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PubMed1999

Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Amino Acid Motifs↗

PubMed1998

A uniform framework for ordered restriction map problems.

Optical Mapping is an emerging technology for constructing ordered restriction maps of DNA molecules. The underlying computational problems for this technology have been studied and several models have been proposed in recent literature. Most of these propose combinatorial models; some of them also present statistical approaches. However, it is not a priori clear as to how these models relate to one another and to the underlying problem. We present a uniform framework for the restriction map problems where each of these various models is a specific instance of the basic framework. We achieve this by identifying two "signature" functions f() and g() that characterize the models. We identify the constraints these two functions must satisfy, thus opening up the possibility of exploring other plausible models. We show that for all of the combinatorial models proposed in literature, the signature functions are semi-algebraic. We also analyze a proposed statistical method in this framework and show that the signature functions are transcendental for this model. We also believe that this framework would provide useful guidelines for dealing with other inferencing problems arising in practice. Finally, we indicate the open problems by including a survey of the best known results for these problems.

Algorithms↗