A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks

Background Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes. Results By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks. Conclusion wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function

AltAnalyze and DomainGraph: analyzing and visualizing exon expression data

Alternative splicing is an important mechanism for increasing protein diversity. However, its functional effects are largely unknown. Here, we present our new software workflow composed of the open-source application AltAnalyze and the Cytoscape plugin DomainGraph. Both programs provide an intuitive and comprehensive end-to-end solution for the analysis and visualization of alternative splicing data from Affymetrix Exon and Gene Arrays at the level of proteins, domains, microRNA binding sites, molecular interactions and pathways. Our software tools include easy-to-use graphical user interfaces, rigorous statistical methods (FIRMA, MiDAS and DABG filtering) and do not require prior knowledge of exon array analysis or programming. They provide new methods for automatic interpretation and visualization of the effects of alternative exon inclusion on protein domain composition and microRNA binding sites. These data can be visualized together with affected pathways and gene or protein interaction networks, allowing a straightforward identification of potential biological effects due to alternative splicing at different levels of granularity. Our programs are available at http://www.altanalyze.org and http://www.domaingraph.de. These websites also include extensive documentation, tutorials and sample data

Regulation of splicing networks in neurodevelopment

Alternative splicing of pre-mRNA is a critical mechanism for enabling genetic diversity, and is a carefully regulated process in neuronal differentiation. RNA binding proteins (RBPs) are developmentally expressed and physically interact with RNA to drive specific splicing changes. This work tests the hypothesis that RBP-RNA interactions are critical for regulating timed and coordinated alternative splicing changes during neurodevelopment and that these splicing changes are in turn part of major regulatory mechanisms that underlie morphological and functional maturation of neurons. I describe our efforts to identify functional RBP-RNA interactions, including the identification of previously unobserved splicing events, and explore the combinatorial roles of multiple brain-specific RBPs during development. Using integrative modeling that combines multiple sources of data, we find hundreds of regulated splicing events for each of RBFOX, NOVA, PTBP, and MBNL. In the neurodevelopmental context, we find that the proteins control different sets of exons, with RBFOX, NOVA, and PTBP regulating early splicing changes and MBNL largely regulating later splicing changes. These findings additionally led to the observation that CNS and sensory neurons express a variety of different RBP programs, with many sensory neurons expressing a less mature splicing pattern than CNS neurons. We also establish a foundation for further exploration of neurodevelopmental splicing, by investigating the regulation of previously unobserved splicing events

Light Regulation of Alternative Pre-mRNA Splicing in Plants.

Alternative splicing (AS) is a major post-transcriptional mechanism to enhance the diversity of proteome in response to environmental signals. Among the numerous external signals perceived by plants, light is the most crucial one. Plants utilize complex photoreceptor signaling networks to sense different light conditions and adjust their growth and development accordingly. Although light-mediated gene expression has been widely investigated, little is known regarding the mechanism of light affecting AS to modulate mRNA at the post-transcriptional level. In this minireview, we summarize current progresses on how light affects AS, and how sensory photoreceptors and retrograde signaling pathways may coordinately regulate AS of pre-mRNAs. In addition, we also discuss the possibility that AS of the mRNAs encoding photoreceptors may be involved in feedback control of AS. We hypothesize that light regulation of the expression and activity of splicing factors would be a major mechanism of light-mediated AS. The combination of genetic study and high-throughput analyses of AS and splicing complexes in response to light is likely to further advance our understanding of the molecular mechanisms underlying light control of AS and plant development

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

GenMAPP 2: New features and resources for pathway analysis

BACKGROUND: Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses. RESULTS: To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages. CONCLUSION: GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms

INVESTIGATION OF BIOTIC STRESS RESPONSES IN FRUIT TREE CROPS USING META-ANALYTICAL TECHNIQUES.

In recent years, RNA sequencing and analysis using Next Generation Sequencing (NGS) methods have enabled to understand the gene expression pertaining to plant biotic and abiotic stress conditions in both quantitative and qualitative manner. The large number of transcriptomic works published in plants requires more meta-analysis studies that would identify common and specific features in relation of the high number of objective studies performed at different developmental and environmental conditions. Meta-analysis of transcriptomic data will identify commonalities and differences between differentially regulated gene lists and will allow screen which genes are key players in gene-gene and protein-protein interaction networks. These analyses will allow delivering important information on how a specific environmental factor affects plant molecular responses and how plants activate general stress responses to environmental stresses. The identification of common genes between different biotic stress will allow to gain insight into these general responses and help the diagnosis of an early “stress state” of the plants. These analyses help in monitoring stressed plants to start early specific management procedures for each disease or disorder. In this meta-analysis study, I considered all transcriptomic data related to biotic stresses in fruit tree crops, which are already published. The aim was to determine which genes, pathways, gene set categories and predicted protein-protein interaction networks may play key roles in specific responses to pathogen infections