Literature DB >> 9521927

GAIA: framework annotation of genomic sequence.

L C Bailey1, S Fischer, J Schug, J Crabtree, M Gibson, G C Overton.   

Abstract

As increasing amounts of genomic sequence from many organisms become available, and as DNA sequences become a primary reagent in biologic investigations, the role of annotation as a prospective guide for laboratory experiments will expand rapidly. Here we describe a process of high-throughput, reliable annotation, called framework annotation, which is designed to provide a foundation for initial biologic characterization of previously unexamined sequence. To examine this concept in practice, we have constructed Genome Annotation and Information Analysis (GAIA), a prototype software architecture that implements several elements important for framework annotation. The center of GAIA consists of an annotation database and the associated data management subsystem that forms the software bus along which other components communicate. The schema for this database defines three principal concepts: (1) Entries, consisting of sequence and associated historical data; (2) Features, comprising information of biologic interest; and (3) Experiments, describing the evidence that supports Features. The database permits tracking of annotation results over time, as well as assessment of the reliability of particular results. New framework annotation is produced by CARTA, a set of autonomous sensors that perform automatic analyses and assert results into the annotation database. These results are available via a Web-based query interface that uses graphical Java applets as well as text-based HTML pages to display data at different levels of resolution and permit interactive exploration of annotation. We present results for initial application of framework annotation to a set of test sequences, demonstrating its effectiveness in providing a starting point for biologic investigation, and discuss ways in which the current prototype can be improved. The prototype is available for public use and comment at http://www.cbil.upenn.edu/gaia.

Mesh:

Year:  1998        PMID: 9521927     DOI: 10.1101/gr.8.3.234

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  8 in total

1.  A complexity reduction algorithm for analysis and annotation of large genomic sequences.

Authors:  Trees-Juen Chuang; Wen-Chang Lin; Hurng-Chun Lee; Chi-Wei Wang; Keh-Lin Hsiao; Zi-Hao Wang; Danny Shieh; Simon C Lin; Lan-Yang Ch'ang
Journal:  Genome Res       Date:  2003-02       Impact factor: 9.043

2.  DNannotator: Annotation software tool kit for regional genomic sequences.

Authors:  Chunyu Liu; Tom I Bonner; Tu Nguyen; Jennifer L Lyons; Susan L Christian; Elliot S Gershon
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

3.  The phytophthora genome initiative database: informatics and analysis for distributed pathogenomic research.

Authors:  M Waugh; P Hraber; J Weller; Y Wu; G Chen; J Inman; D Kiphart; B Sobral
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

4.  AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system.

Authors:  K Bryson; V Loux; R Bossy; P Nicolas; S Chaillou; M van de Guchte; S Penaud; E Maguin; M Hoebeke; P Bessières; J-F Gibrat
Journal:  Nucleic Acids Res       Date:  2006-07-19       Impact factor: 16.971

5.  Re-annotation of genome microbial coding-sequences: finding new genes and inaccurately annotated genes.

Authors:  Stéphanie Bocs; Antoine Danchin; Claudine Médigue
Journal:  BMC Bioinformatics       Date:  2002-02-05       Impact factor: 3.169

6.  GOPET: a tool for automated predictions of Gene Ontology terms.

Authors:  Arunachalam Vinayagam; Coral del Val; Falk Schubert; Roland Eils; Karl-Heinz Glatting; Sándor Suhai; Rainer König
Journal:  BMC Bioinformatics       Date:  2006-03-20       Impact factor: 3.169

7.  Applying Support Vector Machines for Gene Ontology based gene function prediction.

Authors:  Arunachalam Vinayagam; Rainer König; Jutta Moormann; Falk Schubert; Roland Eils; Karl-Heinz Glatting; Sándor Suhai
Journal:  BMC Bioinformatics       Date:  2004-08-26       Impact factor: 3.169

8.  An optimized approach for annotation of large eukaryotic genomic sequences using genetic algorithm.

Authors:  Biswanath Chowdhury; Arnav Garai; Gautam Garai
Journal:  BMC Bioinformatics       Date:  2017-10-24       Impact factor: 3.169

  8 in total

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