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cid is a computational tool developed in the Web environment to process cloned DNA fragments with the objective of masking the vector and adaptor regions, detecting the presence of microsatellites and designing the most appropriate primer pairs for the amplification of the identified repetitive sequences. This entire process is executed by the user in a simple and automated manner with the data input as a Zip file of chromatograms or a multiFASTA file. Thus, it is possible to analyse dozens of sequences at the same time, optimizing data processing and the search for the information of interest. cid is freely available on http://www.shrimp.ufscar.br/cid/index.php.

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Primarily, I acknowledge my advisor, Prof. Maria da Graça C. Pimentel, for the continuous support and guidance during the development of this dissertation. Thanks for helping me make this dissertation possible. I also thank CNPq for financing my research. Thanks to the evaluation committee for the important insights that contributed to improve the final version of this text. Thanks to research colleagues that have collaborated, directly or indirectly, to the discussions and experiments that lead to this research: Didier Vega-Oliveros, Lílian Oliveira e Diogo Pedrosa. I extend acknowledgements to the researchers from the SEN-5 group at CWI, who, during my internship with them, have contributed in important ways to my research: Pablo Cesar, Rodrigo Guimarães, Jack Jansen e Dick Bulterman. Finally, I thank my family for the support through this journey. First, my mother, Arlete, for standing up for me along my entire academic career and believing in my choices. My grandmother, Seide, whose early support in my education made it possible that I reached this milestone. In addition, Mario Liziér, for the understanding, support, and encouragement whenever I needed.

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This paper proposes an information retrieval process that employs a relevance feedback approach based on implicit evidences provided by contextual information and explicit evidences provided by the user behavior during interaction. This process takes advantage of semantic information processing enabled by the use of ontologies to build semantic indexes, to represent context and domain knowledge and to aid interactions mediated by mobile devices.

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