The use of evolutionary biology concepts for genome annotation

The past decade has seen the completion of numerous whole-genome sequencing projects, began with bacterial genomes and continued with eukaryotic species from different phyla: fungi, plants and animals. Besides, more biological information are produced and are shared thanks to information exchange systems, and more biological concepts, as well as more bioinformatics tools, are available. In this article, we will describe how the evolutionary biology concepts, as well as computer science, are useful for a better understanding of biology in general and genome annotation in particular. The genome annotation process consists of taking the raw DNA produced, for example, by the genome sequencing projects, adding the layers of analysis and interpretation necessary to extract its biological significance and placing it in the context of our understanding of biological processes. Genome annotation is a multistep process falling into two broad categories: structural and functional annotation.     

Systematic discovery of functional modules and context-specific functional annotation of human genome

MOTIVATION: The rapid accumulation of microarray datasets provides unique opportunities to perform systematic functional characterization of the human genome. We designed a graph-based approach to integrate cross-platform microarray data, and extract recurrent expression patterns. A series of microarray datasets can be modeled as a series of co-expression networks, in which we search for frequently occurring network patterns. The integrative approach provides three major advantages over the commonly used microarray analysis methods: (1) enhance signal to noise separation (2) identify functionally related genes without co-expression and (3) provide a way to predict gene functions in a context-specific way. RESULTS: We integrate 65 human microarray datasets, comprising 1105 experiments and over 11 million expression measurements. We develop a data mining procedure based on frequent itemset mining and biclustering to systematically discover network patterns that recur in at least five datasets. This resulted in 143,401 potential functional modules. Subsequently, we design a network topology statistic based on graph random walk that effectively captures characteristics of a gene's local functional environment. Function annotations based on this statistic are then subject to the assessment using the random forest method, combining six other attributes of the network modules. We assign 1126 functions to 895 genes, 779 known and 116 unknown, with a validation accuracy of 70%. Among our assignments, 20% genes are assigned with multiple functions based on different network environments. AVAILABILITY: http://zhoulab.usc.edu/ContextAnnotation.     

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.     

Social pathway annotation: extensions of the systems biology metabolic modelling assistant

High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. These data are usually represented in different formats (and sometimes in technical documents) on the Web. Inevitably, life science researchers have to deal with all these data and different formats to perform their daily research, but it is simply not possible for a single human mind to analyse all these data. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the 'omic' era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this article, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).     

Structural proteomics: a tool for genome annotation

In any newly sequenced genome, 30% to 50% of genes encode proteins with unknown molecular or cellular function. Fortunately, structural genomics is emerging as a powerful approach of functional annotation. Because of recent developments in high-throughput technologies, ongoing structural genomics projects are generating new structures at an unprecedented rate. In the past year, structural studies have identified many new structural motifs involved in enzymatic catalysis or in binding ligands or other macromolecules (DNA, RNA, protein). The efficiency by which function is deduced from structure can be further improved by the integration of structure with bioinformatics and other experimental approaches, such as screening for enzymatic activity or ligand binding.     

Genepi: a blackboard framework for genome annotation

Background  

Genome annotation can be viewed as an incremental, cooperative, data-driven, knowledge-based process that involves multiple methods to predict gene locations and structures. This process might have to be executed more than once and might be subjected to several revisions as the biological (new data) or methodological (new methods) knowledge evolves. In this context, although a lot of annotation platforms already exist, there is still a strong need for computer systems which take in charge, not only the primary annotation, but also the update and advance of the associated knowledge. In this paper, we propose to adopt a blackboard architecture for designing such a system     

Whole genome searching with shotgun proteomic data: applications for genome annotation

High-throughput genome sequencing continues to accelerate the rate at which complete genomes are available for biological research. Many of these new genome sequences have little or no genome annotation currently available and hence rely upon computational predictions of protein coding genes. Evidence of translation from proteomic techniques could facilitate experimental validation of protein coding genes, but the techniques for whole genome searching with MS/MS data have not been adequately developed to date. Here we describe GENQUEST, a novel method using peptide isoelectric focusing and accurate mass to greatly reduce the peptide search space, making fast, accurate, and sensitive whole human genome searching possible on common desktop computers. In an initial experiment, almost all exonic peptides identified in a protein database search were identified when searching genomic sequence. Many peptides identified exclusively in the genome searches were incorrectly identified or could not be experimentally validated, highlighting the importance of orthogonal validation. Experimentally validated peptides exclusive to the genomic searches can be used to reannotate protein coding genes. GENQUEST represents an experimental tool that can be used by the proteomics community at large for validating computational approaches to genome annotation.     

Standards requirements for systems biology approaches to health care: mitochondrial proteomics

A review of the standards needs of the mitochondrial proteomics communities is presented based on the presentations and discussions at National Institute of Standards and Technology (NIST) workshop, Systems Biology Approaches to Health Care: Mitochondrial Proteomics, held on September 17-18, 2002. The mitochondrial proteomics areas addressed for standards needs are model systems, methods and data. This review outlines the challenges in the field, proposes standards efforts that the community would like to see pursued to meet those challenges, and is followed by a summary and NIST's planned efforts to address these standards requirements.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Proteomics approaches to the systems biology of cardiovascular diseases

Proteins play a central role in a systems view of biologic processes. This review provides an overview of proteomics from a systems perspective. We survey the key tools and methodologies used, present examples of how these are currently being used in the systems biology context, and discuss future directions.     

Schistosoma comparative genomics: integrating genome structure, parasite biology and anthelmintic discovery

Schistosoma genomes provide a comprehensive resource for identifying the molecular processes that shape parasite evolution and for discovering novel chemotherapeutic or immunoprophylactic targets. Here, we demonstrate how intragenus and intergenus comparative genomics can be used to drive these investigations forward, illustrate the advantages and limitations of these approaches and review how post-genomic technologies offer complementary strategies for genome characterisation. Although sequencing and functional characterisation of other schistosome/platyhelminth genomes continues to expedite anthelmintic discovery, we contend that future priorities should equally focus on improving assembly quality, and chromosomal assignment, of existing schistosome/platyhelminth genomes.     

Computational methods for gene annotation: the Arabidopsis genome

Since the structure of the DNA molecule was identified half a century ago, the complete genome sequence has been determined for 37 prokaryotes and several eukaryotes. With the exponential growth of genetic information, bioinformatics has attempted to predict gene locations and functions in cyberspace prior to experimental confirmation at the bench.