We propose a novel, transparent and very simple algorithm to analyze middle-range correlations in genomic nucleotide sequences. Analysis by this algorithm of the EMBL Nucleotide Sequence Database demonstrates that all four nucleotides cluster in the genomic nucleotide sequences of eukaryotes on the scale of several hundred base pairs. In prokaryotes, the clustering is weak but still evident. The non-dominant three bases are deficient in the clusters, while A is the most deficient nucleotide in the clusters of C, and vice versa, and G is the most deficient nucleotide in the clusters of T, and vice versa. The algorithm also detects CG islands, extending over 1 kb, in vertebrate sequences. In plants, the CG islands are shown to be much smaller, if they exist at all. A clustering tendency is also exhibited by the TA doublet. Other doublets do not cluster. We observe no strong correlation between nucleotides separated in genomes by > 1 kb.

We present a program UNIREP, written in PowerBASIC for IBM-PCs, that identifies repetitive and unique nucleotide sequences in genomes or parts of genomes. A key feature of the algorithm is an oligonucleotide representation in a numerical code to make possible a comparison of all pairs of oligonucleotides (including overlaps) occurring in the analyzed sequence. This comparison assigns a score to each oligonucleotide, reflecting its similarity/dissimilarity to other oligonucleotides of the same length in the analyzed sequence. The score is plotted along the sequence so that peaks in the plot indicate repetitive regions and very low values reflect unique sequences. The scores are filtered to suppress or enhance the unique or repetitive sequences according to the user's wish. UNIREP is extended by auxiliary programs HIGHER and LOWER to list nucleotide sequences that have scores higher or lower than given limits. The potential of UNIREP is demonstrated using several long nucleotide sequences including the complete genomic sequence of EBV.

This paper describes the program GLOBIC, which compares, characterizes and fingerprints even 0.1 Mbase sequences in a few minutes with the aid of an IBM-AT microcomputer. Instead of the nucleotide sequences themselves, GLOBIC compares the local nucleotide or short oligonucleotide compositions. GLOBIC presents two-dimensional maps of contour lines depicting the similarity of two different sequences, a sequence compared to itself, to its complementary sequence or to a random sequence. A vocabulary is presented to translate the typical patterns appearing in the two-dimensional maps into their meanings as relationships between the compared sequences. The application of GLOBIC is demonstrated using several examples from the genomic nucleotide sequences of bacteriophage T7, adenovirus type-2 and Epstein-Barr virus.

This paper presents a simple program for interactive searching for nucleotide sequences that may code for the helix-turn-helix, zinc finger or leucine zipper motifs in proteins. The helix-turn-helix motifs are predicted using the recently published method of Dodd and Egan, while zinc fingers and leucine zippers are searched for by our original methods. DNABIND is shown to detect all four known helix-turn-helix motifs in bacteriophage lambda genes and both zinc fingers of the adr1 gene of yeast.

This article briefly describes our program Jamsek written in FORTRAN for an ICL 2950/10 computer. Jamsek combines statistical and stereochemical rules most frequently encountered in literature to predict protein secondary structure from its sequence, into a single algorithm. The composite algorithm does not work better than the best existing single algorithms of Garnier et al. (J. Mol. Biol., 120, 97-120, 1978) or Lim (J. Mol. Biol., 88, 873-894, 1974) if percentage of residues with a correctly predicted secondary structure is taken as a criterion. However, it is fairly reliable in predicting the total amount of alpha-helices and beta-sheets in proteins, the secondary structure of highly ordered proteins or their parts and identification of long alpha-helices. It surpasses the previous algorithms by providing a possibility to make a notion about confidence of the prediction of the particular secondary structure elements thanks to the simultaneous availability of four independent predictions of the secondary structure and other relevant data (hydrophobic profile and helical wheel representation). The main body of this article is devoted to a demonstration that output data of Jamsek can simply be used for the prediction of protein topological class, identification of globular proteins containing hydrophobic alpha-helices and, as an auxiliary means, to distinguish between protein coding and non-coding nucleotide sequences.