Automated system for gene annotation and metabolic pathway reconstruction using general sequence databases.
Despite the growing number of genomes published or currently being sequenced, there is a relative paucity of software for functional classification of newly discovered genes and their assignment to metabolic pathways. Available software for such analyses has a very steep learning curve and requires the installation, configuration, and maintenance of large amounts of complex infrastructure, including complementary software and databases. Many such tools are restricted to one or a few data sources and classification schemes. In this work, we report an automated system for gene annotation and metabolic pathway reconstruction (ASGARD), which was designed to be powerful and generalizable, yet simple for the biologist to install and run on centralized, commonly available computers. It avoids the requirement for complex resources such as relational databases and web servers, as well as the need for administrator access to the operating system. Our methodology contributes to a more rapid investigation of the potential biochemical capabilities of genes and genomes by the biological researcher, and is useful in biochemical as well as comparative and evolutionary studies of pathways and networks.
Alves
João M P
JM
Center for the Study of Biological Complexity, Virginia Commonwealth University, P.O. Box 842030, Richmond, VA, 23284, USA.
Buck
Gregory A
GA
D001483:Base Sequence / Q000235:genetics; D030541:Databases, Genetic / Q000145:classification*; D016678:Genome / Q000235:genetics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software