Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

BackgroundAutomated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.ResultsUsing a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.ConclusionThe study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.<br /

Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration

BACKGROUND: There is an ever increasing rate of data made available on genetic variation, transcriptomes and proteomes. Similarly, a growing variety of bioinformatic programs are becoming available from many diverse sources, designed to identify a myriad of sequence patterns considered to have potential biological importance within inter-genic regions, genes, transcripts, and proteins. However, biologists require easy to use, uncomplicated tools to integrate this information, visualise and print gene annotations. Integrating this information usually requires considerable informatics skills, and comprehensive knowledge of the data format to make full use of this information. Tools are needed to explore gene model variants by allowing users the ability to create alternative transcript models using novel combinations of exons not necessarily represented in current database deposits of mRNA/cDNA sequences. RESULTS: Djinn Lite is designed to be an intuitive program for storing and visually exploring of custom annotations relating to a eukaryotic gene sequence and its modelled gene products. In particular, it is helpful in developing hypothesis regarding alternate splicing of transcripts by allowing the construction of model transcripts and inspection of their resulting translations. It facilitates the ability to view a gene and its gene products in one synchronised graphical view, allowing one to drill down into sequence related data. Colour highlighting of selected sequences and added annotations further supports exploration, visualisation of sequence regions and motifs known or predicted to be biologically significant. CONCLUSION: Gene annotating remains an ongoing and challengingtask that will continue as gene structures, gene transcription repertoires, disease loci, protein products and their interactions become moreprecisely defined. Djinn Lite offers an accessible interface to help accumulate, enrich, and individualise sequence annotations relating to a gene, its transcripts and translations. The mechanism of transcript definition and creation, and subsequent navigation and exploration of features, are very intuitive and demand only a short learning curve. Ultimately, Djinn Lite can form the basis for providing valuable clues to plan new experiments, providing storage of sequences and annotations for dedication to customised projects. The application is appropriate for Windows 98-ME-2000-XP-2003 operating systems

The transcriptional and functional properties of mouse epiblast stem cells resemble the anterior primitive streak.

Mouse epiblast stem cells (EpiSCs) can be derived from a wide range of developmental stages. To characterize and compare EpiSCs with different origins, we derived a series of EpiSC lines from pregastrula stage to late-bud-stage mouse embryos. We found that the transcriptomes of these cells are hierarchically distinct from those of the embryonic stem cells, induced pluripotent stem cells (iPSCs), and epiblast/ectoderm. The EpiSCs display globally similar gene expression profiles irrespective of the original developmental stage of the source tissue. They are developmentally similar to the ectoderm of the late-gastrula-stage embryo and behave like anterior primitive streak cells when differentiated in vitro and in vivo. The EpiSC lines that we derived can also be categorized based on a correlation between gene expression signature and predisposition to differentiate into particular germ-layer derivatives. Our findings therefore highlight distinct identifying characteristics of EpiSCs and provide a foundation for further examination of EpiSC properties and potential

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis

The use of soluble protein structures in modeling helical proteins in a layered membrane

<div><p>Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8 Å to the native structures.</p></div

Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration-0

<p><b>Copyright information:</b></p><p>Taken from "Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration"</p><p>BMC Bioinformatics 2006;7():33-33.</p><p>Published online 23 Jan 2006</p><p>PMCID:PMC1397871.</p><p>Copyright © 2006 Teber et al; licensee BioMed Central Ltd.</p>ferred to as Cyclooxygenase 1 (COX1), PTGS1 is a target for aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs), in particular, for reducing platelet aggregation (GenBank: Chromosome 9 genomic contig – nucleotides 32,450,650-32,481,650). This figure displays a section of the nucleotide annotations page showing a customised list of PTGS1 gene annotations, including predicted promoter elements, transcription factor binding regions, enhancer splicing elements and known SNPs

Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration-1

<p><b>Copyright information:</b></p><p>Taken from "Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration"</p><p>BMC Bioinformatics 2006;7():33-33.</p><p>Published online 23 Jan 2006</p><p>PMCID:PMC1397871.</p><p>Copyright © 2006 Teber et al; licensee BioMed Central Ltd.</p>regions have the colour highlights. Colours red, green, and aqua, respectively, refer to a putative signal peptide, an Epidermal Growth Factor domain, and the splice site junction between Exon 1/Exon2. b) The three varieties of 5'UTRs ([GenBank: NM_080591.1] and two computationally derived alternate splice variants from AltSplice [32]) aligned to the PTGS1 gene sequence (main sequence). Alternate 5' sequences can occur due to differential regulation of upstream promoters and splicing factors, giving rise to different transcription-start sites and splice donor/or acceptor sites [21, 33]. Mfold program [34] predicts a hair-pin RNA secondary structure within 5' UTR(a), represented in red. Secondary structures in the 5'UTR can modulate translation efficiency

Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration-2

<p><b>Copyright information:</b></p><p>Taken from "Djinn Lite: a tool for customised gene transcript modelling, annotation-data enrichment and exploration"</p><p>BMC Bioinformatics 2006;7():33-33.</p><p>Published online 23 Jan 2006</p><p>PMCID:PMC1397871.</p><p>Copyright © 2006 Teber et al; licensee BioMed Central Ltd.</p>to SP3. The sizes of the transcripts and their associated proteins are scaled relative to each other, providing visual insights into the differences between transcripts, and clues to dissimilarities in transcriptional regions and protein domains or motifs. Boxes represent exons and untranslated regions and narrow lines represent introns or non-genic regions. An annotation ruler displays colour bars that feature annotations alongside their corresponding relative location along the transcript. Translations of transcripts are displayed as outlined boxes and are overlaid with colour code bars of annotations relative to the protein. Annotation legends are depicted at the bottom

Extensive Proliferation of Human Cancer Cells with Ever-Shorter Telomeres

Summary: Acquisition of replicative immortality is currently regarded as essential for malignant transformation. This is achieved by activating a telomere lengthening mechanism (TLM), either telomerase or alternative lengthening of telomeres, to counter normal telomere attrition. However, a substantial proportion of some cancer types, including glioblastomas, liposarcomas, retinoblastomas, and osteosarcomas, are reportedly TLM-negative. As serial samples of human tumors cannot usually be obtained to monitor telomere length changes, it has previously been impossible to determine whether tumors are truly TLM-deficient, there is a previously unrecognized TLM, or the assay results are false-negative. Here, we show that a subset of high-risk neuroblastomas (with ∼50% 5-year mortality) lacked significant TLM activity. Cancer cells derived from these highly aggressive tumors initially had long telomeres and proliferated for >200 population doublings with ever-shorter telomeres. This indicates that prevention of telomere shortening is not always required for oncogenesis, which has implications for inhibiting TLMs for cancer therapy. : Dagg et al. find that a subset of highly malignant neuroblastomas (survival ∼50% despite intensive treatment) lack an effective telomere length maintenance mechanism. Their cells undergo continuous telomere shortening throughout >200 population doublings, challenging the concept that activation of a mechanism to prevent telomere shortening is essential for oncogenesis. Keywords: telomeres, ever-shorter telomeres, telomerase, alternative lengthening of telomeres, neuroblastom