TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.     

NORMA: The Network Makeup Artist — A Web Tool for Network Annotation Visualization

The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA.     

Junker: An Intergenic Explorer for Bacterial Genomes

In the past few decades, scientists from all over the world have taken a keen interest in novel functional units such as small regulatory RNAs, small open reading frames, pseudogenes, transposons, integrase binding attB/attP sites, repeat elements within the bacterial intergenic regions (IGRs) and in the analysis of those junk regions for ge- nomic complexity. Here we have developed a web server, named Junker, to facilitate the in-depth analysis of IGRs for examining their length distribution, four-quadrant...     

MitoP2: An Integrative Tool for the Analysis of the Mitochondrial Proteome

Mitochondria are crucial for normal cell metabolism and maintenance. Mitochondrial dysfunction has been implicated in a spectrum of human diseases, ranging from rare monogenic to common multifactorial disorders. Important for the understanding of organelle function is the assignment of its constituents, and although over 1,500 proteins are predicted to be involved in mammalian mitochondrial function, so far only about 900 are assigned to mitochondria with reasonable certainty. Continuing efforts are being taken to obtain a complete inventory of the mitochondrial proteome by single protein studies and high-throughput approaches. To be of best value for the scientific community this data needs to be structured, explored, and customized. For this purpose, the MitoP2 database () was established and is maintained in order to incorporate such data. The central database contains manually evaluated yeast, mouse, and human reference proteins, which show convincing evidence of a mitochondrial location. In addition, entries from genome-wide approaches that suggest protein localization are integrated and serve to compile a combined score for each candidate, which provides a best estimate of mitochondrial localization. Furthermore, it integrates information on the orthology between species, including Saccharomyces cerevisiae, mouse, human, Arabidopsis thaliana, and Neurospora crassa, thus mutually enhancing evidence across species. In contrast to other known databases, MitoP2 takes into account the reliability by which the protein is estimated as being mitochondrially located, as described herein. Multiple search functions, as well as information on disease causing genes and available mouse models, makes MitoP2 a valuable tool for the genetic investigation of human mitochondrial pathology.     

PIMD:An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion

The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.     

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.     

Intracellularly Truncated Human α2B-Adrenoceptors: Stable and Functional GPCRs for Structural Studies

All three α₂-adrenoceptor subtypes have a long third intracellular loop (3i), which is conserved by overall size and charge-hydrophobic properties but not by amino acid sequence similarity. These properties must be relevant for function and structure, because they have been preserved during hundreds of millions of years of evolution. The contribution of different loop portions to agonist/antagonist binding properties and G protein coupling of the human α_2B-adrenoceptor (α_2B-AR) was investigated with a series of 3i truncated constructs (Δ 3i). We used a variety of agonists/antagonists in competition binding assays. We stimulated α_2B-AR Δ3i with various agonists and measured [³⁵S]GTPγS binding in isolated cell membranes with or without antagonist inhibition. We also evaluated the ability of oligopeptides, analogous to the amino and carboxyl terminal parts of 3i, to promote G protein activation, monitored with the [³⁵S]GTPγS assay. Our results reveal that the carboxyl end residues of 3i, R360(6.24) to V372(6.36), are important for G_i/G_o protein activation. Deletions in regions from G206(5.72) to R245(5.110) altered the binding of some α_2B-AR agonists, indicating that agonist binding is dependent on the conformation of the 3i domain, possibly through the involvement of G protein interactions. The truncated receptor constructs may be more stable on purification and thus be useful for structural characterization of α_2B-AR.     

生态产品价值实现模式应用场景及其运行机制——基于典型案例文本数据的实证分析

因地制宜选择实现模式是建立健全生态产品价值实现机制、践行"两山理论"的重要举措。利用自然资源部和生态环境部推介的生态产品价值实现典型案例文本词频数据,采用随机森林分析方法,从自然资本、生态服务和参与主体三个角度,实证总结了不同价值实现模式的应用场景及其运行机制。研究表明:生态产业化经营模式主要是用于农地自然资本,通过市场手段激励农户等经营主体参与,提升农地供给生态产品的功能。生态资源指标及产权交易模式主要是用于林地自然资本,通过生态权益交易,协调公共和私人部门之间的利益,以保护森林的调节服务功能。生态补偿模式主要是用于水域自然资本,通过建立受益者和保护者之间的补偿机制,提升水域的调节和供给服务功能。生态修复及价值提升模式主要是用于废弃工矿用地自然资本,通过政府部门和市场主体联合开展修复整治,提升废弃工矿用地的文化服务和供给功能。研究结果为因地制宜建立生态产品价值实现推进机制提供政策参考。