Development of FuGO: An ontology for functional genomics investigations

The development of the Functional Genomics Investigation Ontology (FuGO) is a collaborative, international effort that will provide a resource for annotating functional genomics investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. FuGO will contain both terms that are universal to all functional genomics investigations and those that are domain specific. In this way, the ontology will serve as the “semantic glue” to provide a common understanding of data from across these disparate data sources. In addition, FuGO will reference out to existing mature ontologies to avoid the need to duplicate these resources, and will do so in such a way as to enable their ease of use in annotation. This project is in the early stages of development; the paper will describe efforts to initiate the project, the scope and organization of the project, the work accomplished to date, and the challenges encountered, as well as future plans

A factor analysis model for functional genomics

BACKGROUND: Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories. RESULTS: We propose a factor analysis model (FAM) for functional genomics and give a two-step algorithm, using genome-wide expression data for yeast and a subset of Gene-Ontology Biological Process functional annotations. We show that the predictive performance of our method is comparable to the current best approach while our total computation time was faster by a factor of 4000. We discuss the unique challenges in performance evaluation of algorithms used for genome-wide functions genomics. Finally, we discuss extensions to our method that can incorporate the inherent correlation structure of the functional categories to further improve predictive performance. CONCLUSION: Our factor analysis model is a computationally efficient technique for functional genomics and provides a clear and unified statistical framework with potential for incorporating important gene ontology information to improve predictions

The RCSB PDB information portal for structural genomics

The RCSB Protein Data Bank (PDB) offers online tools, summary reports and target information related to the worldwide structural genomics initiatives from its portal at . There are currently three components to this site: Structural Genomics Initiatives contains information and links on each structural genomics site, including progress reports, target lists, target status, targets in the PDB and level of sequence redundancy; Targets provides combined target information, protocols and other data associated with protein structure determination; and Structures offers an assessment of the progress of structural genomics based on the functional coverage of the human genome by PDB structures, structural genomics targets and homology models. Functional coverage can be examined according to enzyme classification, gene ontology (biological process, cell component and molecular function) and disease

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Yeast Features: Identifying Significant Features Shared Among Yeast Proteins for Functional Genomics

Background
High throughput yeast functional genomics experiments are revealing associations among tens to hundreds of genes using numerous experimental conditions. To fully understand how the identified genes might be involved in the observed system, it is essential to consider the widest range of biological annotation possible. Biologists often start their search by collating the annotation provided for each protein within databases such as the Saccharomyces Genome Database, manually comparing them for similar features, and empirically assessing their significance. Such tasks can be automated, and more precise calculations of the significance can be determined using established probability measures. 
Results
We developed Yeast Features, an intuitive online tool to help establish the significance of finding a diverse set of shared features among a collection of yeast proteins. A total of 18,786 features from the Saccharomyces Genome Database are considered, including annotation based on the Gene Ontology’s molecular function, biological process and cellular compartment, as well as conserved domains, protein-protein and genetic interactions, complexes, metabolic pathways, phenotypes and publications. The significance of shared features is estimated using a hypergeometric probability, but novel options exist to improve the significance by adding background knowledge of the experimental system. For instance, increased statistical significance is achieved in gene deletion experiments because interactions with essential genes will never be observed. We further demonstrate the utility by suggesting the functional roles of the indirect targets of an aminoglycoside with a known mechanism of action, and also the targets of an herbal extract with a previously unknown mode of action. The identification of shared functional features may also be used to propose novel roles for proteins of unknown function, including a role in protein synthesis for YKL075C.
Conclusions
Yeast Features (YF) is an easy to use web-based application (http://software.dumontierlab.com/yeastfeatures/) which can identify and prioritize features that are shared among a set of yeast proteins. This approach is shown to be valuable in the analysis of complex data sets, in which the extracted associations revealed significant functional relationships among the gene products.
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B2G-FAR, a species-centered GO annotation repository

Motivation: Functional genomics research has expanded enormously in the last decade thanks to the cost reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless, many biologists, especially working with non-model organisms, still suffer from non-existing or low-coverage functional annotation, or simply struggle retrieving, summarizing and querying these data

NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases

Enrichment analysis is a widely applied procedure for shedding light on the molecular mechanisms and functions at the basis of phenotypes, for enlarging the dataset of possibly related genes/proteins and for helping interpretation and prioritization of newly determined variations. Several standard and Network-based enrichment methods are available. Both approaches rely on the annotations that characterize the genes/proteins included in the input set; network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Sex differences in DNA methylation assessed by 450 K BeadChip in newborns.

BackgroundDNA methylation is an important epigenetic mark that can potentially link early life exposures to adverse health outcomes later in life. Host factors like sex and age strongly influence biological variation of DNA methylation, but characterization of these relationships is still limited, particularly in young children.MethodsIn a sample of 111 Mexican-American subjects (58 girls , 53 boys), we interrogated DNA methylation differences by sex at birth using the 450 K BeadChip in umbilical cord blood specimens, adjusting for cell composition.ResultsWe observed that ~3% of CpG sites were differentially methylated between girls and boys at birth (FDR P < 0.05). Of those CpGs, 3031 were located on autosomes, and 82.8% of those were hypermethylated in girls compared to boys. Beyond individual CpGs, we found 3604 sex-associated differentially methylated regions (DMRs) where the majority (75.8%) had higher methylation in girls. Using pathway analysis, we found that sex-associated autosomal CpGs were significantly enriched for gene ontology terms related to nervous system development and behavior. Among hits in our study, 35.9% had been previously reported as sex-associated CpG sites in other published human studies. Further, for replicated hits, the direction of the association with methylation was highly concordant (98.5-100%) with previous studies.ConclusionsTo our knowledge, this is the first reported epigenome-wide analysis by sex at birth that examined DMRs and adjusted for confounding by cell composition. We confirmed previously reported trends that methylation profiles are sex-specific even in autosomal genes, and also identified novel sex-associated CpGs in our methylome-wide analysis immediately after birth, a critical yet relatively unstudied developmental window