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PubMed2003

Growing evidence for diabetes susceptibility genes from genome scan data.

Genome-wide scans for linkage have provided one of the dominant approaches adopted by researchers in their efforts to identify genes responsible for the inherited component of type 2 diabetes susceptibility. Around 20 genome scans have now been completed, in a wide variety of populations. Integration of data from these diverse scans has proven far from trivial, but the contours of genome-wide linkage topography are steadily emerging from the fog of data. Identification of the calpain-10 gene as the probable basis for the chromosome 2q linkage seen in Mexican Americans has provided validation of this positional cloning approach. This report provides an update on recent type 2 diabetes genome scan data, focusing on several chromosomal regions where the evidence for linkage has considerably strengthened in the past year. The current and future value of genome-wide linkage information in the search for type 2 diabetes susceptibility effects is also discussed.

Diabetes Mellitus, Type 2↗

PubMed1999

Inferring tree models for oncogenesis from comparative genome hybridization data.

Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer. We derive a tree model inference algorithm based on the idea of a maximum-weight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.

Chromosome Aberrations↗

PubMed2005

SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data.

Comparative genome hybridization (CGH) to DNA microarrays (array CGH) is a technique capable of detecting deletions and duplications in genomes at high resolution. However, array CGH studies of the human genome noting false negative and false positive results using large insert clones as probes have raised important concerns regarding the suitability of this approach for clinical diagnostic applications. Here, we adapt the Smith-Waterman dynamic-programming algorithm to provide a sensitive and robust analytic approach (SW-ARRAY) for detecting copy-number changes in array CGH data. In a blind series of hybridizations to arrays consisting of the entire tiling path for the terminal 2 Mb of human chromosome 16p, the method identified all monosomies between 267 and 1567 kb with a high degree of statistical significance and accurately located the boundaries of deletions in the range 267-1052 kb. The approach is unique in offering both a nonparametric segmentation procedure and a nonparametric test of significance. It is scalable and well-suited to high resolution whole genome array CGH studies that use array probes derived from large insert clones as well as PCR products and oligonucleotides.

Algorithms↗

PubMed1999

A method for meta-analysis of genome searches: application to simulated data.

Genome searches have been performed for many complex traits, and in some cases several searches have been performed in a single disease. Replication of significant results is rare, and a systematic method of reviewing results from a number of searches is needed. A method for meta-analysis is presented which provides a systematic descriptive overview of the separate analyses while dealing with some of the problems specific to meta-analysis of genome searches. The results of two separate meta-analyses correctly indicate the presence of susceptibility loci on chromosomes 1, 3, and 5 in the GAW11 Problem 2 data.

Genetic Linkage↗

PubMed1998

DNA Data Bank of Japan at work on genome sequence data.

We at the DNA Data Bank of Japan (DDBJ) (http://www.ddbj.nig.ac.jp) have recently begun receiving, processing and releasing EST and genome sequence data submitted by various Japanese genome projects. The data include those for human, Arabidopsis thaliana, rice, nematode, Synechocystis sp. and Escherichia coli. Since the quantity of data is very large, we organized teams to conduct preliminary discussions with project teams about data submission and handling for release to the public. We also developed a mass submission tool to cope with a large quantity of data. In addition, to provide genome data on WWW, we developed a genome information system using Java. This system (http://mol.genes.nig.ac.jp/ecoli/) can in theory be used for any genome sequence data. These activities will facilitate processing of large quantities of EST and genome data.

Animals↗

PubMed2006

CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data.

BACKGROUND: Comparative genomic hybridization can rapidly identify chromosomal regions that vary between organisms and tissues. This technique has been applied to detecting differences between normal and cancerous tissues in eukaryotes as well as genomic variability in microbial strains and species. The density of oligonucleotide probes available on current microarray platforms is particularly well-suited for comparisons of organisms with smaller genomes like bacteria and yeast where an entire genome can be assayed on a single microarray with high resolution. Available methods for analyzing these experiments typically confine analyses to data from pre-defined annotated genome features, such as entire genes. Many of these methods are ill suited for datasets with the number of measurements typical of high-density microarrays. RESULTS: We present an algorithm for analyzing microarray hybridization data to aid identification of regions that vary between an unsequenced genome and a sequenced reference genome. The program, CGHScan, uses an iterative random walk approach integrating multi-layered significance testing to detect these regions from comparative genomic hybridization data. The algorithm tolerates a high level of noise in measurements of individual probe intensities and is relatively insensitive to the choice of method for normalizing probe intensity values and identifying probes that differ between samples. When applied to comparative genomic hybridization data from a published experiment, CGHScan identified eight of nine known deletions in a Brucella ovis strain as compared to Brucella melitensis. The same result was obtained using two different normalization methods and two different scores to classify data for individual probes as representing conserved or variable genomic regions. The undetected region is a small (58 base pair) deletion that is below the resolution of CGHScan given the array design employed in the study. CONCLUSION: CGHScan is an effective tool for analyzing comparative genomic hybridization data from high-density microarrays. The algorithm is capable of accurately identifying known variable regions and is tolerant of high noise and varying methods of data preprocessing. Statistical analysis is used to define each variable region providing a robust and reliable method for rapid identification of genomic differences independent of annotated gene boundaries.

Algorithms↗

PubMed2006

Genolevures complete genomes provide data and tools for comparative genomics of hemiascomycetous yeasts.

The Génolevures online database (http://cbi.labri.fr/Genolevures/) provides tools and data relative to 4 complete and 10 partial genome sequences determined and manually annotated by the Génolevures Consortium, to facilitate comparative genomic studies of hemiascomycetous yeasts. With their relatively small and compact genomes, yeasts offer a unique opportunity for exploring eukaryotic genome evolution. The new version of the Génolevures database provides truly complete (subtelomere to subtelomere) chromosome sequences, 25 000 protein-coding and tRNA genes, and in silico analyses for each gene element. A new feature of the database is a novel collection of conserved multi-species protein families and their mapping to metabolic pathways, coupled with an advanced search feature. Data are presented with a focus on relations between genes and genomes: conservation of genes and gene families, speciation, chromosomal reorganization and synteny. The Génolevures site includes an area for specific studies by members of its international community.

Databases, Genetic↗

PubMed2000

Accurate prediction of protein functional class from sequence in the Mycobacterium tuberculosis and Escherichia coli genomes using data mining.

The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on the M. tuberculosis and E. coli genomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function in M. tuberculosis and 24% of those in E. coli, with an estimated accuracy of 60-80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history of M. tuberculosis and E. coli.

Amino Acid Sequence↗

PubMed1997

Data note system for capturing laboratory data.

The complexity of genome data limits the usefulness of traditional database management systems. The highly interconnected structure of genome data can be captured in a data representation language based on the mathematical formalism of graphs. We have tailored graphs for describing genome data and have developed a database management system, called the Data Note System, for developing small databases to capture data from genome laboratories. To simplify the use of the Data Note System, a series of tools with graphical user interfaces has been developed. The system is designed to be easy to install and use by novice database developers with a minimal amount of computer expertise. We describe the tools and present examples of their use. The system consists of a storage facility, a schema editing tool to simplify the design of small databases, and three tools for data entry and querying.

Computers↗

PubMed1999

Serpins in the Caenorhabditis elegans genome.

Data mining in genome sequences can identify distant homologues of known protein families, and is most powerful if solved structures are available to reveal the three-dimensional implications of very dissimilar sequences. Here we describe putative serpin sequences identified with very high statistical significance in the Caenorhabditis elegans genome. When mapped onto vertebrate serpins such as alpha1-antitrypsin, they suggest novel structural features. Some appear complete, some show extensive deletions, and others appear to contain only the C-terminal part of the known serpin fold, probably in partnership with N-terminal regions that have conformations unlike those of known serpins. The observation of such striking sequence similarity, in proteins that must have significantly different overall structures, substantially extends the structural characteristics of the serpin family of proteins.

Amino Acid Sequence↗

PubMed2005

The implications of electronic health record for personalized medicine.

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.

Computational Biology↗

PubMed2004

Reclassification of Mesoplasma pleciae as Acholeplasma pleciae comb. nov. on the basis of 16S rRNA and gyrB gene sequence data.

Genomic DNA sequence data for the 16S rRNA gene and the gyrB gene of Mesoplasma pleciae PS-1(T) (=ATCC 49582(T)=NBRC 100476(T)) demonstrate a much closer relationship to Acholeplasma laidlawii and Acholeplasma oculi than to other species in the order Entomoplasmatales. In addition, the preferred use of UGG rather than UGA as the codon for tryptophan in the gyrB sequence probably places the organism outside the order Entomoplasmatales. It is proposed that M. pleciae be reclassified in the genus Acholeplasma, as Acholeplasma pleciae comb. nov.

Acholeplasma↗

PubMed2000

Detection of deleted genomic DNA using a semiautomated computational analysis of GeneChip data.

Genomic diversity within and between populations is caused by single nucleotide mutations, changes in repetitive DNA systems, recombination mechanisms, and insertion and deletion events. The contribution of these sources to diversity, whether purely genetic or of phenotypic consequence, can only be investigated if we have the means to quantitate and characterize diversity in many samples. With the advent of complete sequence characterization of representative genomes of different species, the possibility of developing protocols to screen for genetic polymorphism across entire genomes is actively being pursued. The large numbers of measurements such approaches yield demand that we pay careful attention to the numerical analysis of data. In this paper we present a novel application of an Affymetrix GeneChip to perform genome-wide screens for deletion polymorphism. A high-density oligonucleotide array formatted for mRNA expression and targeted at a fully sequenced 4.4-million-base pair Mycobacterium tuberculosis standard strain genome was adapted to compare genomic DNA. Hybridization intensities to 111,000 probe pairs (perfect complement and mismatch complement) were measured for genomic DNA from a clinical strain and from a vaccine organism. Because individual probe-pair hybridization intensities exhibit limited sensitivity/specificity characteristics to detect deletions, data-analytical methodology to exploit measurements from multiple probes in tandem locations across the genome was developed. The TSTEP (Tandem Set Terminal Extreme Probability) algorithm designed specifically to analyze the tandem hybridization measurements data was applied and shown to discover genomic deletions with high sensitivity. The TSTEP algorithm provides a foundation for similar efforts to characterize deletions in many hybridization measures in similar-sized and larger genomes. Issues relating to the design of genome content screening experiments and the implications of these methods for studying population genomics and the evolution of genomes are discussed.

Algorithms↗

PubMed2025

AQuA Tools: clear and reliable BEDPE operations for 3D genomics.

MOTIVATION: The genome interacts with itself within the volume of the cell nucleus to process information. These interactions mediate signal integration, gene regulation, and cell identity. The identification of new therapeutic targets from non-coding disease-associated variants relies critically on correctly assigning variants to genes through 3D interactions. Experimental techniques in 3D genomics, such as HiC and HiChIP, allow the mapping of interactions through sequencing. Bioinformatics for 3D genomics contends primarily with contact matrices that contain interaction frequencies for all possible element pairs, and BEDPE files that store element pairs that interact. Whereas the tools available for processing linear genomic data are mature, operating on contact matrices and BEDPE files remains cumbersome, opaque, and error-prone, as researchers have had to shoehorn tools originally designed for linear data. A genome arithmetic designed from the ground up for 3D genomics does not yet exist. RESULTS: We present AQuA Tools, a suite of shell- and R-based command-line tools that provide a set of core operations on contact matrices and BEDPE files motivated by key questions in population genetics, cancer research, and precision medicine. We have designed our core operations to be clear, reliable, intuitive and versatile. Core operations can be chained together along with standard UNIX commands. Our goal is to make AQuA Tools easy for the novice to learn and the go-to choice for power users. We hope our tools will motivate more researchers to use 3D genomic data in their projects. AVAILABILITY AND IMPLEMENTATION: We provide and maintain AQuA Tools at https://github.com/axiotl/aqua-tools.

Genomics↗

PubMed2005

Advances in phylogeny reconstruction from gene order and content data.

Genomes can be viewed in terms of their gene content and the order in which the genes appear along each chromosome. Evolutionary events that affect the gene order or content are "rare genomic events" (rarer than events that affect the composition of the nucleotide sequences) and have been advocated by systematists for inferring deep evolutionary histories. This chapter surveys recent developments in the reconstruction of phylogenies from gene order and content, focusing on their performance under various stochastic models of evolution. Because such methods are quite restricted in the type of data they can analyze, we also present research aimed at handling the full range of whole-genome data.

Algorithms↗

PubMed2005

Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data.

Genome wide association studies using high throughput technology are already being conducted despite the significant hurdles that need to be overcome (Nat Rev Genet 6:95-108, 2005; Nat Rev Genet 6:109-118, 2005). Methods for detecting haplotype association signals in genome wide haplotype datasets are as yet very limited. Much methodological research has already been devoted to linkage disequilibrium (LD) fine mapping where the focus is the identification of the disease locus rather than the detection of a disease signal. Applications of these approaches to genome wide scanning are limited by the strong model assumptions of the sharing process, which lead to computational complexity. We describe a new algorithm for the initial identification of disease susceptibility loci in genome wide haplotype association studies. Excess sharing of ancestral haplotypes, which indicates the presence of a disease locus, is detected with a simple, easy to interpret, chi2 based statistic. The method allows genome wide scanning for qualitative traits within reasonable computational timeframes and can serve as a first pass analysis prior to the usage of likelihood based methods, providing candidate regions and inferred susceptibility haplotypes. Our method makes no assumptions regarding the population history or the pattern of background LD. Statistical significance is evaluated with permutation tests. The method is illustrated on simulated and real data where it is applied to simple (cystic fibrosis) and complex disease (multiple sclerosis) examples. The statistic has low type I error and greater power to map disease loci over conventional single marker tests for low to moderate levels of LD.

Algorithms↗

PubMed2004

SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data.

BACKGROUND: Array comparative genomic hybridization (CGH) is a technique which detects copy number differences in DNA segments. Complete sequencing of the human genome and the development of an array representing a tiling set of tens of thousands of DNA segments spanning the entire human genome has made high resolution copy number analysis throughout the genome possible. Since array CGH provides signal ratio for each DNA segment, visualization would require the reassembly of individual data points into chromosome profiles. RESULTS: We have developed a visualization tool for displaying whole genome array CGH data in the context of chromosomal location. SeeGH is an application that translates spot signal ratio data from array CGH experiments to displays of high resolution chromosome profiles. Data is imported from a simple tab delimited text file obtained from standard microarray image analysis software. SeeGH processes the signal ratio data and graphically displays it in a conventional CGH karyotype diagram with the added features of magnification and DNA segment annotation. In this process, SeeGH imports the data into a database, calculates the average ratio and standard deviation for each replicate spot, and links them to chromosome regions for graphical display. Once the data is displayed, users have the option of hiding or flagging DNA segments based on user defined criteria, and retrieve annotation information such as clone name, NCBI sequence accession number, ratio, base pair position on the chromosome, and standard deviation. CONCLUSIONS: SeeGH represents a novel software tool used to view and analyze array CGH data. The software gives users the ability to view the data in an overall genomic view as well as magnify specific chromosomal regions facilitating the precise localization of genetic alterations. SeeGH is easily installed and runs on Microsoft Windows 2000 or later environments.

Chromosome Mapping↗

PubMed2005

BioMediator data integration: beyond genomics to neuroscience data.

The BioMediator system developed at the University of Washington (UW) provides a theoretical and practical foundation for data integration across diverse biomedical research domains and various data types. In this paper we demonstrate the generalizability of its architecture through its application to the UW Human Brain Project (HBP) for understanding language organization in the brain. We first describe the system architecture and the characteristics of the four data sources developed by the UW HBP. Second we present the process of developing the application prototype for HBP neuroscience researchers posing queries across these semantically and syntactically heterogeneous neurophysiologic data sources. Then we discuss the benefits and potential limitations of the BioMediator system as a general data integration solution for different user groups in genomic and neuroscience research domains.

Brain↗