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Biomedical subjects

K Quandt

Publications and source records attributed to K Quandt.

8 recordsLinked to original sources
PubMed1999

Functional promoter modules can be detected by formal models independent of overall nucleotide sequence similarity.

MOTIVATION: Gene regulation often depends on functional modules which feature a detectable internal organization. Overall sequence similarity of these modules is often insufficient for detection by general search methods like FASTA or even Gapped BLAST. However, it is of interest to evaluate whether modules, often known from experimental analysis of single sequences, are present in other regulatory sequences. RESULTS: We developed a new method (FastM) which combines a search algorithm for individual transcription factor binding sites (MatInspector) with a distance correlation function. FastM allows fast definition of a model of correlated binding sites derived from as little as a single promoter or enhancer. ModelInspector results are suitable for evaluation of the significance of the model. We used FastM to define a model for the experimentally verified NFkappaB/IRF1 regulatory module from the major histocompatibility complex (MHC) class I HLA-B gene promoter. Analysis of a test set of sequences as well as database searches with this model showed excellent correlation of the model with the biological function of the module. These results could not be obtained by searches using FASTA or Gapped BLAST, which are based on sequence similarity. We were also able to demonstrate association of a hypothetical GRE-GRE module with viral sequences based on analysis of several GenBank sections with this module. AVAILABILITY: The WWW version of FastM is accessible at: http://www.gsf.de/cgi-bin/fastm. pl and http://genomatix.gsf.de/cgi-bin/fastm2/fastm.pl

Algorithms↗
PubMed1997

Software for the analysis of DNA sequence elements of transcription.

The detection of transcription control elements in DNA sequences became both more important and more complicated by the completion of the first full genome sequencing projects. Rapid evaluation of potential regulatory elements in large amounts of sequence data requires specific methods preferably available as user-friendly computer programs. However, many more algorithms and methods have been published than programs are available, creating problems for scientists who try to select an appropriate method for their needs from the literature. The Internet provides a worldwide and relatively easy access to computer software if the user knows where to look. One of the major problems remaining is how to find the appropriate software. We have compiled a guide detailing where software is available and what is to be expected in terms of interface and data compatibility with other programs. We also show results obtained with each program for several examples. The summarized features of each program should allow scientists to select quickly the method of their choice and inform them where to download the software.

Algorithms↗
PubMed1996

GenomeInspector: basic software tools for analysis of spatial correlations between genomic structures within megabase sequences.

The speed of acquisition of genomic sequence data exceeds the evaluation of function of the sequences by a vast margin. Most software available for the prediction of individual features does not assess the correlation of different motifs (level 1 methods). Here, we present a second-level software package called GenomeInspector (GI) for further analysis of results obtained with level 1 methods. Our approach does not require any a priori knowledge about motif organization and was designed as a modular package with a graphical user interface. Three examples for GI application are presented.

Genome↗
PubMed1996

GenomeInspector: a new approach to detect correlation patterns of elements on genomic sequences.

MOTIVATION: Most of the sequences determined in current genome sequencing projects remain at least partially unannotated. The available software for DNA sequence analysis is usually limited to the prediction of individual elements (level 1 methods), but does not assess the context of different motifs. However, the functionality of biological units like promoters depends on the correct spatial organization of multiple individual elements. RESULTS: Here, we present a second-level software package called GenomeInspector [[http:@www.gsf.de/biodv/genomeinspector.html ]], for further analysis of results obtained with level 1 methods (e.g. MatInspector [[http:@www.gsf.de/biodv/matinspector.html ]] or ConsInspector [[http:@www.gsf.de/biodv/consinspector.html++ +]]). One of the main features of this modular program is its ability to assess distance correlations between large sets of sequence elements which can be used for the identification and definition of basic patterns of functional units. The program provides an easy-to-use graphical user interface with direct comprehensive display of all results for megabase sequences. Sequence elements showing spatial correlations can be easily extracted and traced back to the nucleotide sequence with the program. GenomeInspector identified promoters of glycolytic enzymes in yeast [[http:@www.mips.biochem.mpg.de/mips/yeast/]] as members of a subgroup with unusual location of an ABF1 site. Solely on the basis of distance correlation analysis, the program correctly selected those transcription factors within these promoters already known to be involved in the regulation of glycolytic enzymes, demonstrating the power of this method.

Algorithms↗
PubMed1995

MatInd and MatInspector: new fast and versatile tools for detection of consensus matches in nucleotide sequence data.

The identification of potential regulatory motifs in new sequence data is increasingly important for experimental design. Those motifs are commonly located by matches to IUPAC strings derived from consensus sequences. Although this method is simple and widely used, a major drawback of IUPAC strings is that they necessarily remove much of the information originally present in the set of sequences. Nucleotide distribution matrices retain most of the information and are thus better suited to evaluate new potential sites. However, sufficiently large libraries of pre-compiled matrices are a prerequisite for practical application of any matrix-based approach and are just beginning to emerge. Here we present a set of tools for molecular biologists that allows generation of new matrices and detection of potential sequence matches by automatic searches with a library of pre-compiled matrices. We also supply a large library (> 200) of transcription factor binding site matrices that has been compiled on the basis of published matrices as well as entries from the TRANSFAC database, with emphasis on sequences with experimentally verified binding capacity. Our search method includes position weighting of the matrices based on the information content of individual positions and calculates a relative matrix similarity. We show several examples suggesting that this matrix similarity is useful in estimating the functional potential of matrix matches and thus provides a valuable basis for designing appropriate experiments.

Base Sequence↗
PubMed1988

Simple permeation absorber for sampling and preconcentrating hazardous air contaminants.

A permeation absorber was developed and experimentally evaluated for sampling and preconcentrating vapors of a primary aromatic amine into a small volume (ca. 0.1 ml) of a liquid extractant that can be directly injected into a chromatograph or other analytical instrument. Starting with 1-l or 4-l samples containing dry or humidified air (0, 7% or 35% relative humidity) and 0.5-5 parts per million by volume of aniline, the measured collection efficiency (fraction of aniline recovered in the extractant) ranged between 60 and 100% when the samples were recirculated 3-6 times. For a single-pass non-recirculating mode, the collection efficiency is calculated to be 40-50%. The degree of preconcentration is directly proportional to the volume V of the sampled air. The collection method is simple and fast and should also be applicable to the sampling and preconcentration of other hazardous air contaminants.

Absorption↗