Soft computing is gradually opening up several possibilities in bioinformatics, especially by generating low-cost, low-precision (approximate), good solutions. In this paper, we survey the role of different soft computing paradigms, like fuzzy sets (FSs), artificial neural networks (ANNs), evolutionary computation, rough sets (RSes), and support vector machines (SVMs), in this direction. The major pattern-recognition and data-mining tasks considered here are clustering, classification, feature selection, and rule generation. Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described. Since the work entails processing huge amounts of incomplete or ambiguous biological data, we can utilize the learning ability of neural networks for adapting, uncertainty handling capacity of FSs and RSes for modeling ambiguity, searching potential of genetic algorithms for efficiently traversing large search spaces, and the generalization capability of SVMs for minimizing errors