scholarly journals The Planarian Anatomy Ontology: A resource to connect data within and across experimental platforms

Development ◽  
2021 ◽  
Author(s):  
Stephanie H. Nowotarski ◽  
Erin L. Davies ◽  
Sofia M. C. Robb ◽  
Eric J. Ross ◽  
Nicolas Matentzoglu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Anatomy ◽  
System Level ◽  
Anatomy Ontology ◽  
Data Annotation ◽  
Life Cycle Stages ◽  
Organization Framework ◽  
Relational Framework ◽  
Tool Building ◽  
Transcript Gene

As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system-level across all life cycle stages, in intact animals, and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource.

scholarly journals The Planarian Anatomy Ontology: A resource to connect data within and across experimental platforms

2020 ◽  
Author(s):  
Stephanie H. Nowotarski ◽  
Erin L. Davies ◽  
Sofia M. C. Robb ◽  
Eric J. Ross ◽  
Nicolas Matentzoglu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Anatomy ◽  
Controlled Vocabulary ◽  
System Level ◽  
Web Based ◽  
Anatomy Ontology ◽  
Data Annotation ◽  
Life Cycle Stages ◽  
Cross Platform ◽  
Anatomical Nomenclature

AbstractAs the planarian Schmidtea mediterranea (Smed) gains popularity as a research organism, the need for standard anatomical nomenclature is increasingly apparent. A controlled vocabulary streamlines data annotation, improves data organization, and enhances cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable framework of defined Smed anatomical terms organized using relationships. The most current version contains over 800 terms that describe Smed anatomy from subcellular to system-level across all life cycle stages, in intact animals, and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote ontology interoperability and comparative anatomy studies. To demonstrate the utility of PLANA for data curation, we created web-based resources for planarian embryogenesis, including a staging series and molecular fate mapping atlas, as well as a searchable Planarian Anatomy Gene Expression database, which integrates a variety of published gene expression data and allows retrieval of information of all published sequences associated with specific planarian anatomical regions. Finally, we report methods for continued curation of PLANA, providing a path for expansion and evolution of this community resource.Summary StatementWe report construction of an anatomy ontology for an emerging research organism and show its use to curate and mine data across multiple experimental platforms.

scholarly journals Expression of the Pseudorabies Virus Latency-Associated Transcript Gene during Productive Infection of Cultured Cells

1999 ◽  
Vol 73 (12) ◽  
pp. 9781-9788 ◽  
Author(s):  
Ling Jin ◽  
Gail Scherba
Keyword(s):  
Gene Expression ◽  
Pseudorabies Virus ◽  
Cultured Cells ◽  
Initiation Site ◽  
Early Gene ◽  
Productive Infection ◽  
Virus Latency ◽  
Infected Cells ◽  
Transcript Gene

ABSTRACT Like other alphaherpesviruses, pseudorabies virus (PrV) exhibits restricted gene expression during latency. These latency-associated transcripts (LATs) are derived from the region located within 0.69 to 0.77 map units of the viral genome. However, the presence of such viral RNAs during a productive infection has not been described. Although several transcripts originating between 0.706 to 0.737 map units have been detected in PrV-infected cultured cells, their relationship to the LATs has not been examined. Therefore, to determine if any correlation exists between PrV LAT gene expression in the natural and laboratory systems, transcription from the LAT gene region during lytic infection of cultured neuronal and nonneuronal cells was evaluated. A Northern blot assay using single-stranded RNA probes complementary to the spliced in vivo 8.4-kb largest latency transcript (LLT) detected 1.0-, 2.0-, and 8.0-kb poly(A) RNAs in all PrV-infected cells lines. The 1.0- and 8.0-kb transcripts partially overlapped the first and second exons of the LLT, respectively. In contrast, portions of both LLT exons comprised the 2.0-kb RNA sequence, which lacked the same intron as the LLT. Generation of this transcript began about 243 bp downstream of the LLT initiation site and terminated near the junction of BamHI fragments 8′ and 8. Its synthesis was inhibited by cycloheximide but not by cytosine β-d-arabinofuranoside, which suggests that the 2.0-kb RNA is not an immediate-early gene product. Thus, although the PrV LAT gene is transcriptionally active during a productive infection of cultured cells, the resulting RNAs are distinctive from the LLT.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

scholarly journals Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 586
Author(s):  
Papori Neog Bora ◽  
Vishwa Jyoti Baruah ◽  
Surajit Borkotokey ◽  
Loyimee Gogoi ◽  
Priyakshi Mahanta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Diseases ◽  
System Level ◽  
Theoretic Model ◽  
Cancer Dataset ◽  
Network Games ◽  
Diagnosis And Prognosis ◽  
Network Game ◽  
Game Theoretic ◽  
Relevance Index

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

scholarly journals Assessment of reference genes at six different developmental stages of Schistosoma mansoni for quantitative RT-PCR

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gilbert O. Silveira ◽  
Murilo S. Amaral ◽  
Helena S. Coelho ◽  
Lucas F. Maciel ◽  
Adriana S. A. Pereira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Life Cycle ◽  
Schistosoma Mansoni ◽  
Reference Genes ◽  
Large Scale ◽  
Developmental Stages ◽  
Adult Males ◽  
Histone H4 ◽  
Expression Levels ◽  
Life Cycle Stages

AbstractReverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) is the most used, fast, and reproducible method to confirm large-scale gene expression data. The use of stable reference genes for the normalization of RT-qPCR assays is recognized worldwide. No systematic study for selecting appropriate reference genes for usage in RT-qPCR experiments comparing gene expression levels at different Schistosoma mansoni life-cycle stages has been performed. Most studies rely on genes commonly used in other organisms, such as actin, tubulin, and GAPDH. Therefore, the present study focused on identifying reference genes suitable for RT-qPCR assays across six S. mansoni developmental stages. The expression levels of 25 novel candidates that we selected based on the analysis of public RNA-Seq datasets, along with eight commonly used reference genes, were systematically tested by RT-qPCR across six developmental stages of S. mansoni (eggs, miracidia, cercariae, schistosomula, adult males and adult females). The stability of genes was evaluated with geNorm, NormFinder and RefFinder algorithms. The least stable candidate reference genes tested were actin, tubulin and GAPDH. The two most stable reference genes suitable for RT-qPCR normalization were Smp_101310 (Histone H4 transcription factor) and Smp_196510 (Ubiquitin recognition factor in ER-associated degradation protein 1). Performance of these two genes as normalizers was successfully evaluated with females maintained unpaired or paired to males in culture for 8 days, or with worm pairs exposed for 16 days to double-stranded RNAs to silence a protein-coding gene. This study provides reliable reference genes for RT-qPCR analysis using samples from six different S. mansoni life-cycle stages.

scholarly journals Developmental Anatomy Ontology of Zebrafish: an Integrative semantic framework

2007 ◽  
Vol 4 (3) ◽  
pp. 64-76 ◽  
Author(s):  
Mounia Belmamoune ◽  
Fons J. Verbeek
Keyword(s):  
Gene Expression ◽  
Information Systems ◽  
Heterogeneous Databases ◽  
Marker Genes ◽  
Learning Approaches ◽  
Anatomy Ontology ◽  
Developmental Anatomy ◽  
Zebrafish Development ◽  
Semantic Framework ◽  
Expression Of Genes

Summary Integration of information is quintessential to make use of the wealth of bioinformatics resources. One aspect of integration is to make databases interoperable through well annotated information. With new databases one strives to store complementary information and such results in collections of heterogeneous information systems. Concepts in these databases need to be connected and ontologies typically provide a common terminology to share information among different resources.Our focus of research is the zebrafish and we have developed several information systems in which ontologies are crucial. Pivot is an ontology describing the developmental anatomy, referred to as the Developmental Anatomy Ontolgoy of Zebrafish (DAOZ). The anatomical and temporal concepts are provided by the Zebrafish Information Network (ZFIN) and proven within the research community. We have constructed a 3D digital atlas of zebrafish development based on histology; the atlas is series of volumetric models; in each instance, every volume element is assigned to an anatomical term. Complementing the atlas we developed an information system with 3D patterns of gene expression in zebrafish development based on marker genes. The spatial and temporal annotations to these 3D images are drawn from the ontology that we have designed. In its design the DAOZ ontology is structured as a Directed Acyclic Graph (DAG). Such is required to find unique concept paths and prevent self referencing.As we need to address the ontology in a direct manner, the DAG structure is transferred to a database. The database is used in the integration of our databases that share concepts at different levels of aggregation. In order to make sure that sufficient levels of aggregation for applications in mind are present, the original vocabulary was enriched with more relations and concepts. Both databases can now be addressed with the same unique terms and co-occurrence and co-expression of genes can be readily extracted from the databases. Integration can be further extended to the ZFIN resource and also by including ontologies that relate to gene/gene expression (e.g. Gene Ontology). In this manner, interoperable information retrieval from heterogeneous databases can be realized. This greatly facilitates processing complex information and retrieving relations in the data through machine learning approaches.

scholarly journals C. elegansexhibits coordinated oscillation in gene activation in single-cell developmental data

2017 ◽  
Author(s):  
Luke A.D. Hutchison ◽  
Bonnie Berger ◽  
Isaac Kohane
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Meta Analysis ◽  
Gene Activation ◽  
Developmental Genes ◽  
Division Rate ◽  
Developmental Processes ◽  
Anatomy Ontology ◽  
Analysis Techniques ◽  
Cell Phenotypes

AbstractBackgroundThe advent ofin vivoautomated single-cell lineaging and sequencing will dramatically increase our understanding of development. New integrative analysis techniques are needed to generate insights from single-cell developmental data.ResultsWe applied novel meta-analysis techniques to the EPIC single-cell-resolution developmental gene expression dataset forC. elegansto show that a simple linear combination of the expression levels of the developmental genes is strongly correlated with the developmental age of the organism, irrespective of the cell division rate of different cell lineages. We uncovered a pattern of collective sinusoidal oscillation in gene activation, in multiple dominant frequencies and in multiple orthogonal axes of gene expression, pointing to the existence of a coordinated, multi-frequency global timing mechanism. We developed a novel method based on Fisher’s Discriminant Analysis (FDA) to identify linear gene expression weightings that are able to produce sinusoidal oscillations of any frequency and phase, adding to the evidence that oscillatory mechanisms likely play an important role in the timing of development. We cross-linked EPIC with gene ontology and anatomy ontology terms, employing FDA methods to identify previously unknown positive and negative genetic contributions to developmental processes and cell phenotypes.ConclusionsThis meta-analysis demonstrates new evidence for direct linear and/or sinusoidal mechanisms regulating the timing of development. We uncovered a number of previously unknown positive and negative correlations between developmental genes and developmental processes or cell phenotypes. The presented novel analysis techniques are broadly applicable within developmental biology.

scholarly journals The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

2020 ◽  
Author(s):  
Frederic B. Bastian ◽  
Julien Roux ◽  
Anne Niknejad ◽  
Aurélie Comte ◽  
Sara S. Fonseca Costa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Animal Species ◽  
Single Gene ◽  
Expression Patterns ◽  
R Package ◽  
Gene Expression Patterns ◽  
Data Types ◽  
Knock Out ◽  
Data Annotation ◽  
Very Large Datasets

ABSTRACTBgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as “healthy” or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

Sign in / Sign up

Export Citation Format

Share Document