Background Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets. Results We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 samples and has several new useful functions. First, GENT2 now provides gene expression across 72 different tissues compared to 57 in GENT. Second, with increasing importance of tumor subtypes, GENT2 provides an option to study the differential expression and its prognostic significance based on tumor subtypes. Third, whenever available, GENT2 provides prognostic information of a gene of interest. Fourth, GENT2 provides a meta-analysis of survival information to provide users more reliable prognostic value of a gene of interest. Conclusions In conclusion, with these significant improvements, GENT2 will continue to be a useful tool to a wide range of researchers. GENT2 is freely available at http://gent2.appex.kr . Electronic supplementary material The online version of this article (10.1186/s12920-019-0514-7) contains supplementary material, which is available to authorized users.

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Recently, conjoined genes (CGs) have emerged as important genetic factors necessary for understanding the human genome. However, their formation mechanism and precise structures have remained mysterious. Based on a detailed structural analysis of 57 human CG transcript variants (CGTVs, discovered in this study) and all (833) known CGs in the human genome, we discovered that the poly(A) signal site from the upstream parent gene region is completely removed via the skipping or truncation of the final exon; consequently, CG transcription is terminated at the poly(A) signal site of the downstream parent gene. This result led us to propose a novel mechanism of CG formation: the complete removal of the poly(A) signal site from the upstream parent gene is a prerequisite for the CG transcriptional machinery to continue transcribing uninterrupted into the intergenic region and downstream parent gene. The removal of the poly(A) signal sequence from the upstream gene region appears to be caused by a deletion or truncation mutation in the human genome rather than post-transcriptional trans-splicing events. With respect to the characteristics of CG sequence structures, we found that intergenic regions are hot spots for novel exon creation during CGTV formation and that exons farther from the intergenic regions are more highly conserved in the CGTVs. Interestingly, many novel exons newly created within the intergenic and intragenic regions originated from transposable element sequences. Additionally, the CGTVs showed tumor tissue-biased expression. In conclusion, our study provides novel insights into the CG formation mechanism and expands the present concepts of the genetic structural landscape, gene regulation, and gene formation mechanisms in the human genome.