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DAVID / Database for Annotation, Visualization and Integrated Discovery
Allows users to obtain biological features/meaning associated with large gene or protein lists. DAVID can determine gene-gene similarity, based on the assumption that genes sharing global functional annotation profiles are functionally related to each other. It groups related genes or terms into functional groups employing the similarity distances measure. This tool takes into account the redundant and network nature of biological annotation contents.
Blast2GO
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Permits functional annotation, management, and data mining of novel sequence data. Blast2GO is based on the utilization of common controlled vocabulary schemas, the gene ontology (GO). It takes in consideration similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. This tool is suitable for plant genomics research. It generates functional annotation and assesses the functional meaning of their experimental results.
GREAT
Predicts functions of cis-regulatory regions. Many coding genes are well annotated with their biological functions. Non-coding regions typically lack such annotation. GREAT assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes. Thus, it is particularly useful in studying cis functions of sets of non-coding genomic regions. Cis-regulatory regions can be identified via both experimental methods (e.g. ChIP-seq) and by computational methods (e.g. comparative genomics).
NaviGO
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Allows interactive visualization and retrieval of Gene Ontology (GO) terms and genes. NaviGO analyzes functional similarity and associations of GO terms and genes. It constructs similarity matrices based on the input GO terms and further continues to compute functional similarity among gene products/proteins based on the GO similarity matrices or it moves onto performing an enrichment analysis by calculating p-values for the overrepresented GO terms in the input.
AmiGO
Provides a set of tools for searching and browsing the Gene Ontology (GO) database. AMIGO visualizes speciation, duplication and horizontal gene transfer events, sequence alignments and descriptive data and external links for both proteins and annotations. The workflow annotation is a two-step process: (i) curators create a model of evolution that is consistent with the observed experimental annotations of modern-day sequences. Once constructed, this model is used in a (ii) step to create inferred annotations over the entire tree. This search box was designed to involve strict selection of terms, which results in coherent annotations within proteins families, as well as across families implicated in a single process.
TXTGate
Combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. TXTGate is a platform that offers multiple 'views' on vast amounts of genebased free-text information available in selected curated database entries and scientific publications. It enables detailed functional analysis of interesting gene groups by displaying key terms extracted from the associated literature and by offering options to link out to other resources or to sub-cluster the genes on the basis of text.
Interpro2GO
Classifies sequences into protein families and predicts the presence of important domains and sites. InterPro is an integrated resource of protein families, domains and sites which are combined from a number of different protein signature databases, including: Gene3D, Panther, PRSF, Pfam, PRINTS, ProSite, ProDom, SMART, SUPERFAMILY and TIGRFAMs. InterPro2GO creates annotations from data of InterPro. Gene Ontology terms assigned by InterPro2GO are cross-referenced more than 168 million times in UniProtKB, providing terms for almost 50 million individual proteins.
GOTreePlus
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An interactive gene ontology (GO) browser that superimposes annotation information over GO structures. GOTreePlus can facilitate the identification of important GO terms through interactive visualization of them in the GO structure. The interactive pie chart summarizing an annotation distribution for a selected GO term provides users with a succinct context-sensitive overview of their experimental results.
GenGO / GENerative GO analysis
Accommodates noise and errors in the selected gene set and Gene Ontology (GO). GenGO analyses the GO hierarchy for yeast and humans. This platform is effective in minimizing false positives while at the same time it can accurately balance the set of categories it returns, including both high level and specific categories. GenGO consistently outperforms both the original hypergeometric method and the methods considering only local structural dependencies, in some cases dramatically so.
GRYFUN
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Generates Gene Ontology annotation graphs for protein sets and their associated statistics from simple frequencies to enrichment values and information content based metrics. GRYFUN is a freely available web application that allows GO annotation visualization of protein sets and which can be used for annotation coherence and cohesiveness analysis and annotation extension assessments within under-annotated protein sets.
Annotare
A tool for annotating biomedical investigations and resulting data. It is a stand-alone desktop application that features 1) a set of intuitive editor forms to create and modify annotations, 2) support for easy incorporation of terms from biomedical ontologies, 3) standard templates for common experiment types, 4) a design wizard to help create a new document, and 5) a validator that checks for syntactic and semantic violation (see figure below). Annotare will help a bench biologist construct a MIAME-compliant annotation file based on the MAGE-TAB format.
GOLabeler
Predicts functions of no-knowledge proteins, particularly for those in the difficult type. GOLabeler is a method that integrates five component classifiers, trained from different features (such as GO term frequency, sequence alignment or amino acid trigram) in the framework of “learning to rank” (LTR). The software addresses three challenging issues of sequence-based large-scale automated function prediction (SAFP) for proteins: (1) structured ontology, (2) many labels per protein, and (3) large variation in the number of GO terms per protein.
IT3F / Interspecies Transcription Factor Function Finder
Allows users to visualize both evolutionary gene relationships and expression data for plant transcription factors. IT3F offers users to display information thanks to different graphics allowing users to compare structurally related genes. The graphs assist users in detection of orthologous genes. Its online interface includes an interrogative phylogenetic tree permitting the submission of new sequences relevant to a transcription factor family or subfamily.
FUNC
Identifies significant associations between gene sets and ontological annotations. FUNC allows researchers to correlate their data with gene annotations that are often provided in the form of ontologies. It integrates four kinds of tests suitable for the analysis of gene expression data and DNA sequence data of which two are not implemented in other gene ontology (GO) analysis programs. The tool provides flexible, statistically rigorous tools to analyse the functional annotation of a variety of genome-wide data.
AMEN / Annotation Mapping Expression and Network
Enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein -protein interaction (PPI), expression profiling and proteomics data. AMEN provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. AMEN facilitates the design and execution of optimized procedures for processing, analysis and interpretation of multifaceted high-throughput data.
CompGO
A bioinformatics tool for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. CompGO implements a statistic method normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from . BED data containing genomic coordinates as well as gene lists as inputs. It is applicable to any species where a reference genome assembly is available. As CompGO is implemented in R, it is accessible to a broad range of users and can readily be incorporated into existing pipelines. CompGO is an easy and fast comparative package for GO enrichments from experimentally identified DNA regions or genes.
BoWiki
Provides a platform for research communities to collaboratively integrate information and annotate data. BOWiki includes a wiki system that uses a core ontology together with an automated reasoner to maintain a consistent knowledge base. It is specifically targeted at small- to medium-sized communities. It allows users to characterize the entities specified by wikipages as instances of ontological categories, to define new relations within the wiki, to interrelate wikipages and to query for wikipages satisfying selected criteria.
HMTGO / Hidden Markov tree method for Gene Ontology
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Allows users to test multiple gene sets on a tree-transformed gene ontology (GO) directed acyclic graph (DAG). HMTGO is based on a hidden Markov tree model (HMTM). This method can be applied to a large-scale expression quantitative trait loci (eQTL) dataset. This tool is able to borrow information throughout the GO DAG structure and supplies an individual estimate of posterior probability of being differential expressed (DE) for each gene set/hypothesis.
BD-Func / BiDirectional FUNCtional enrichment
Calculates functional enrichment by comparing lists of pre-defined genes that are known to be activated versus inhibited in a pathway or by a regulatory molecule. BD-Func predicts cell line alternations and patient characteristics with accuracy comparable to popular algorithms. It also calculates a test statistic to represent functional activation or inhibition for each individual sample in a dataset. This statistic can be used as a classifier to quantify the predictive power of a given functional model.
NoisyGOA / Noisy GO Annotations
Removes noisy annotations. NoisyGOA firstly computes the taxonomic similarity between two ontological terms using the ontology structure, and the semantic similarity between two groups of terms annotated to pairwise genes. Next, it approximates aggregated taxonomic score for each available annotation of a gene with respect to annotations of the gene’s most semantic similar neighbors. Then, it predicts the annotations with the smallest scores as noisy annotations of the gene.
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