TAFFEL: Independent Enrichment Analysis of gene sets

<p>Abstract</p> <p>Background</p> <p>A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools.</p> <p>Results</p> <p>We present a novel method called Independent Enrichment Analysis (IEA) and software TAFFEL that eases the task by clustering genes to subgroups using Gene Ontology categories and transcription regulators. IEA indicates transcriptional regulators putatively controlling biological functions in studied condition.</p> <p>Conclusions</p> <p>We demonstrate that the developed method and TAFFEL tool give new insight to the analysis of differentially expressed genes and can generate novel hypotheses. Our comparison to other popular methods showed that the IEA method implemented in TAFFEL can find important biological phenomena, which are not reported by other methods.</p

An ectomycorrhizal fungus alters sensitivity to jasmonate, salicylate, gibberellin, and ethylene in host roots.

The phytohormones jasmonate, gibberellin, salicylate, and ethylene regulate an interconnected reprogramming network integrating root development with plant responses against microbes. The establishment of mutualistic ectomycorrhizal symbiosis requires the suppression of plant defense responses against fungi as well as the modification of root architecture and cortical cell wall properties. Here, we investigated the contribution of phytohormones and their crosstalk to the ontogenesis of ectomycorrhizae (ECM) between grey poplar (Populus tremula x alba) roots and the fungus Laccaria bicolor. To obtain the hormonal blueprint of developing ECM, we quantified the concentrations of jasmonates, gibberellins, and salicylate via liquid chromatography-tandem mass spectrometry. Subsequently, we assessed root architecture, mycorrhizal morphology, and gene expression levels (RNA sequencing) in phytohormone-treated poplar lateral roots in the presence or absence of L. bicolor. Salicylic acid accumulated in mid-stage ECM. Exogenous phytohormone treatment affected the fungal colonization rate and/or frequency of Hartig net formation. Colonized lateral roots displayed diminished responsiveness to jasmonate but regulated some genes, implicated in defense and cell wall remodelling, that were specifically differentially expressed after jasmonate treatment. Responses to salicylate, gibberellin, and ethylene were enhanced in ECM. The dynamics of phytohormone accumulation and response suggest that jasmonate, gibberellin, salicylate, and ethylene signalling play multifaceted roles in poplar L. bicolor ectomycorrhizal development

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Putative cold acclimation pathways in Arabidopsis thaliana identified by a combined analysis of mRNA co-expression patterns, promoter motifs and transcription factors

<p>Abstract</p> <p>Background</p> <p>With the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.</p> <p>Results</p> <p>By means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from ~24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.</p> <p>Conclusion</p> <p>The rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.</p

Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence.

Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation

Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays

Gene transcription in a set of 49 human primary lung adenocarcinomas and 9 normal lung tissue samples was examined using Affymetrix GeneChip technology. A total of 3442 genes, called the set M AD, were found to be either up- or down-regulated by at least 2-fold between the two phenotypes. Genes assigned to a particular gene ontology term were found, in many cases, to be significantly unevenly distributed between the genes in and outside M AD. Terms that were overrepresented in M AD included functions directly implicated in the cancer cell metabolism. Based on their functional roles and expression profiles, genes in M AD were grouped into likely co-regulated gene sets. Highly conserved sequences in the 5 kb region upstream of the genes in these sets were identified with the motif discovery tool, MoDEL. Potential oncogenic transcription factors and their corresponding binding sites were identified in these conserved regions using the TRANSFAC 8.3 database. Several of the transcription factors identified in this study have been shown elsewhere to be involved in oncogenic processes. This study searched beyond phenotypic gene expression profiles in cancer cells, in order to identify the more important regulatory transcription factors that caused these aberrations in gene expressio

Gene discovery in the horned beetle Onthophagus taurus

<p>Abstract</p> <p>Background</p> <p>Horned beetles, in particular in the genus <it>Onthophagus</it>, are important models for studies on sexual selection, biological radiations, the origin of novel traits, developmental plasticity, biocontrol, conservation, and forensic biology. Despite their growing prominence as models for studying both basic and applied questions in biology, little genomic or transcriptomic data are available for this genus. We used massively parallel pyrosequencing (Roche 454-FLX platform) to produce a comprehensive EST dataset for the horned beetle <it>Onthophagus taurus</it>. To maximize sequence diversity, we pooled RNA extracted from a normalized library encompassing diverse developmental stages and both sexes.</p> <p>Results</p> <p>We used 454 pyrosequencing to sequence ESTs from all post-embryonic stages of <it>O. taurus. </it>Approximately 1.36 million reads assembled into 50,080 non-redundant sequences encompassing a total of 26.5 Mbp. The non-redundant sequences match over half of the genes in <it>Tribolium castaneum</it>, the most closely related species with a sequenced genome. Analyses of Gene Ontology annotations and biochemical pathways indicate that the <it>O. taurus </it>sequences reflect a wide and representative sampling of biological functions and biochemical processes. An analysis of sequence polymorphisms revealed that SNP frequency was negatively related to overall expression level and the number of tissue types in which a given gene is expressed. The most variable genes were enriched for a limited number of GO annotations whereas the least variable genes were enriched for a wide range of GO terms directly related to fitness.</p> <p>Conclusions</p> <p>This study provides the first large-scale EST database for horned beetles, a much-needed resource for advancing the study of these organisms. Furthermore, we identified instances of gene duplications and alternative splicing, useful for future study of gene regulation, and a large number of SNP markers that could be used in population-genetic studies of <it>O. taurus </it>and possibly other horned beetles.</p

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer