Fast parsers for Entrez Gene

NCBI completed the transition of its main genome annotation database from Locuslink to Entrez Gene in Spring 2005. However, to this date few parsers exist for the Entrez Gene annotation file. Owing to the widespread use of Locuslink and the popularity of Perl programming language in bioinformatics, a publicly available high performance Entrez Gene parser in Perl is urgently needed. We present four such parsers that were developed using several parsing approaches (Parse::RecDescent, Parse::Yapp, Perl-byacc and Perl 5 regular expressions) and provide the first in-depth comparison of these sophisticated Perl tools. Our fastest parser processes the entire human Entrez Gene annotation file in under 12 min on one Intel Xeon 2.4 GHz CPU and can be of help to the bioinformatics community during and after the transition from Locuslink to Entrez Gene.     

Enabling high-throughput data management for systems biology: the Bioinformatics Resource Manager

The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users' data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability. BRM is developed using Java and other open-source technologies for free distribution. AVAILABILITY: BRM, sample data sets and a user manual can be downloaded from http://www.sysbio.org/dataresources/brm.stm.     

Anvaya: a workflows environment for automated genome analysis

Anvaya is a workflow environment for automated genome analysis that provides an interface for several bioinformatics tools and databases, loosely coupled together in a coordinated system, enabling the execution of a set of analyses tools in series or in parallel. It is a client-server workflow environment that has an advantage over existing software as it enables extensive pre & post processing of biological data in an efficient manner. "Anvaya" offers the user, novel functionalities to carry out exhaustive comparative analysis via "custom tools," which are tools with new functionality not available in standard tools, and "built-in PERL parsers," which automate data-flow between tools that hitherto, required manual intervention. It also provides a set of 11 pre-defined workflows for frequently used pipelines in genome annotation and comparative genomics ranging from EST assembly and annotation to phylogenetic reconstruction and microarray analysis. It provides a platform that serves as a single-stop solution for biologists to carry out hassle-free and comprehensive analysis, without being bothered about the nuances involved in tool installation, command line parameters, format conversions required to connect tools and manage/process multiple data sets at a single instance.     

Novel search method for the discovery of functional relationships

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.     

eL-DASionator: an LDAS upload file generator

Background  

The Distributed Annotation System (DAS) allows merging of DNA sequence annotations from multiple sources and provides a single annotation view. A straightforward way to establish a DAS annotation server is to use the "Lightweight DAS" server (LDAS). Onto this type of server, annotations can be uploaded as flat text files in a defined format. The popular Ensembl ContigView uses the same format for the transient upload and display of user data.     

QAUST: Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.     

KEGGanim: pathway animations for high-throughput data

MOTIVATION: Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data. Emerging -omics fields such as proteomics and interactomics introduce new information sources. With the rise of systems biology, researchers need to concentrate on entire complex pathways that guide individual genes and related processes. Bioinformatics methods are needed to link the existing knowledge about pathways with the growing amounts of experimental data. RESULTS: We present KEGGanim, a novel web-based tool for visualizing experimental data in biological pathways. KEGGanim produces animations and images of KEGG pathways using public or user uploaded high-throughput data. Pathway members are coloured according to experimental measurements, and animated over experimental conditions. KEGGanim visualization highlights dynamic changes over conditions and allows the user to observe important modules and key genes that influence the pathway. The simple user interface of KEGGanim provides options for filtering genes and experimental conditions. KEGGanim may be used with public or private data for 14 organisms with a large collection of public microarray data readily available. Most common gene and protein identifiers and microarray probesets are accepted for visualization input. AVAILABILITY: http://biit.cs.ut.ee/KEGGanim/.     

yrGATE: a web-based gene-structure annotation tool for the identification and dissemination of eukaryotic genes

Your Gene structure Annotation Tool for Eukaryotes (yrGATE) provides an Annotation Tool and Community Utilities for worldwide web-based community genome and gene annotation. Annotators can evaluate gene structure evidence derived from multiple sources to create gene structure annotations. Administrators regulate the acceptance of annotations into published gene sets. yrGATE is designed to facilitate rapid and accurate annotation of emerging genomes as well as to confirm, refine, or correct currently published annotations. yrGATE is highly portable and supports different standard input and output formats. The yrGATE software and usage cases are available at .     

KUTE-BASE: storing, downloading and exporting MIAME-compliant microarray experiments in minutes rather than hours

MOTIVATION: The BioArray Software Environment (BASE) is a very popular MIAME-compliant, web-based microarray data repository. However in BASE, like in most other microarray data repositories, the experiment annotation and raw data uploading can be very timeconsuming, especially for large microarray experiments. RESULTS: We developed KUTE (Karmanos Universal daTabase for microarray Experiments), as a plug-in for BASE 2.0 that addresses these issues. KUTE provides an automatic experiment annotation feature and a completely redesigned data work-flow that dramatically reduce the human-computer interaction time. For instance, in BASE 2.0 a typical Affymetrix experiment involving 100 arrays required 4 h 30 min of user interaction time forexperiment annotation, and 45 min for data upload/download. In contrast, for the same experiment, KUTE required only 28 min of user interaction time for experiment annotation, and 3.3 min for data upload/download. AVAILABILITY: http://vortex.cs.wayne.edu/kute/index.html.     

EMMA: a platform for consistent storage and efficient analysis of microarray data

As a high throughput technique, microarray experiments produce large data sets, consisting of measured data, laboratory protocols, and experimental settings. We have implemented the open source platform EMMA to store and analyze these data. The system provides automated pipelines for data processing and has a modular architecture that can be easily extended. EMMA features detailed reports about spots and their corresponding measurements. In addition to routine data analysis algorithms, the system can be integrated with other components that contain additional data sources (e.g. genome annotation systems).     

Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools

The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.     

The MGED Ontology: a resource for semantics-based description of microarray experiments

MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Ontology annotation: mapping genomic regions to biological function

With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.     

A fast structural multiple alignment method for long RNA sequences

Background

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.

Results

In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.

Conclusion

GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.     

Imagene: an integrated computer environment for sequence annotation and analysis

MOTIVATION: To be fully and efficiently exploited, data coming from sequencing projects together with specific sequence analysis tools need to be integrated within reliable data management systems. Systems designed to manage genome data and analysis tend to give a greater importance either to the data storage or to the methodological aspect, but lack a complete integration of both components. RESULTS: This paper presents a co-operative computer environment (called Imagenetrade mark) dedicated to genomic sequence analysis and annotation. Imagene has been developed by using an object-based model. Thanks to this representation, the user can directly manipulate familiar data objects through icons or lists. Imagene also incorporates a solving engine in order to manage analysis tasks. A global task is solved by successive divisions into smaller sub-tasks. During program execution, these sub- tasks are graphically displayed to the user and may be further re- started at any point after task completion. In this sense, Imagene is more transparent to the user than a traditional menu-driven package. Imagene also provides a user interface to display, on the same screen, the results produced by several tasks, together with the capability to annotate these results easily. In its current form, Imagene has been designed particularly for use in microbial sequencing projects. AVAILABILITY: Imagene best runs on SGI (Irix 6.3 or higher) workstations. It is distributed free of charge on a CD-ROM, but requires some Ilog licensed software to run. Some modules also require separate license agreements. Please contact the authors for specific academic conditions and other Unix platforms. CONTACT: imagene home page: http://wwwabi.snv.jussieu.fr/imagene      

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

Background  

Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.     

MADGE: scalable distributed data management software for cDNA microarrays

MOTIVATION: The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. RESULTS: We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. AVAILABILITY: This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.