiosensitivity, the lack of a unifying interpretation of these findings has restricted translational deployment. The aim of the current study was to develop a ‘knowledge network’ with which to visualise and interpret published, quantitative, biomarker data relating to radiosensitivity in rectal cancer, beyond the conventional format of a systematic review. Methods Existing data on predictive biomarkers were retrieved by way of a systematic review of electronic bibliographic databases. Biomarkers were classified according to biological function and built into a hierarchical Gene Ontology tree. Significance was binarized based on p-values or multivariate statistics. An interactive, direct acyclic graph was developed using the Dagre-D3 JavaScript library. Nodes were sized by number of studied biomarkers and color-coded according to their significance scores. The scores reflect the ratio of significant versus non-significant evidence across studied biomarkers. A negative score range indicates more non-significant biomarker findings for that ontological term (node). Weightings were applied to reflect those biomarkers confirmed as significant across two or more studies. p-values of 0.05 or less (adjusted for multiple comparative analysis where appropriate) were considered to be statistically significant. Results 72 individual biomarkers were identified through review. On highest order classification, the domains of response to stress and factors inhibiting apoptosis were found to be most significant (aggregate significance scores across identified biomarkers, 0.75 and 0.714 respectively). A predictive power was not reached for the majority of prognostic biomarkers; rather, the levels of their statistical significance were assessed. Conclusions Building a knowledge-based network analysis of published data identifies promising areas for further research into cellular mechanisms, which may aid in biomarker discovery. Regarding significant node clusters within a network of published data on predictive biomarkers, modifications in cellular metabolic responses to the insult posed by nCRT appear to hold promise in developing a panel of biomarkers with a predictive capacity for response. Network-based analytics takes into account the complex nature of response to therapy, and is a novel way of presenting results obtained from a systematic review. Citation Format: Liam R. Poynter, Kirill Veselkov, Dieter Galea, James Kinross, Alexander Mirnezami, Jeremy Nicholson, Zoltan Takats, Reza Mirnezami, Ara Darzi. Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 839. doi:10.1158/1538-7445.AM2017-839

The Novel Landscape of Long Non-Coding RNAs in Response to Human Foamy Virus Infection Characterized by RNA-Seq.

Human foamy virus (HFV) is a complex and unique retrovirus with the longest genomes among retroviruses that are used as vectors for gene therapy. Long non-coding RNAs (lncRNAs) are regarded as key regulators that are involved in diverse biological processes during viral infection. However, the role of lncRNAs in HFV infection remains unknown. In this study, we utilized next-generation sequencing to first characterize lncRNAs in 293T cells after HFV infection, evaluating length distribution, exon number distribution, volcano picture, and lncRNA class distribution. We identified 11,336 lncRNAs (4,729 upregulated lncRNAs and 6,588 downregulated lncRNAs) and 61,367 mRNAs (30,133 upregulated mRNAs and 31,220 downregulated mRNAs), which were differentially expressed in the HFV-infected 293T cells. Subsequently, six differentially expressed lncRNAs characterized using RNA-seq were confirmed by quantitative real-time polymerase chain reaction assays. Interestingly, Gene Ontology (GO)/Gene Ontology Tree Machine (GOTM) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway analyses indicated that positive regulation of interleukin 8 (IL8) production and cytokine-cytokine receptor interaction might be involved in the functional enrichment of lncRNAs. Moreover, cis-acting and trans-acting regulatory networks show that NR_028036 may target the fas gene in a cis-acting manner and that ENST00000354838 may target the IL18 gene in a trans-acting manner. Overall, these results not only provide novel insights into the relationship between HFV and lncRNAs in the host response to infection but also have implications for the future wider application of HFV as a vector.

A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL.

BackgroundThe process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel ‘targets’, usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases.DescriptionWe have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the ‘ChEMBL protein target slim’ allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms.We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The ‘ChEMBL protein target slim’ provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL.The ‘ChEMBL protein target slim’ is available as a browsable ‘Gene Ontology Tree’ on the ChEMBL site under the browse targets tab (https://www.ebi.ac.uk/chembl/target/browser). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website (http://geneontology.org/page/go-slim-and-subset-guide).ConclusionsWe have created a protein target navigation tool based on the ‘ChEMBL protein target slim’. The ‘ChEMBL protein target slim’ provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the ‘ChEMBL protein target slim’ can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways.

TopoICSim: a new semantic similarity measure based on gene ontology

BackgroundThe Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.ResultsHere we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests.ConclusionsThe TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R.

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods.

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

Functional Cross-Talking between Differentially Expressed and Alternatively Spliced Genes in Human Liver Cancer Cells Treated with Berberine

Berberine has been identified with anti-proliferative effects on various cancer cells. Many researchers have been trying to elucidate the anti-cancer mechanisms of berberine based on differentially expressed genes. However, differentially alternative splicing genes induced by berberine might also contribute to its pharmacological actions and have not been reported yet. Moreover, the potential functional cross-talking between the two sets of genes deserves further exploration. In this study, RNA-seq technology was used to detect the differentially expressed genes and differentially alternative spliced genes in BEL-7402 cancer cells induced by berberine. Functional enrichment analysis indicated that these genes were mainly enriched in the p53 and cell cycle signalling pathway. In addition, it was statistically proven that the two sets of genes were locally co-enriched along chromosomes, closely connected to each other based on protein-protein interaction and functionally similar on Gene Ontology tree. These results suggested that the two sets of genes regulated by berberine might be functionally cross-talked and jointly contribute to its cell cycle arresting effect. It has provided new clues for further researches on the pharmacological mechanisms of berberine as well as the other botanical drugs.

Transcriptome Profiling of Louisiana iris Root and Identification of Genes Involved in Lead-Stress Response.

Louisiana iris is tolerant to and accumulates the heavy metal lead (Pb). However, there is limited knowledge of the molecular mechanisms behind this feature. We describe the transcriptome of Louisiana iris using Illumina sequencing technology. The root transcriptome of Louisiana iris under control and Pb-stress conditions was sequenced. Overall, 525,498 transcripts representing 313,958 unigenes were assembled using the clean raw reads. Among them, 43,015 unigenes were annotated and their functions classified using the euKaryotic Orthologous Groups (KOG) database. They were divided into 25 molecular families. In the Gene Ontology (GO) database, 50,174 unigenes were categorized into three GO trees (molecular function, cellular component and biological process). After analysis of differentially expressed genes, some Pb-stress-related genes were selected, including biosynthesis genes of chelating compounds, metal transporters, transcription factors and antioxidant-related genes. This study not only lays a foundation for further studies on differential genes under Pb stress, but also facilitates the molecular breeding of Louisiana iris.

Exploring robust diagnostic signatures for cutaneous melanoma utilizing genetic and imaging data.

Multimodal data combined in an integrated dataset can be used to aim the identification of instrumental biological actions that trigger the development of a disease. In this paper, we use an integrated dataset related to cutaneous melanoma that fuses two separate sets providing complementary information (gene expression profiling and imaging). Our first goal is to select a subset of genes that comprise candidate genetic biomarkers. The derived gene signature is then utilized in order to select imaging features, which characterize disease at a macroscopic level, presenting the highest, mutual information content to the selected genes. Using information gain ratio measurements and exploration of the gene ontology tree, we identified a set of 32 uncorrelated genes with a pivotal role as regards molecular regulation of melanoma, which expression across samples correlates highly with the different pathological states. These genes steered the selection of a subset of uncorrelated imaging features based on their ranking according to mutual information measurements to the selected gene expression values. Selected genes and imaging features were used to train various classifiers that could generalize well when discriminating malignant from benign melanoma samples. Results on the selection on imaging features and classification were compared to feature selection based on a straight forward statistical selection and a stochastic-based methodology. Genes in the backstage of low-level biological processes showed to carry higher information content than the macroscopic imaging features.

Pop’s Pipes: poplar gene expression data analysis pipelines

We developed multiple gene expression pipelines and assembled them into a web-based tool called Pop's Pipes to facilitate preprocessing and analysis of substantial poplar gene expression data. The input data can be spatio- temporal microarray and RNA-seq data from comparable tissues, time points, or treatment-vs-control conditions. Pop's Pipes can be used to identify differentially expressed genes between one or multiple paired tissues, time points, or treatment-vs-control conditions in a single in silico anal- ysis. The differentially expressed genes (DEGs) obtained for each comparison will be automatically analyzed by Pop's Pipes for identifying significantly enriched gene on- tologies and interpro protein domains. Also, significantly changed metabolic pathways across all input data sets will be identified. We also integrated a pipeline into Pop's Pipes for constructing any of three type gene ontology trees when a short list of gene ontologies from biological processes, molecular functions, or cellular components is used as an input. The resulting information from Pop's Pipes enables scrutiny to create spatiotemporal models and hypotheses to understand how poplar develops and functions. Pop' sP ipes can analyze a microarray or RNA-seq data set with 10 time points in 4-10 h, with each time point containing three replicates of treatments and three controls. Such a data set usually takes a bioinformatician a few months to a year to analyze. Pop's Pipes can thus save users tremendous amounts of research time when large numbers of compara- tive data need to be analyzed.

Genes involved in immunity and apoptosis are associated with human presbycusis based on microarray analysis

Conclusions: Genes involved in immunity and apoptosis were associated with human presbycusis. CCR3 and GILZ played an important role in the pathogenesis of presbycusis, probably through regulating chemokine receptor, T-cell apoptosis, or T-cell activation pathways. Objectives: To identify genes associated with human presbycusis and explore the molecular mechanism of presbycusis. Methods: Hearing function was tested by pure-tone audiometry. Microarray analysis was performed to identify presbycusis-correlated genes by Illumina Human-6 BeadChip using the peripheral blood samples of subjects. To identify biological process categories and pathways associated with presbycusis-correlated genes, bioinformatics analysis was carried out by Gene Ontology Tree Machine (GOTM) and database for annotation, visualization, and integrated discovery (DAVID). Quantitative RT-PCR (qRT-PCR) was used to validate the microarray data. Results: Microarray analysis identified 469 up-regulated genes and 323 down-regulated genes. Both the dominant biological processes by Gene Ontology (GO) analysis and the enriched pathways by Kyoto encyclopedia of genes and genomes (KEGG) and BIOCARTA showed that genes involved in immunity and apoptosis were associated with presbycusis. In addition, CCR3, GILZ, CXCL10, and CX3CR1 genes showed consistent difference between groups for both the gene chip and qRT-PCR data. The differences of CCR3 and GILZ between presbycusis patients and controls were statistically significant (p < 0.05).

LipidGO: database for lipid-related GO terms and applications

Lipid, an essential class of biomolecules, is receiving increasing attention in the research community, especially with the development of analytical technique like mass spectrometry. Gene Ontology (GO) is the de facto standard function annotation scheme for gene products. Identification of both explicit and implicit lipid-related GO terms will help lipid research in many ways, e.g. assigning lipid function in protein function prediction. We have constructed a Web site 'LipidGO' that facilitates browsing and searching lipid-related GO terms. An expandable hierarchical GO tree is constructed that allows users to find lipid-related GO terms easily. To support large-scale analysis, a user is able to upload a list of gene products or a list of GO terms to find out which of them is lipid related. Finally, we demonstrate the usefulness of 'LipidGO' by two applications: (i) identifying lipid-related gene products in model organisms and (ii) discovering potential novel lipid-related molecular functions LipidGO is available at http://compbio.ddns.comp.nus.edu.sg/%7elipidgo/index.php.

RamiGO: an R/Bioconductor package providing an AmiGO Visualize interface

The R/Bioconductor package RamiGO is an R interface to AmiGO that enables visualization of Gene Ontology (GO) trees. Given a list of GO terms, RamiGO uses the AmiGO visualize API to import Graphviz-DOT format files into R, and export these either as images (SVG, PNG) or into Cytoscape for extended network analyses. RamiGO provides easy customization of annotation, highlighting of specific GO terms, colouring of terms by P-value or export of a simplified summary GO tree. We illustrate RamiGO functionalities in a genome-wide gene set analysis of prognostic genes in breast cancer. RamiGO is provided in R/Bioconductor, is open source under the Artistic-2.0 License and is available with a user manual containing installation, operating instructions and tutorials. It requires R version 2.15.0 or higher. URL: http://bioconductor.org/packages/release/bioc/html/RamiGO.html

Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses

BackgroundSemantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lower parts of GO trees are full of terms with more specific semantics.MethodsThis study proposed a method of measuring semantic similarities between GO terms using the entire GO tree structure, including both the upper (ancestral) and the lower (descendant) parts. Comprehensive comparison studies were performed with well-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, protein–protein interactions, and biological pathway analyses.ConclusionThe proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.