K2/Kleisli and GUS: Experiments in Integrated Access to Genomic Data Sources

The integration of heterogeneous data sources and software systems is a major issue in the biomed ical community and several approaches have been explored: linking databases, on-the- fly integration through views, and integration through warehousing. In this paper we report on our experiences with two systems that were developed at the University of Pennsylvania: an integration system called K2, which has primarily been used to provide views over multiple external data sources and software systems; and a data warehouse called GUS which downloads, cleans, integrates and annotates data from multiple external data sources. Although the view and warehouse approaches each have their advantages, there is no clear winner . Therefore, users must consider how the data is to be used, what the performance guarantees must be, and how much programmer time and expertise is available to choose the best strategy for a particular application

Integrating computationally assembled mouse transcript sequences with the Mouse Genome Informatics (MGI) database

Databases of experimentally generated and computationally derived transcript sequences are valuable resources for genome analysis and annotation. The utility of such databases is enhanced when the sequences they contain are integrated with such biological information as genomic location, gene function, gene expression and phenotypic variation. We present the analysis and results of a semi-automated process of connecting transcript assemblies with highly curated biological information for mouse genes that is available through the Mouse Genome Informatics (MGI) database

Local Admixture of Amplified and Diversified Secreted Pathogenesis Determinants Shapes Mosaic \u3cem\u3eToxoplasma gondii\u3c/em\u3e Genomes

Toxoplasma gondii is among the most prevalent parasites worldwide, infecting many wild and domestic animals and causing zoonotic infections in humans. T. gondii differs substantially in its broad distribution from closely related parasites that typically have narrow, specialized host ranges. To elucidate the genetic basis for these differences, we compared the genomes of 62 globally distributed T. gondii isolates to several closely related coccidian parasites. Our findings reveal that tandem amplification and diversification of secretory pathogenesis determinants is the primary feature that distinguishes the closely related genomes of these biologically diverse parasites. We further show that the unusual population structure of T. gondii is characterized by clade-specific inheritance of large conserved haploblocks that are significantly enriched in tandemly clustered secretory pathogenesis determinants. The shared inheritance of these conserved haploblocks, which show a different ancestry than the genome as a whole, may thus influence transmission, host range and pathogenicity

GeneDB--an annotation database for pathogens.

GeneDB (http://www.genedb.org) is a genome database for prokaryotic and eukaryotic pathogens and closely related organisms. The resource provides a portal to genome sequence and annotation data, which is primarily generated by the Pathogen Genomics group at the Wellcome Trust Sanger Institute. It combines data from completed and ongoing genome projects with curated annotation, which is readily accessible from a web based resource. The development of the database in recent years has focused on providing database-driven annotation tools and pipelines, as well as catering for increasingly frequent assembly updates. The website has been significantly redesigned to take advantage of current web technologies, and improve usability. The current release stores 41 data sets, of which 17 are manually curated and maintained by biologists, who review and incorporate data from the scientific literature, as well as other sources. GeneDB is primarily a production and annotation database for the genomes of predominantly pathogenic organisms

EuPathDB: the eukaryotic pathogen genomics database resource

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions

EuPathDB: the eukaryotic pathogen database

ABSTRACT EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNAseq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010

HFR1 Is Crucial for Transcriptome Regulation in the Cryptochrome 1-Mediated Early Response to Blue Light in Arabidopsis thaliana

Cryptochromes are blue light photoreceptors involved in development and circadian clock regulation. They are found in both eukaryotes and prokaryotes as light sensors. Long Hypocotyl in Far-Red 1 (HFR1) has been identified as a positive regulator and a possible transcription factor in both blue and far-red light signaling in plants. However, the gene targets that are regulated by HFR1 in cryptochrome 1 (cry1)-mediated blue light signaling have not been globally addressed. We examined the transcriptome profiles in a cry1- and HFR1-dependent manner in response to 1 hour of blue light. Strikingly, more than 70% of the genes induced by blue light in an HFR1-dependent manner were dependent on cry1, and vice versa. High overrepresentation of W-boxes and OCS elements were found in these genes, indicating that this strong cry1 and HFR1 co-regulation on gene expression is possibly through these two cis-elements. We also found that cry1 was required for maintaining the HFR1 protein level in blue light, and that the HFR1 protein level is strongly correlated with the global gene expression pattern. In summary, HFR1, which is fine-tuned by cry1, is crucial for regulating global gene expression in cry1-mediated early blue light signaling, especially for the function of genes containing W-boxes and OCS elements

The Oxytricha trifallax Mitochondrial Genome

The Oxytricha trifallax mitochondrial genome contains the largest sequenced ciliate mitochondrial chromosome (∼70 kb) plus a ∼5-kb linear plasmid bearing mitochondrial telomeres. We identify two new ciliate split genes (rps3 and nad2) as well as four new mitochondrial genes (ribosomal small subunit protein genes: rps- 2, 7, 8, 10), previously undetected in ciliates due to their extreme divergence. The increased size of the Oxytricha mitochondrial genome relative to other ciliates is primarily a consequence of terminal expansions, rather than the retention of ancestral mitochondrial genes. Successive segmental duplications, visible in one of the two Oxytricha mitochondrial subterminal regions, appear to have contributed to the genome expansion. Consistent with pseudogene formation and decay, the subtermini possess shorter, more loosely packed open reading frames than the remainder of the genome. The mitochondrial plasmid shares a 251-bp region with 82% identity to the mitochondrial chromosome, suggesting that it most likely integrated into the chromosome at least once. This region on the chromosome is also close to the end of the most terminal member of a series of duplications, hinting at a possible association between the plasmid and the duplications. The presence of mitochondrial telomeres on the mitochondrial plasmid suggests that such plasmids may be a vehicle for lateral transfer of telomeric sequences between mitochondrial genomes. We conjecture that the extreme divergence observed in ciliate mitochondrial genomes may be due, in part, to repeated invasions by relatively error-prone DNA polymerase-bearing mobile elements