Ontology-based validation and identification of regulatory phenotypes

Characterization of the WRKY gene family in Akebia trifoliata and their response to Colletotrichum acutatum

BACKGROUND

Akebia trifoliata, belonging to the Lardizabalaceae family, is a well-known Chinese traditional medicinal plant, susceptible to many diseases, such as anthracnose and powdery mildew. WRKY is one of the largest plant-specific transcription factor families and plays important roles in plant growth, development and stress response, especially in disease resistance. However, little was known about the numbers, characters, evolutionary relationship and expression of WRKY genes in A. trifoliata in response to plant disease due to lacking of A. trifoliata genome.

RESULTS

A total of 42 putative AktWRKY genes were identified based on the full-length transcriptome-sequencing data of A. trifoliata. Then 42 AktWRKY genes were divided into three major groups (Group I-III) based on the WRKY domains. Motif analysis showed members within same group shared a similar motif composition, implying a functional conservation. Tissue-specific expression analysis showed that AktWRKY genes could be detected in all tissues, while few AktWRKY genes were tissue specific. We further evaluated the expression of AktWRKY genes in three varieties in response to Colletotrichum acutatum by qRT-PCR. The expression patterns of AktWRKY genes were similar between C01 and susceptible variety I02, but distinctly different in resistant variety H05. In addition, it showed that more than 64 percentages of AktWRKY genes were differentially expressed during fungal infection in I02 and H05. Furthermore, Gene ontology (GO) analysis showed that AktWRKY genes were categorized into 26 functional groups under cellular components, molecular functions and biological processes, and a predicted protein interaction network was also constructed.

CONCLUSIONS

Results of bioinformation analysis and expression patterns implied that AktWRKYs might play multiple function in response to biotic stresses. Our study could facilitate to further investigate the function and regulatory mechanism of the WRKY in A. trifoliata during pathogen response.

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.

Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression as information to prediction phenotypes. Our method uses a neural network classifier that is able to account for hierarchical dependencies between phenotypes. We extensively evaluate our method and compare it with related approaches, and we show that DeepPheno results in better performance in several evaluations. Furthermore, we found that many of the new predictions made by our method have been added to phenotype association databases released one year later. Overall, DeepPheno simulates some aspects of human physiology and how molecular and physiological alterations lead to abnormal phenotypes.

Curation and annotation of planarian gene expression patterns with segmented reference morphologies

Motivation

Morphological and genetic spatial data from functional experiments based on genetic, surgical and pharmacological perturbations are being produced at an extraordinary pace in developmental and regenerative biology. However, our ability to extract knowledge from these large datasets are hindered due to the lack of formalization methods and tools able to unambiguously describe, centralize and interpret them. Formalizing spatial phenotypes and gene expression patterns is especially challenging in organisms with highly variable morphologies such as planarian worms, which due to their extraordinary regenerative capability can experimentally result in phenotypes with almost any combination of body regions or parts.

Results

Here, we present a computational methodology and mathematical formalism to encode and curate the morphological outcomes and gene expression patterns in planaria. Worm morphologies are encoded with mathematical graphs based on anatomical ontology terms to automatically generate reference morphologies. Gene expression patterns are registered to these standard reference morphologies, which can then be annotated automatically with anatomical ontology terms by analyzing the spatial expression patterns and their textual descriptions. This methodology enables the curation and annotation of complex experimental morphologies together with their gene expression patterns in a centralized standardized dataset, paving the way for the extraction of knowledge and reverse-engineering of the much sought-after mechanistic models in planaria and other regenerative organisms.

Availability and implementation

We implemented this methodology in a user-friendly graphical software tool, PlanGexQ, freely available together with the data in the manuscript at https://lobolab.umbc.edu/plangexq.

Supplementary information

Supplementary data are available at Bioinformatics online.

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.