GONUTS: the Gene Ontology Normal Usage Tracking System

The Gene Ontology Normal Usage Tracking System (GONUTS) is a community-based browser and usage guide for Gene Ontology (GO) terms and a community system for general GO annotation of proteins. GONUTS uses wiki technology to allow registered users to share and edit notes on the use of each term in GO, and to contribute annotations for specific genes of interest. By providing a site for generation of third-party documentation at the granularity of individual terms, GONUTS complements the official documentation of the Gene Ontology Consortium. To provide examples for community users, GONUTS displays the complete GO annotations from seven model organisms: Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus and Arabidopsis thaliana. To support community annotation, GONUTS allows automated creation of gene pages for gene products in UniProt. GONUTS will improve the consistency of annotation efforts across genome projects, and should be useful in training new annotators and consumers in the production of GO annotations and the use of GO terms. GONUTS can be accessed at http://gowiki.tamu.edu. The source code for generating the content of GONUTS is available upon request

Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case

A genome modular classification that associates cellular processes to modules could lead to a method to quantify the differences in gene expression levels in different cellular stages or conditions: the transcriptogram, a powerful tool for assessing cell performance, would be at hand. Here we present a computational method to order genes on a line that clusters strongly interacting genes, defining functional modules associated with gene ontology terms. The starting point is a list of genes and a matrix specifying their interactions, available at large gene interaction databases. Considering the Saccharomyces cerevisiae genome we produced a succession of plots of gene transcription levels for a fermentation process. These plots discriminate the fermentation stage the cell is going through and may be regarded as the first versions of a transcriptogram. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms

Formalization of taxon-based constraints to detect inconsistencies in annotation and ontology development

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology project supports categorization of gene products according to their location of action, the molecular functions that they carry out, and the processes that they are involved in. Although the ontologies are intentionally developed to be taxon neutral, and to cover all species, there are inherent taxon specificities in some branches. For example, the process 'lactation' is specific to mammals and the location 'mitochondrion' is specific to eukaryotes. The lack of an explicit formalization of these constraints can lead to errors and inconsistencies in automated and manual annotation.</p> <p>Results</p> <p>We have formalized the taxonomic constraints implicit in some GO classes, and specified these at various levels in the ontology. We have also developed an inference system that can be used to check for violations of these constraints in annotations. Using the constraints in conjunction with the inference system, we have detected and removed errors in annotations and improved the structure of the ontology.</p> <p>Conclusions</p> <p>Detection of inconsistencies in taxon-specificity enables gradual improvement of the ontologies, the annotations, and the formalized constraints. This is progressively improving the quality of our data. The full system is available for download, and new constraints or proposed changes to constraints can be submitted online at <url>https://sourceforge.net/tracker/?atid=605890&group_id=36855</url>.</p

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

Phospho.ELM: a database of phosphorylation sites—update 2008

Phospho.ELM is a manually curated database of eukaryotic phosphorylation sites. The resource includes data collected from published literature as well as high-throughput data sets

PlantGDB: a resource for comparative plant genomics

PlantGDB (http://www.plantgdb.org/) is a genomics database encompassing sequence data for green plants (Viridiplantae). PlantGDB provides annotated transcript assemblies for >100 plant species, with transcripts mapped to their cognate genomic context where available, integrated with a variety of sequence analysis tools and web services. For 14 plant species with emerging or complete genome sequence, PlantGDB's genome browsers (xGDB) serve as a graphical interface for viewing, evaluating and annotating transcript and protein alignments to chromosome or bacterial artificial chromosome (BAC)-based genome assemblies. Annotation is facilitated by the integrated yrGATE module for community curation of gene models. Novel web services at PlantGDB include Tracembler, an iterative alignment tool that generates contigs from GenBank trace file data and BioExtract Server, a web-based server for executing custom sequence analysis workflows. PlantGDB also hosts a plant genomics research outreach portal (PGROP) that facilitates access to a large number of resources for research and training

Sequencing and Analysis of the Mediterranean Amphioxus (Branchiostoma lanceolatum) Transcriptome

BACKGROUND: The basally divergent phylogenetic position of amphioxus (Cephalochordata), as well as its conserved morphology, development and genetics, make it the best proxy for the chordate ancestor. Particularly, studies using the amphioxus model help our understanding of vertebrate evolution and development. Thus, interest for the amphioxus model led to the characterization of both the transcriptome and complete genome sequence of the American species, Branchiostoma floridae. However, recent technical improvements allowing induction of spawning in the laboratory during the breeding season on a daily basis with the Mediterranean species Branchiostoma lanceolatum have encouraged European Evo-Devo researchers to adopt this species as a model even though no genomic or transcriptomic data have been available. To fill this need we used the pyrosequencing method to characterize the B. lanceolatum transcriptome and then compared our results with the published transcriptome of B. floridae. RESULTS: Starting with total RNA from nine different developmental stages of B. lanceolatum, a normalized cDNA library was constructed and sequenced on Roche GS FLX (Titanium mode). Around 1.4 million of reads were produced and assembled into 70,530 contigs (average length of 490 bp). Overall 37% of the assembled sequences were annotated by BlastX and their Gene Ontology terms were determined. These results were then compared to genomic and transcriptomic data of B. floridae to assess similarities and specificities of each species. CONCLUSION: We obtained a high-quality amphioxus (B. lanceolatum) reference transcriptome using a high throughput sequencing approach. We found that 83% of the predicted genes in the B. floridae complete genome sequence are also found in the B. lanceolatum transcriptome, while only 41% were found in the B. floridae transcriptome obtained with traditional Sanger based sequencing. Therefore, given the high degree of sequence conservation between different amphioxus species, this set of ESTs may now be used as the reference transcriptome for the Branchiostoma genus

SchistoDB: a Schistosoma mansoni genome resource

SchistoDB (http://schistoDB.net/) is a genomic database for the parasitic organism Schistosoma mansoni, one of the major causative agents of schistosomiasis worldwide. It currently incorporates sequences and annotation for S. mansoni in a single user-friendly database. Several genomic scale analyses are available as well as ESTs, oligonucleotides, metabolic pathways and drugs. In this article, we describe the data sets and its analyses, how to query the database and tools available in the website

mirConnX: condition-specific mRNA-microRNA network integrator

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface. Two organisms are currently supported: Homo sapiens and Mus musculus. The intuitive user interface and large database make mirConnX a useful tool for clinical scientists for hypothesis generation and explorations. mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx