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Functional Annotation of Hierarchical Modularity  [PDF]
Kanchana Padmanabhan, Kuangyu Wang, Nagiza F. Samatova
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0033744
Abstract: In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function–hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of “enriched” functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-anno?tation-of-hierarchical-modularity/ (Accessed 2012 March 13).
Functional annotation strategy for protein structures  [cached]
Olivia Doppelt,Fabrice Moriaud,Aurélie Bornot,Alexandre G. de Brevern
Bioinformation , 2007,
Abstract: Whole-genome sequencing projects are a major source of unknown function proteins. However, as predicting protein function from sequence remains a difficult task, research groups recently started to use 3D protein structures and structural models to bypass it. MED-SuMo compares protein surfaces analyzing the composition and spatial distribution of specific chemical groups (hydrogen bond donor, acceptor, positive, negative, aromatic, hydrophobic, guanidinium, hydroxyl, acyl and glycine). It is able to recognize proteins that have similar binding sites and thus, may perform similar functions. We present here a fine example which points out the interest of MED-SuMo approach for functional structural annotation.
AutoFACT: An Automatic Functional Annotation and Classification Tool
Liisa B Koski, Michael W Gray, B Franz Lang, Gertraud Burger
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-151
Abstract: We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%.AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at http://megasun.bch.umontreal.ca/Software/AutoFACT.htm webcite.Automatic functional annotation is essential for high-throughput sequencing projects. Typically, large datasets undergo annotation by means of "annotation jamborees", where groups of experts are assigned to manually annotate a designated portion of an organism's genome. More recently, various tools have become available to streamline this process [1-9]. However, limitations encountered with these tools are that many require web-submission of data [2], need substantial manual intervention [1,4], supply only a single output format, are part of a large sequence analysis package [3] and most importantly, do not combine a broad range of information resources. To address these shortcomings, we developed a new annotation pipeline, which we term "AutoFACT".Unique to AutoFACT, is its hierarchal filtering system for determining the most informative
Probabilistic annotation of protein sequences based on functional classifications
Emmanuel D Levy, Christos A Ouzounis, Walter R Gilks, Benjamin Audit
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-302
Abstract: Here, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases.The performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines.The gap between the growth rate of biological sequence databases and the capability to characterise experimentally the roles and functions associated with these new sequences is constantly increasing [1]. This results in an accumulation of raw data that can lead to an increase in our biological knowledge only if computational characterisation tools are developed. We focus here on the annotation of protein function. A generic approach to this problem consists of transferring the annotation from sequences of known function to uncharacterised proteins [2]. The transfer mechanism might be subdivided in two steps: (i) to establish the list of known proteins with significant sequence similarity to the uncharacterised sequence; (ii) to select the known sequence(s) from which the annotation is transferred [
Comparing functional annotation analyses with Catmap
Thomas Breslin, Patrik Edén, Morten Krogh
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-193
Abstract: We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap webcite), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes.In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.In genome-wide microarray experiments, it is possible to analyse the relevance of many different categories of genes, obtained from prior knowledge in the form of database annotations or from other experiments. These gene annotation analyses can unravel new information about pathways and cellular functions responsible for different phenotypes. Computational tools aiding in this process have recently been developed [1-8], most notably for annotations based on the Gene Ontology (GO) [9]. Generally, category relevance is calculated as the p-value of a score, thus being dependent on both the choice of score and the choice of null hypothesis.In microarray analyses such as clustering, which provide defined subsets of genes with no internal ranking, it is natural to base the score on the number of category genes in the relevant subset. However, ranking of genes appear in many techniques for micro
Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome
Teresia J Buza, Fiona M McCarthy, Shane C Burgess
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-425
Abstract: We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.After genome sequencing, genome annotation is critical to denote and demarcate the functional elements in the genome (structural annotation) and to link these genomic elements to biological function (functional annotation). Structural annotation of newly sequenced genomes begins during the final stages of genome assembly with electronic prediction of open reading frames (ORFs) [1-3]. Sequencing consortiums typically release these predicted genes and their translated products into public databases, where they account for the majority of data for the newly sequenced species [4,5] and are critical for high-throughput wet lab functional genomics (microarray and proteomics) experiments [4,6]. The NCBI N
Functional annotation of the human retinal pigment epithelium transcriptome
Judith C Booij, Simone van Soest, Sigrid MA Swagemakers, Anke HW Essing, Annemieke JMH Verkerk, Peter J van der Spek, Theo GMF Gorgels, Arthur AB Bergen
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-164
Abstract: In total, we identified 19,746 array entries with significant expression in the RPE. Gene expression was analyzed according to expression levels, interindividual variability and functionality. A group of highly (n = 2,194) expressed RPE genes showed an overrepresentation of genes of the oxidative phosphorylation, ATP synthesis and ribosome pathways. In the group of moderately expressed genes (n = 8,776) genes of the phosphatidylinositol signaling system and aminosugars metabolism were overrepresented. As expected, the top 10 percent (n = 2,194) of genes with the highest interindividual differences in expression showed functional overrepresentation of the complement cascade, essential in inflammation in age-related macular degeneration, and other signaling pathways. Surprisingly, this same category also includes the genes involved in Bruch's membrane (BM) composition. Among the top 10 percent of genes with low interindividual differences, there was an overrepresentation of genes involved in local glycosaminoglycan turnover.Our study expands current knowledge of the RPE transcriptome by assigning new genes, and adding data about expression level and interindividual variation. Functional annotation suggests that the RPE has high levels of protein synthesis, strong energy demands, and is exposed to high levels of oxidative stress and a variable degree of inflammation. Our data sheds new light on the molecular composition of BM, adjacent to the RPE, and is useful for candidate retinal disease gene identification or gene dose-dependent therapeutic studies.The retinal pigment epithelium (RPE) is a multifunctional neural-crest derived cell layer, flanked by the photoreceptor cells on the apical side and the Bruch's membrane (BM)/choroid complex on the basolateral side. Among others, the RPE supplies the photoreceptors with nutrients, regulates the ion balance in the subretinal space and recycles retinal from the photoreceptor cells, which is necessary for the continuation o
Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes  [PDF]
Kevin L. Childs,Rebecca M. Davidson,C. Robin Buell
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0022196
Abstract: With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation.
GeneTools – application for functional annotation and statistical hypothesis testing
Vidar Beisvag, Frode KR Jünge, Hallgeir Bergum, Lars J?lsum, Stian Lydersen, Clara-Cecilie Günther, Heri Ramampiaro, Mette Langaas, Arne K Sandvik, Astrid L?greid
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-470
Abstract: GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis.GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.noMicroarray technology allows researchers to monitor transcript levels of thousands of genes in a single experiment [1]. Typically it confronts the researcher with vast amounts of numerical data as a starting point from which to begin to investigate how molecular mechanisms are involved in a specific biological setting. Typically, scientists have to manually query several resources/databases for information. Although these can be h
Annotation Enrichment Analysis: An Alternative Method for Evaluating the Functional Properties of Gene Sets  [PDF]
Kimberly Glass,Michelle Girvan
Quantitative Biology , 2012,
Abstract: Gene annotation databases (compendiums maintained by the scientific community that describe the biological functions performed by individual genes) are commonly used to evaluate the functional properties of experimentally derived gene sets. Overlap statistics, such as Fisher's Exact Test (FET), are often employed to assess these associations, but don't account for non-uniformity in the number of genes annotated to individual functions or the number of functions associated with individual genes. We find FET is strongly biased toward over-estimating overlap significance if a gene set has an unusually high number of annotations. To correct for these biases, we develop Annotation Enrichment Analysis (AEA), which properly accounts for the non-uniformity of annotations. We show that AEA is able to identify biologically meaningful functional enrichments that are obscured by numerous false-positive enrichment scores in FET, and we therefore suggest it be used to more accurately assess the biological properties of gene sets.
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