Biomedical subjects
Publications and source records attributed to U Grob.
We improved an already existing formula for calculating the probability of occurrence of specific oligomers (Grob & Stüber, 1987) by taking into account unequal base distribution. This method identifies specific oligomers in a given sequence as candidates for biological signals.
A computer tool is described for comparison, analysis and search of genetic signals. The method is based on sequence consensus matrices. It assumes that a genetic signal (such as a promoter, enhancer or whatever) is composed of several signal blocks separated from each other by variable distances. A set of programs is presented to perform the analysis. The result of such an analysis is a description of the investigated signal including matrices for each signal block, distances between each block and distribution of the values. Programs are provided to search for a signal using results from previous analysis. The method is able to align large sets of sequences within a few minutes and to check the quality of the alignment. An analysis of E.coli promoters is provided as an example.
COOL is a program designed to find COmmon OLigomers in a number of nucleic acid sequences. The results of COOL serve as starting points in sequence analysis investigations as well as suggestions for polymerase chain reaction probes. As an example we analyzed thyroid hormone receptor genes and found two oligomers which are characteristic of almost all those genes.
We provide a menu-driven integration of the genetic programs of the Genetics Computer Group (GCG). This allows in-experienced users a very simple access to all GCG programs regardless of the system environment. No modifications to the GCG package are necessary.
In order to scan nucleic acid databases for potentially relevant but as yet unknown signals, we have developed an improved statistical model for pattern analysis of nucleic acid sequences by modifying previous methods based on Markov chains. We demonstrate the importance of selecting the appropriate parameters in order for the method to function at all. The model allows the simultaneous analysis of several short sequences with unequal base frequencies and Markov order k not equal to 0 as is usually the case in databases. As a test of these modifications, we show that in E. coli sequences there is a bias against palindromic hexamers which correspond to known restriction enzyme recognition sites.
A set of programs has been developed for the definition and handling of nucleic acid sequence consensus information. The sequences of known genetic control signals are combined in a matrix. The origins and positions of the signals are recorded. Old matrices can be updated dynamically: new signals are included and obsolete ones deleted. Matrices of several different types are computed optionally. Several of these matrices can be combined to find possible new signals. The use of matrices allows the exact quantification of signal qualities. The described programs are part of a program library named GENEXPERT. Application examples given are the search for tRNA genes and the search for promoters in the bacteriophage lambda genome.