BEReX: Biomedical Entity-Relationship eXplorer

Biomedical Entity Explorer: A Web Server for Biomedical Entity Exploration

Biomedical Entity Explorer (BEE) is a web server that can search for biomedical entities from a database of six biomedical entity types (gene, miRNA, drug, disease, single nucleotide polymorphism [SNP], pathway) and their gene associations. The search results can be explored using intersections, unions, and negations. BEE has integrated biomedical entities from 16 databases (Ensemble, PharmGKB, Genetic Home Reference, Tarbase, Mirbase, NCI Thesaurus, DisGeNET, Linked life data, UMLS, GSEA MsigDB, Reactome, KEGG, Gene Ontology, HGVD, SNPedia, and dbSNP) based on their gene associations and built a database with their synonyms, descriptions, and links containing individual details. Users can enter the keyword of one or more entities and select the type of entity for which they want to know the relationship for and by using set operations such as union, negation, and intersection, they can navigate the search results more clearly. We believe that BEE will not only be useful for biologists querying for complex associations between entities, but can also be a good starting point for general users searching for biomedical entities. BEE is accessible at (http://bike-bee.snu.ac.kr).

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.

BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature

As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have an advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user’s query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.

HiPub: translating PubMed and PMC texts to networks for knowledge discovery

We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations.

AVAILABILITY AND IMPLEMENTATION

HiPub and detailed user guide are available at http://hipub.korea.ac.kr

CONTACT

kangj@korea.ac.kr, aikchoon.tan@ucdenver.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.