MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia.

MAGIA2 (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA2 performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA2 tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target.

BackgroundThe Manila clam, Ruditapes philippinarum, is one of the major aquaculture species in the world and a potential sentinel organism for monitoring the status of marine ecosystems. However, genomic resources for R. philippinarum are still extremely limited. Global analysis of gene expression profiles is increasingly used to evaluate the biological effects of various environmental stressors on aquatic animals under either artificial conditions or in the wild. Here, we report on the development of a transcriptomic platform for global gene expression profiling in the Manila clam.ResultsA normalized cDNA library representing a mixture of adult tissues was sequenced using a ultra high-throughput sequencing technology (Roche 454). A database consisting of 32,606 unique transcripts was constructed, 9,747 (30%) of which could be annotated by similarity. An oligo-DNA microarray platform was designed and applied to profile gene expression of digestive gland and gills. Functional annotation of differentially expressed genes between different tissues was performed by enrichment analysis. Expression of Natural Antisense Transcripts (NAT) analysis was also performed and bi-directional transcription appears a common phenomenon in the R. philippinarum transcriptome. A preliminary study on clam samples collected in a highly polluted area of the Venice Lagoon demonstrated the applicability of genomic tools to environmental monitoring.ConclusionsThe transcriptomic platform developed for the Manila clam confirmed the high level of reproducibility of current microarray technology. Next-generation sequencing provided a good representation of the clam transcriptome. Despite the known limitations in transcript annotation and sequence coverage for non model species, sufficient information was obtained to identify a large set of genes potentially involved in cellular response to environmental stress.

Background: Improvements in genome sequence annotation revealed discrepancies in the original probeset/gene assignment in Affymetrix microarray and the existence of differences between annotations and effective alignments of probes and transcription products. In the current generation of Affymetrix human GeneChips, most probesets include probes matching transcripts from more than one gene and probes which do not match any transcribed sequence.

BackgroundSeveral tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.ResultsTRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.ConclusionsTRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.

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BackgroundOnce highly abundant, the European eel (Anguilla anguilla L.; Anguillidae; Teleostei) is considered to be critically endangered and on the verge of extinction, as the stock has declined by 90-99% since the 1980s. Yet, the species is poorly characterized at molecular level with little sequence information available in public databases.ResultsThe first European eel transcriptome was obtained by 454 FLX Titanium sequencing of a normalized cDNA library, produced from a pool of 18 glass eels (juveniles) from the French Atlantic coast and two sites in the Mediterranean coast. Over 310,000 reads were assembled in a total of 19,631 transcribed contigs, with an average length of 531 nucleotides. Overall 36% of the contigs were annotated to known protein/nucleotide sequences and 35 putative miRNA identified.ConclusionsThis study represents the first transcriptome analysis for a critically endangered species. EeelBase, a dedicated database of annotated transcriptome sequences of the European eel is freely available at http://compgen.bio.unipd.it/eeelbase. Considering the multiple factors potentially involved in the decline of the European eel, including anthropogenic factors such as pollution and human-introduced diseases, our results will provide a rich source of data to discover and identify new genes, characterize gene expression, as well as for identification of genetic markers scattered across the genome to be used in various applications.

BackgroundSturgeons are a group of Condrostean fish with very high evolutionary, economical and conservation interest. The eggs of these living fossils represent one of the most high prized foods of animal origin. The intense fishing pressure on wild stocks to harvest caviar has caused in the last decades a dramatic decline of their distribution and abundance leading the International Union for Conservation of Nature to list them as the more endangered group of species. As a direct consequence, world-wide efforts have been made to develop sturgeon aquaculture programmes for caviar production. In this context, the characterization of the genes involved in sex determination could provide relevant information for the selective farming of the more profitable females.ResultsThe 454 sequencing of two cDNA libraries from the gonads and brain of one male and one female full-sib A. naccarii, yielded 182,066 and 167,776 reads respectively, which, after strict quality control, were iterative assembled into more than 55,000 high quality ESTs. The average per-base coverage reached by assembling the two libraries was 4X. The multi-step annotation process resulted in 16% successfully annotated sequences with GO terms. We screened the transcriptome for 32 sex-related genes and highlighted 7 genes that are potentially specifically expressed, 5 in male and 2 in females, at the first life stage at which sex is histologically identifiable. In addition we identified 21,791 putative EST-linked SNPs and 5,295 SSRs.ConclusionsThis study represents the first large massive release of sturgeon transcriptome information that we organized into the public database AnaccariiBase, which is freely available at http://compgen.bio.unipd.it/anaccariibase/. This transcriptomic data represents an important source of information for further studies on sturgeon species. The hundreds of putative EST-linked molecular makers discovered in this study will be invaluable for sturgeon reintroduction and breeding programs.