One of the key questions about genomic alterations in cancer is whether they are functional in the sense of contributing to the selective advantage of tumor cells. The frequency with which an alteration occurs might reflect its ability to increase cancer cell growth, or alternatively, enhanced instability of a locus may increase the frequency with which it is found to be aberrant in tumors, regardless of oncogenic impact. Here we’ve addressed this on a genome-wide scale for cancer-associated focal deletions, which are known to pinpoint both tumor suppressor genes (tumor suppressors) and unstable loci. Based on DNA copy number analysis of over one-thousand human cancers representing ten different tumor types, we observed five loci with focal deletion frequencies above 5%, including the A2BP1 gene at 16p13.3 and the MACROD2 gene at 20p12.1. However, neither RNA expression nor functional studies support a tumor suppressor role for either gene. Further analyses suggest instead that these are sites of increased genomic instability and that they resemble common fragile sites (CFS). Genome-wide analysis revealed properties of CFS-like recurrent deletions that distinguish them from deletions affecting tumor suppressor genes, including their isolation at specific loci away from other genomic deletion sites, a considerably smaller deletion size, and dispersal throughout the affected locus rather than assembly at a common site of overlap. Additionally, CFS-like deletions have less impact on gene expression and are enriched in cell lines compared to primary tumors. We show that loci affected by CFS-like deletions are often distinct from known common fragile sites. Indeed, we find that each tumor tissue type has its own spectrum of CFS-like deletions, and that colon cancers have many more CFS-like deletions than other tumor types. We present simple rules that can pinpoint focal deletions that are not CFS-like and more likely to affect functional tumor suppressors.

High-throughput transcriptomics technologies have been widely used to study plant transcriptional reprogramming during the process of plant defense responses, and a large quantity of gene expression data have been accumulated in public repositories. However, utilization of these data is often hampered by the lack of standard metadata annotation. In this study, we curated 2444 public pathogenesis-related gene expression samples from the model plant Arabidopsis and three major crops (maize, rice, and wheat). We organized the data into a user-friendly database termed as PlaD. Currently, PlaD contains three key features. First, it provides large-scale curated data related to plant defense responses, including gene expression and gene functional annotation data. Second, it provides the visualization of condition-specific expression profiles. Third, it allows users to search co-regulated genes under the infections of various pathogens. Using PlaD, we conducted a large-scale transcriptome analysis to explore the global landscape of gene expression in the curated data. We found that only a small fraction of genes were differentially expressed under multiple conditions, which might be explained by their tendency of having more network connections and shorter network distances in gene networks. Collectively, we hope that PlaD can serve as an important and comprehensive knowledgebase to the community of plant sciences, providing insightful clues to better understand the molecular mechanisms underlying plant immune responses. PlaD is freely available at http://systbio.cau.edu.cn/plad/index.php or http://zzdlab.com/plad/index.php.

Background: Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. Description: PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. Conclusion: PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.

Alternative splicing (AS) is an essential co-transcriptional regulatory mechanism in eukaryotes. The accumulation of plant RNA-Seq data provides an unprecedented opportunity to investigate the global landscape of plant AS events. However, most existing AS identification tools were originally designed for animals, and their performance in plants was not rigorously benchmarked. In this work, we developed a simple and easy-to-use bioinformatics tool named ASTool for detecting AS events from plant RNA-Seq data. As an exon-based method, ASTool can detect 4 major AS types, including intron retention (IR), exon skipping (ES), alternative 5′ splice sites (A5SS), and alternative 3′ splice sites (A3SS). Compared with existing tools, ASTool revealed a favorable performance when tested in simulated RNA-Seq data, with both recall and precision values exceeding 95% in most cases. Moreover, ASTool also showed a competitive computational speed and consistent detection results with existing tools when tested in simulated or real plant RNA-Seq data. Considering that IR is the most predominant AS type in plants, ASTool allowed the detection and visualization of novel IR events based on known splice sites. To fully present the functionality of ASTool, we also provided an application example of ASTool in processing real RNA-Seq data of Arabidopsis in response to heat stress.

Background Alternative splicing (AS) is a co-transcriptional regulatory mechanism of plants in response to environmental stress. However, the role of AS in biotic and abiotic stress responses remains largely unknown. To speed up our understanding of plant AS patterns under different stress responses, development of informative and comprehensive plant AS databases is highly demanded. Description In this study, we first collected 3,255 RNA-seq data under biotic and abiotic stresses from two important model plants (Arabidopsis and rice). Then, we conducted AS event detection and gene expression analysis, and established a user-friendly plant AS database termed PlaASDB. By using representative samples from this highly integrated database resource, we compared AS patterns between Arabidopsis and rice under abiotic and biotic stresses, and further investigated the corresponding difference between AS and gene expression. Specifically, we found that differentially spliced genes (DSGs) and differentially expressed genes (DEG) share very limited overlapping under all kinds of stresses, suggesting that gene expression regulation and AS seemed to play independent roles in response to stresses. Compared with gene expression, Arabidopsis and rice were more inclined to have conserved AS patterns under stress conditions. Conclusion PlaASDB is a comprehensive plant-specific AS database that mainly integrates the AS and gene expression data of Arabidopsis and rice in stress response. Through large-scale comparative analyses, the global landscape of AS events in Arabidopsis and rice was observed. We believe that PlaASDB could help researchers understand the regulatory mechanisms of AS in plants under stresses more conveniently. PlaASDB is freely accessible at http://zzdlab.com/PlaASDB/ASDB/index.html.