Search

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern ‘omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines, including those targeting seasonal influenza, yellow fever, and hepatitis B. These data are made available to the broader scientific community through the ImmuneSpace data portal and analysis engine leveraging the NIH/NIAID ImmPort repository1,2. However, a barrier to progress in this area is that comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines and includes transcriptional profiles and antibody response measurements. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.

International audience; Background: β-lactam antibiotics are associated with a variety of immune-mediated or hypersensitivity reactions, including immediate (type I) reactions mediated by antigen-specific IgE.Objective: We sought to identify genetic predisposing factors for immediate reactions to β-lactam antibiotics.Methods: Patients with a clinical history of immediate hypersensitivity reactions to either penicillins or cephalosporins, which were immunologically confirmed, were recruited from allergy clinics. A genome-wide association study was conducted on 662 patients (the discovery cohort) with a diagnosis of immediate hypersensitivity and the main finding was replicated in a cohort of 98 Spanish cases, recruited using the same diagnostic criteria as the discovery cohort.Results: Genome-wide association study identified rs71542416 within the Class II HLA region as the top hit (P = 2 × 10-14); this was in linkage disequilibrium with HLA-DRB1∗10:01 (odds ratio, 2.93; P = 5.4 × 10-7) and HLA-DQA1∗01:05 (odds ratio, 2.93, P = 5.4 × 10-7). Haplotype analysis identified that HLA-DRB1∗10:01 was a risk factor even without the HLA-DQA1∗01:05 allele. The association with HLA-DRB1∗10:01 was replicated in another cohort, with the meta-analysis of the discovery and replication cohorts showing that HLA-DRB1∗10:01 increased the risk of immediate hypersensitivity at a genome-wide level (odds ratio, 2.96; P = 4.1 × 10-9). No association with HLA-DRB1∗10:01 was identified in 268 patients with delayed hypersensitivity reactions to β-lactams.Conclusions: HLA-DRB1∗10:01 predisposed to immediate hypersensitivity reactions to penicillins. Further work to identify other predisposing HLA and non-HLA loci is required.

Carbamazepine (CBZ) causes life-threating T-cell-mediated hypersensitivity reactions, including serious cutaneous adverse reactions (SCARs) and drug-induced liver injury (CBZ-DILI). In order to evaluate shared or phenotype-specific genetic predisposing factors for CBZ hypersensitivity reactions, we performed a meta-analysis of two genomewide association studies (GWAS) on a total of 43 well-phenotyped Northern and Southern European CBZ-SCAR cases and 10,701 population controls and a GWAS on 12 CBZ-DILI cases and 8,438 ethnically matched population controls. HLA-A*31:01 was identified as the strongest genetic predisposing factor for both CBZ-SCAR (odds ratio (OR) = 8.0; 95% CI 4.10-15.80; P = 1.2 x 10(-9)) and CBZ-DILI (OR = 7.3; 95% CI 2.47-23.67; P = 0.0004) in European populations. The association with HLA-A*31:01 in patients with SCAR was mainly driven by hypersensitivity syndrome (OR = 12.9; P = 2.1 x 10(-9)) rather than by Stevens-Johnson syndrome/toxic epidermal necrolysis cases, which showed an association with HLA-B*57:01. We also identified a novel risk locus mapping to ALK only for CBZ-SCAR cases, which needs replication in additional cohorts and functional evaluation.

Essential tremor (ET) is one of the most common causes of tremor in humans. Despite its high heritability and prevalence, few susceptibility genes for ET have been identified. To identify ET genes, whole-exome sequencing was performed in 37 early-onset ET families with an autosomal-dominant inheritance pattern. We identified candidate genes for follow-up functional studies in five ET families. In two independent families, we identified variants predicted to affect function in the nitric oxide (NO) synthase 3 gene (NOS3) that cosegregated with disease. NOS3 is highly expressed in the central nervous system (including cerebellum), neurons and endothelial cells, and is one of three enzymes that converts l-arginine to the neurotransmitter NO. In one family, a heterozygous variant, c.46G>A (p.(Gly16Ser)), in NOS3, was identified in three affected ET cases and was absent in an unaffected family member; and in a second family, a heterozygous variant, c.164C>T (p.(Pro55Leu)), was identified in three affected ET cases (dizygotic twins and their mother). Both variants result in amino-acid substitutions of highly conserved amino-acid residues that are predicted to be deleterious and damaging by in silico analysis. In three independent families, variants predicted to affect function were also identified in other genes, including KCNS2 (KV9.2), HAPLN4 (BRAL2) and USP46. These genes are highly expressed in the cerebellum and Purkinje cells, and influence function of the gamma-amino butyric acid (GABA)-ergic system. This is in concordance with recent evidence that the pathophysiological process in ET involves cerebellar dysfunction and possibly cerebellar degeneration with a reduction in Purkinje cells, and a decrease in GABA-ergic tone.

The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD2 Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD2 Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD2 Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD2 Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD2 Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications. Database URL: https://ctd2-dashboard.nci.nih.gov/

Learning Objectives After completing this course, the reader will be able to: Explain the association between bisphosphonates and osteonecrosis of the jaw.Describe the role of RBMS3 in the risk of BRONJ development. This article is available for continuing medical education credit at CME.TheOncologist.com Bisphosphonate-related osteonecrosis of the jaw (BRONJ) is a serious adverse drug reaction. We conducted a genomewide association study to search for genetic variants with a large effect size that increase the risk for BRONJ. Methods. We ascertained BRONJ cases according to the diagnostic criteria of the American Association of Oral and Maxillofacial Surgeons. We genotyped cases and a set of treatment-matched controls using Illumina Human Omni Express 12v1 chip (733,202 markers). To maximize the power of the study, we expanded the initial control set by including population and treatment-tolerant controls from publicly available sources. Imputation at the whole-genome level was performed to increase the number of single nucleotide polymorphisms (SNPs) investigated. Tests of association were carried out by logistic regression, adjusting for population structure. We also examined a list of candidate genes comprising genes potentially involved in the pathogenesis of BRONJ and genes related to drug absorption, distribution, metabolism, and excretion. Results. Based on principal component analysis, we initially analyzed 30 white cases and 17 treatment-tolerant controls. We subsequently expanded the control set to include 60 genetically matched controls per case. Association testing identified a significant marker in the RBMS3 gene, rs17024608 (p-value < 7 × 10−8); individuals positive for the SNP were 5.8× more likely to develop BRONJ (odds ratio, 5.8; 95% confidence interval, 3.1–11.1). Candidate gene analysis further identified SNPs in IGFBP7 and ABCC4 as potentially implicated in BRONJ risk. Conclusion. Our findings suggest that genetic susceptibility plays a role in the pathophysiology of BRONJ, with RBMS3 having a significant effect in the risk.

Producción Científica, Background & Aims Drug-induced liver injury (DILI), especially from antimicrobial agents, is an important cause of serious liver disease. Amoxicillin-clavulanate (AC) is a leading cause of idiosyncratic DILI, but little is understood about genetic susceptibility to this adverse reaction. Methods We performed a genome-wide association study using 822,927 single-nucleotide polymorphism (SNP) markers from 201 White European and US cases of AC-DILI and 532 population controls, matched for genetic background. Results AC-DILI was associated with many loci in the major histocompatibility complex. The strongest effect was with a human leukocyte antigen (HLA) class II SNP (rs9274407, P=4.8×10−14), which correlated with rs3135388, a tag SNP of HLA-DRB1*1501-DQB1*0602 that was previously associated with AC-DILI. Conditioned on rs3135388, rs9274407 is still significant (P=1.1×10−4). An independent association was observed in the class I region (rs2523822, P=1.8×10−10), related to HLA-A*0201. The most significant class I and II SNPs showed statistical interaction (P=0.0015). High-resolution HLA genotyping (177 cases and 219 controls) confirmed associations of HLA-A*0201 (P=2×10−6) and HLA-DQB1*0602 (P=5×10−10), and their interaction (P=0.005). Additional, population-dependent effects were observed in HLA alleles with nominal significance. In an analysis of auto-immunerelated genes, rs2476601 in the gene PTPN22 was associated (P=1.3×10−4). Conclusions Class I and II HLA genotypes affect susceptibility to AC-DILI, indicating the importance of the adaptive immune response in pathogenesis. The HLA genotypes identified will be useful in studies of the pathogenesis of AC-DILI, but have limited utility as predictive or diagnostic biomarkers because of the low positive-predictive values.

Drug-induced liver injury (DILI) is an important cause of serious liver disease. The antimicrobial agent flucloxacillin is a common cause of DILI, but the genetic basis for susceptibility remains unclear. We conducted a genome-wide association (GWA) study using 866,399 markers in 51 cases of flucloxacillin DILI and 282 controls matched for sex and ancestry. The GWA showed an association peak in the major histocompatibility complex (MHC) region with the strongest association (P = 8.7 x 10(-33)) seen for rs2395029[G], a marker in complete linkage disequilibrium (LD) with HLA-B*5701. Further MHC genotyping, which included 64 flucloxacillin-tolerant controls, confirmed the association with HLA-B*5701 (OR = 80.6, P = 9.0 x 10(-19)). The association was replicated in a second cohort of 23 cases. In HLA-B*5701 carrier cases, rs10937275 in ST6GAL1 on chromosome 3 also showed genome-wide significance (OR = 4.1, P = 1.4 x 10(-8)). These findings provide new insights into the mechanism of flucloxacillin DILI and have the potential to substantially improve diagnosis of this serious disease.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Given a set of N sequence, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. The quality of the alignment is usually measured by penalizing the mis-matches and gaps, and rewarding the matches with appropriate weight functions. However for larger values of N, additional constraints are required to give meaningful alignments. We identify a user-controlled parameter, an alignment number K (2 ≤ K ≤ N): this additional requirement constrains the alignment to have at least K sequences agree on a character, whenever possible, in the alignment. We identify a natural optimization problem for this approach called the K-MSA problem. We show that the problem is MAX SNP hard. We give a natural extension of this problem that incorporates “biological relevance” by using motifs (common patterns in the sequences) and give an approximation algorithm for this problem in terms of the motifs in the data. MUSCA is an implementation of this approach and our experimental results indicate that this approach is efficient, particularly on large numbers of long sequences, and gives good alignments when tested on biological data such as DNA and protein sequences.

MOTIVATION: The discovery of motifs in biological sequences is an important problem. RESULTS: This paper presents a new algorithm for the discovery of rigid patterns (motifs) in biological sequences. Our method is combinatorial in nature and able to produce all patterns that appear in at least a (user-defined) minimum number of sequences, yet it manages to be very efficient by avoiding the enumeration of the entire pattern space. Furthermore, the reported patterns are maximal: any reported pattern cannot be made more specific and still keep on appearing at the exact same positions within the input sequences. The effectiveness of the proposed approach is showcased on a number of test cases which aim to: (i) validate the approach through the discovery of previously reported patterns; (ii) demonstrate the capability to identify automatically highly selective patterns particular to the sequences under consideration. Finally, experimental analysis indicates that the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generated output.

Marked prolongation of the QT interval on the electrocardiogram associated with the polymorphic ventricular tachycardia Torsades de Pointes is a serious adverse event during treatment with antiarrhythmic drugs and other culprit medications, and is a common cause for drug relabeling and withdrawal. Although clinical risk factors have been identified, the syndrome remains unpredictable in an individual patient. Here we used genome-wide association analysis to search for common predisposing genetic variants. Cases of drug-induced Torsades de Pointes (diTdP), treatment tolerant controls, and general population controls were ascertained across multiple sites using common definitions, and genotyped on the Illumina 610k or 1M-Duo BeadChips. Principal Components Analysis was used to select 216 Northwestern European diTdP cases and 771 ancestry-matched controls, including treatment-tolerant and general population subjects. With these sample sizes, there is 80% power to detect a variant at genome-wide significance with minor allele frequency of 10% and conferring an odds ratio of ≥2.7. Tests of association were carried out for each single nucleotide polymorphism (SNP) by logistic regression adjusting for gender and population structure. No SNP reached genome wide-significance; the variant with the lowest P value was rs2276314, a non-synonymous coding variant in C18orf21 (p = 3×10(-7), odds ratio = 2, 95% confidence intervals: 1.5-2.6). The haplotype formed by rs2276314 and a second SNP, rs767531, was significantly more frequent in controls than cases (p = 3×10(-9)). Expanding the number of controls and a gene-based analysis did not yield significant associations. This study argues that common genomic variants do not contribute importantly to risk for drug-induced Torsades de Pointes across multiple drugs.

The Center for the Multiscale Analysis of Genetic Networks (MAGNet, http://magnet.c2b2.columbia.edu) was established in 2005, with the mission of providing the biomedical research community with Structural and Systems Biology algorithms and software tools for the dissection of molecular interactions and for the interaction-based elucidation of cellular phenotypes. Over the last 7 years, MAGNet investigators have developed many novel analysis methodologies, which have led to important biological discoveries, including understanding the role of the DNA shape in protein-DNA binding specificity and the discovery of genes causally related to the presentation of malignant phenotypes, including lymphoma, glioma, and melanoma. Software tools implementing these methodologies have been broadly adopted by the research community and are made freely available through geWorkbench, the Center's integrated analysis platform. Additionally, MAGNet has been instrumental in organizing and developing key conferences and meetings focused on the emerging field of systems biology and regulatory genomics, with special focus on cancer-related research.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but severe, potentially life threatening adverse drug reactions characterized by skin blistering. Previous studies have identified drug-specific and population-specific genetic risk factors with large effects. In this study, we report the first genome-wide association study (GWAS) of SJS/TEN induced by a variety of drugs. Our aim was to identify common genetic risk factors with large effects on SJS/TEN risk. We conducted a genome-wide analysis of 96 retrospective cases and 198 controls with a panel of over one million single-nucleotide polymorphisms (SNPs). We further improved power with about 4000 additional controls from publicly available datasets. No genome-wide significant associations with SNPs or copy number variants were observed, although several genomic regions were suggested that may have a role in predisposing to drug-induced SJS/TEN. Our GWAS did not find common, highly penetrant genetic risk factors responsible for SJS/TEN events in the cases selected.

Summary: geWorkbench (genomics Workbench) is an open source Java desktop application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomics domains (gene expression, sequence, protein structure and systems biology). More than 70 distinct plug-in modules are currently available implementing both classical analyses (several variants of clustering, classification, homology detection, etc.) as well as state of the art algorithms for the reverse engineering of regulatory networks and for protein structure prediction, among many others. geWorkbench leverages standards-based middleware technologies to provide seamless access to remote data, annotation and computational servers, thus, enabling researchers with limited local resources to benefit from available public infrastructure. Availability: The project site (http://www.geworkbench.org) includes links to self-extracting installers for most operating system (OS) platforms as well as instructions for building the application from scratch using the source code [which is freely available from the project's SVN (subversion) repository]. geWorkbench support is available through the end-user and developer forums of the caBIG® Molecular Analysis Tools Knowledge Center, https://cabig-kc.nci.nih.gov/Molecular/forums/ Contact: geworkbench@c2b2.columbia.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

caBIG® is a virtual network of organizations developing and adopting interoperable databases and analytical tools to facilitate translational cancer research (von Eschenbach and Buetow 2007). It is an open-source, open-access program, and all the tools and resources are freely available to the research community. The National Cancer Institute is developing resources to assist enterprise-wide adoption of the caBIG® tools. To this end, we have bundled mature software tools together to facilitate easy adoption and installation. The Life Sciences Distribution (LSD) is comprised of tools to support the continuum of translational research: caArray, for the management and annotation of microarray data; caTissue, to support the collection, annotation, and distribution of biospecimens; the Clinical Trials Object Data System, for the sharing of clinical trials information; the National Biomedical Imaging Archive, for annotation, storage, and sharing of in vivo images; cancer Genome Wide Association Studies, for publishing and mining data from GWAS studies; and geWorkbench, supporting the integrated analysis and annotation of expression and sequence data. All the LSD tools are connected to caGrid (Saltz et al. 2006), which makes it possible for the databases at multiple institutions to be interconnected to support data sharing and integration.

We describe a Biomedical INteroperable Database System (BIND), which is part of a larger research project, the Biomedical Equipment Assessment and Management (BEAM) system. BEAM is an AIM (Advanced Informatics in Medicine) awarded project and it is funded by the European Community. It integrates a number of heterogeneous database systems which store Biomedical information, over public communications networks. The databases are located in various European countries. Users access the information via a common user interface and are able to run transactions over the multidatabase system.

Idiosyncratic drug-induced liver injury (DILI) is a leading cause of morbidity and mortality due to medication use yet is poorly studied, in part, due to its low incidence in the general population. Identification of genetic variants associated with these uncommon and difficult to diagnose adverse events could provide clues to underlying mechanisms. Hypothesis: Common genetic variants exist that contribute to DILI susceptibility from multiple drugs. Methods: DNA obtained from 783 individuals of European ancestry who experienced DILI attributed to >200 individual drugs were genotypedwith the Illumina 1Mor 1Mduo beadchip. Source of DNA samples included the following DILI registries: DILIN (401), DILIGEN (242), EUDRAGENE (89), and Malaga (51). Genome Wide Association (GWA) was performed using population controls (n=3001). Potential associations were tested for replication in 190 independent cases from DILIN. Results: GWA revealed a strong association within the MHC region (p 108), which did not replicate in validation cohorts composed of sufficient cases due to the same drug or class. Conclusion: Our replicated association between hepatocellular DILI and STAT4 supports the emerging role of the immune system in DILI susceptibility and suggests that common genetic variation may contribute to DILI susceptibility from multiple drugs. However, the generally negative GWA results suggest that there may be a preponderance of drug-specific genetic risk factors and/or rare genetic variation underlying DILI susceptibility. Therefore, we have begun whole exome and whole genome sequencing of DILI cases caused by the most frequent drugs in our cohorts in search of rarer, high-penetrance drug-specific risk factors.

The cancer Biomedical Informatics Grid® (caBIG®) is a collaborative network designed to accelerate the translation of discoveries from research to clinical care. This extensible informatics platform integrates diverse data types and supports interoperable software tools in the areas of clinical sciences, biospecimen management, imaging, and basic research. The Life Sciences Distribution (LSD) product bundle brings together a range of tools that support translational research, all connected via grid network technology. Tools included in the bundle support management and annotation of microarray data (caArray), biospecimens (caTissue Suite), imaging data (NBIA), clinical data (CTODS) and genome-wide association studies (caGWAS). LSD also includes integrative tools that allow scientist to search data across different data repositories connected to the grid, and analyze and integrate these data across different data types. geWorkbench supports integrative analysis and visualization of expression data, sequences, pathways and protein structures. cancer Bench-to-Bedside (caB2B) allows end-users to search biospecimens, images and array data across the grid and makes it possible to execute queries such as: “Are there any gene expression microarray data available from patients with Stage III lung cancer and are there corresponding in vivo images available for the affected patients?” Such a query would potentially span information federated across several instances of caArray, caTissue, and NBIA around the world. caIntegrator2 allows researchers to set up study specific custom web portals that bring together heterogeneous clinical, microarray and in vivo imaging data. This tool provides a graphical user interface to allow a study author to “point” to data of interest in systems on the grid and to then bring that data (or pointers to it, in the case of images) into the data warehouse. Once this information is in the caIntegrator2 environment, end user scientists can then run advanced queries, perform correlative outcomes analysis using Kaplan-Meier survival plots, and access analysis and visualization tools on and off the grid. All tools in the LSD are open source and community members are encouraged to participate and contribute. More information on the LSD suite of products, including installation packages, user and installation guides, and links to exemplar installations can be found at https://cabig.nci.nih.gov/adopt/LSD/ Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 99.

The advancement of the computational biology field hinges on progress in three fundamental directions – the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources–data, software tools and web-services. The iTools design, implementation and resource meta - data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.