B. F. Francis Ouellette

Mutations in the ABC1 gene in familial HDL deficiency with defective cholesterol efflux

BACKGROUND A low concentration of HDL cholesterol is the most common lipoprotein abnormality in patients with premature atherosclerosis. We have shown that Tangier disease, a rare and severe form of HDL deficiency characterised by a biochemical defect in cellular cholesterol efflux, is caused by mutations in the ATP-binding-cassette (ABC1) gene. This gene codes for the cholesterol-efflux regulatory protein (CERP). We investigated the presence of mutations in this gene in patients with familial HDL deficiency. METHODS Three French-Canadian families and one Dutch family with familial HDL deficiency were studied. Fibroblasts from the proband of each family were defective in cellular cholesterol efflux. Genomic DNA of each proband was used for mutation detection with primers flanking each exon of the ABC1 gene, and for sequencing of the entire coding region of the gene. PCR and restriction-fragment length polymorphism assays specific to each mutation were used to investigate segregation of the mutation in each family, and to test for absence of the mutation in DNA from normal controls. FINDINGS A different mutation was detected in ABC1 in each family studied. Each mutation either created a stop codon predicted to result in truncation of CERP, or altered a conserved aminoacid residue. Each mutation segregated with low concentrations of HDL-cholesterol in the family, and was not observed in more than 500 control chromosomes tested. INTERPRETATION These data show that mutations in ABC1 are the major cause of familial HDL deficiency associated with defective cholesterol efflux, and that CERP has an essential role in the formation of HDL. Our findings highlight the potential of modulation of ABC1 as a new route for increasing HDL concentrations.

Atlas – a data warehouse for integrative bioinformatics

BackgroundWe present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.DescriptionThe Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.ConclusionThe Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Pathway and network analysis of cancer genomes

Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.

Computational approaches to identify functional genetic variants in cancer genomes

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

Pegasys: software for executing and integrating analyses of biological sequences

BackgroundWe present Pegasys – a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools.ResultsThe Pegasys system includes numerous tools for pair-wise and multiple sequence alignment, ab initio gene prediction, RNA gene detection, masking repetitive sequences in genomic DNA as well as filters for database formatting and processing raw output from various analysis tools. We introduce a novel data structure for creating workflows of sequence analyses and a unified data model to store its results. The software allows users to dynamically create analysis workflows at run-time by manipulating a graphical user interface. All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation. The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools, or GAME XML for import into the Apollo genome editor. The modularity of the design allows for new tools to be added to the system with little programmer overhead. The database application programming interface allows programmatic access to the data stored in the backend through SQL queries.ConclusionsThe Pegasys system enables biologists and bioinformaticians to create and manage sequence analysis workflows. The software is released under the Open Source GNU General Public License. All source code and documentation is available for download at http://bioinformatics.ubc.ca/pegasys/.

Towards BioDBcore: a community-defined information specification for biological databases

The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.

Improving gene recognition accuracy by combining predictions from two gene-finding programs

MOTIVATION Despite constant improvements in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with desired correctness. The current programs can identify up to 75% of exons correctly and less than 50% of predicted gene structures correspond to actual genes. New approaches to computational gene-finding are clearly needed. RESULTS In this paper we have explored the benefits of combining predictions from already existing gene prediction programs. We have introduced three novel methods for combining predictions from programs Genscan and HMMgene. The methods primarily aim to improve exon level accuracy of gene-finding by identifying more probable exon boundaries and by eliminating false positive exon predictions. This approach results in improved accuracy at both the nucleotide and exon level, especially the latter, where the average improvement on the newly assembled dataset is 7.9% compared to the best result obtained by Genscan and HMMgene. When tested on a long genomic multi-gene sequence, our method that maintains reading frame consistency improved nucleotide level specificity by 21.0% and exon level specificity by 32.5% compared to the best result obtained by either of the two programs individually. AVAILABILITY The scripts implementing our methods are available from http://www.cs.ubc.ca/labs/beta/genefinding/

The Bioinformatics Links Directory: a Compilation of Molecular Biology Web Servers

The Bioinformatics Links Directory is an online community resource that contains a directory of freely available tools, databases, and resources for bioinformatics and molecular biology research. The listing of the servers published in this and previous issues of Nucleic Acids Research together with other useful tools and websites represents a rich repository of resources that are openly provided to the research community using internet technologies. The 166 servers highlighted in the 2005 Web Server Issue are included in the more than 700 links to useful online resources that are currently contained within the descriptive biological categories of the Bioinformatics Links Directory. This curated listing of bioinformatics resources is available online at the Bioinformatics Links Directory web site, . A complete listing of the 2005 Nucleic Acids Research Web Server Issue servers is available online at the Nucleic Acids web site, , and on the Bioinformatics Links Directory web site, .

Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory

All of the life science research web servers published in this and previous issues of Nucleic Acids Research, together with other useful tools, databases and resources for bioinformatics and molecular biology research are freely accessible online through the Bioinformatics Links Directory, http://bioinformatics.ca/links_directory/. Entirely dependent on user feedback and community input, the Bioinformatics Links Directory exemplifies an open access research tool and resource. With 112 websites featured in the July 2009 Web Server Issue of Nucleic Acids Research, the 2009 update brings the total number of servers listed in the Bioinformatics Links Directory close to an impressive 1400 links. A complete list of all links listed in this Nucleic Acids Research 2009 Web Server Issue can be accessed online at http://bioinfomatics.ca/links_directory/narweb2009/. The 2009 update of the Bioinformatics Links Directory, which includes the Web Server list and summaries, is also available online at the Nucleic Acids Research website, http://nar.oxfordjournals.org/.

A compilation of molecular biology web servers: 2006 update on the Bioinformatics Links Directory

The Bioinformatics Links Directory is a public online resource that lists the servers published in this and all previously published Nucleic Acids Research Web Server issues together with other useful tools, databases and resources for bioinformatics and molecular biology research. This rich directory of tools and websites can be browsed and searched with all listed links freely accessible to the public. The 2006 update includes the 149 websites highlighted in the July 2006 issue of Nucleic Acids Research and brings the total number of servers listed in the Bioinformatics Links Directory to over 1000 links. To aid navigation through this growing resource, all link entries contain a brief synopsis, a citation list and are classified by function in descriptive biological categories. The most up-to-date version of this actively maintained listing of bioinformatics resources is available at the Bioinformatics Links Directory website, . A complete list of all links listed in this Nucleic Acids Research 2006 Web Server issue can be accessed online at . The 2006 update of the Bioinformatics Links Directory, which includes the Web Server list and summaries, is also available online at the Nucleic Acids Research website, .