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This paper describes a LISP program that analyzes sequences of letters and numbers. The program, which operates interactively, takes as input a sequence such as ACEGI...It produces as output the next element of the sequence, in this case K, together with a LISP program that predicts the n'th element of the sequence. The program is organized into an executive and a set of workers. The executive calls upon each worker in turn. The worker will respond if it recognizes the sequence as the kind that it knows about; since the workers may themselves call upon the executive, the recognition process is recursive. Workers exist for cyclic sequences, linear sequences, letter sequences, polynomials, interwining of sequences, sequences with predictable first differences, sequences with predictable first ratios, and standard sequences such as the primes, the squares, and the cubes. New workers can be added easily. It is concluded that the program can operate efficiently if somewhat slowly on a significantly large collection of sequences, and that the organizational scheme used has application to other kinds of pattern recognition.

Sets of related signals can be represented by separating their joint variation and showing the individual signal offsets with respect to this reference. An example is the Genomic Signal Analysis (GSA) of pathogen variability. The conversion of symbolic nucleotide sequences to genomic signals allows to use signal processing methods to analyse genomic data. This approach reveals striking regularities in the distribution of nucleotides and pair of nucleotides along the sequences, in both prokaryotes and eukaryotes. Genomic signals can also be used for sequence prediction, similarly to time series prediction. The methodology is also adequate for studying the development of pathogen multiple resistance to drugs.