Tak Lee

TRRUST: A reference database of human transcriptional regulatory interactions

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.

AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species

Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

Network-assisted crop systems genetics: network inference and integrative analysis

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years.

GWAB: A web server for the network-based boosting of human genome-wide association data

Abstract During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10−8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

WheatNet: a Genome-Scale Functional Network for Hexaploid Bread Wheat, Triticum aestivum

Gene networks provide a system-level overview of genetic organizations and enable the dissection of functional modules underlying complex traits. Integration of diverse genomics data based on the Bayesian statistics framework has been successfully applied to the construction of genome-scale functional networks for major crop species such as rice (Lee et al., 2011), soybean (Kim et al., 2017), and tomato (Kim et al., 2016), and their predictive power for gene-to-trait associations has been demonstrated. However, such a predictive gene network is not yet available for bread wheat, Triticum aestivum, an important staple food crop accounting for approximately 20% of the world’s daily food consumption. Bread wheat also serves as a model for studying polyploidy in plants. Some of the reasons that functional genomics studies on bread wheat have lagged behind those on other crops include the large genome of bread wheat ( 17 Gb) and its polyploidy nature, which complicates genetic analysis. However, recent advances in wheat research have considerably improved genome assembly and gene models (International Wheat Genome Sequencing Consortium, 2014). Furthermore, the discovery and application of genome editing (Upadhyay et al., 2013) and TILLING technologies (Uauy et al., 2009) have enabled targeted mutagenesis in wheat protoplasts and whole plants, setting the stage for the application of reverse genetics approaches for functional characterization of wheat genes.

Preliminary study on physiological changes of hemorrhagic shock in rats

Hemorrhagic shock is a common cause of death in emergency rooms. The objective evaluation of hemorrhagic shock is very important for early diagnosis and treatment. The purpose of this study is to understand its mechanism by analyzing the changes of bio-signals in hemorrhagic shock using controlled hemorrhage of Sprague-Dawley rats. We constructed an integrated system to be able to control bleeding and to simultaneously measure bio-signals such as ECG, blood pressure, temperature, and respiration. In order to verify the system, we measured the bio-signals mentioned above while hemorrhagic shock was induced by withdrawing blood (2.5ml/100g/15min) from a femoral vein for 10 rats.

A survival prediction model of hemorrhagic shock in rats using a logistic regression equation

Hemorrhagic shock is a common cause of death in emergency rooms. Since the symptoms of hemorrhagic shock occur after shock has considerably progressed, it is difficult to diagnose shock early. The purpose of this study was to improve early diagnosis of hemorrhagic shock using a survival prediction model in rats. We measured ECG, blood pressure, respiration and temperature in 45 Sprague-Dawley rats, and then obtained a logistic regression equation predicting survival rates. Area under the ROC curves was 0.99. The Hosmer-Lemeshow goodness-of-fit chi-square was 0.86 (degree of freedom=8, p=0.999). Applying the determined optimal boundary value of 0.25, the accuracy of survival prediction was 94.7%.

Development of a portable A-ABR screener using a microprocessor

Hearing loss is one of the most common birth defects among infants. Most hearing-impaired children are not diagnosed until one to three years of age, which is too late to treat for normal speech and language development. If hearing impairment is identified and treated in its early stage, a childs speech and language skills could be comparable to his or her normal-hearing peers. Auditory brain-stem response (ABR) is nowadays one of the most reliable diagnostic tools in the early detection of hearing impairment. In this study, we applied the ‘Fsp’ method to distinguish between normal and impaired hearing. We have developed a battery-operated portable automated auditory brainstem response (A-ABR) system that automatically detects hearing impairment in neonates or infants. We partially validated the accuracy of this system in twenty normal-hearing adults.