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The field of genomics has been expanding at a rapid pace since the annotated Escherichia coli K-12 genome was published in 1997.This has witnessed exponential growth of sequence information and related biological databases.Despite decades of intense research on the E.coli genome with the attributions through the biochemical experimentations, complete and accurate functional information of this model organism is still not available. Genome annotation projects can produce incorrect results if they are based on obsolete data or inappropriate models. Such outdated and poor annotation can lead to the significant gaps in our knowledge . The wealth of biological information on E.coli is increasing swiftly and is contributing to a better understanding of this organism as well as functions encoded in other organisms. It is therefore important that the most up-to-date and accurate information on E.coli functions are made available for the use of scientific community. Functional re-annotation, a process of annotating a previously annotated genomic function, would support in providing deeper insight into the genome. This process generally involves a variety of computational techniques for functional prediction. Such functional assignments could also be achieved using more advanced high throughput technologies are employed; but at present this is only a day dream as such high class premium techniques are highly laborious and expensive .Hence in-silico functional re-analysis would assist in making quicker but more reliable functional predictions. The functional proteomics re-annotation can potentially provide some answers regarding higher levels of cellular processes, such as metabolism, transport, pathogenicity and regulation, thereby facilitating the elucidation of individual protein in a proteome.Hence, to overcome such challenges, the re-annotation of the E.coli K12 genome was carried out using a strategy known as “dynamic biological data fusion” in which the biological data from various available databases are integrated into a unique information source and there-on carryout statistical analyses to assign functions to the unknown proteins and update the functions of the pre-annotated proteins wherever possible and thereby facilitating more precise functional information to the research community.

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