Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maximization training procedure is relatively straight-forward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. Then, we experimentally analyze the effectiveness of model regularization, dynamic model modification, and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite. 1 Introduction Since their introduction to the computational biology community (Haussler et al., 1993; Krogh et al., 1994a), hidden Markov models (HMMs...

Kestrel is a programmable linear array processor designed for sequence analysis. Among other features, Kestrel includes an 8-bit word, a single-cycle add-and-minimize instruction, a multiplier and efficient communication using shared registers. This paper describes Kestrel’s functional units in detail, and examines each of their effects on system performance. With functional prototype chips completed, we will assemble a full single-board Kestrel array, with 512 processing elements on eight chips, in early 1998.

Sequence comparison, a vital research tool in computational biology, is based on a simple O(n 2 ) algorithm that easily maps to a linear array of processors. This paper reviews and compares high-performance sequence analysis on general-purpose supercomputers and single-purpose, reconfigurable, and programmable co-processors. The difficulty of comparing hardware from published performance figures is also noted. 1 Introduction The vast databases produced by the Human Genome Project demand innovative tools for fast sequence and database analysis. Because sequence databases contain billions of characters, it is important to locate areas of interest within a database or genome quickly. There are diverse sequence comparison methods, several of which are used by biologists to analyze RNA and DNA, which have 4-character nucleotide "alphabets", and proteins, which have a 20-character amino acid "alphabet" [7]. The simplest is edit distance, the number of insertions and deletions to transfo...

. We review a general, space and time efficient technique for delivering a sequence of values computable by a recurrence relation, in the order opposing the data-dependencies of the recurrence. The technique provides a series of time/space tradeoffs we characterize by parameter K ? 0. Namely, N values can be delivered against the grain in O(KN) time and O(KN 1=K ) space. This basic idea is not new, but here we present it in a framework exposing its essential nature and we give a concise yet easily understood explanation of it in terms of counting in a radix-N 1=K number system. We then show how to apply this paradigm to a couple of problems in sequence comparison, a domain where it has here to fore not been used. We show that in the limiting case of K = log 2 N , the method coincides with the well-known divideand -conquer algorithm of Hirschberg. Thus, our observation provides a continuum of time/space tradeoffs for all comparison problems that have appealed to the Hirschberg para...