Optimization of selectivity in high-performance liquid chromatography using desirability functions and mixture designs according to PRISMA.

A computer program for the mobile phase optimization of high-performance liquid chromatography (HPLC) is described. The desirability function technique combined to the prisma mixture design was employed to enhance the quality of HPLC separations. The use of statistical models to predict the behaviour of retention times (tR) and band broadening at the different eluent compositions obtained by prisma was examined for dansyl amides and coumarins. The study showed that the dependence between the eluent composition and tR values of dansyl amides and coumarins can be expressed using quadratic regression models with a high degree of accuracy. Band broadening given by means of the band width at half-height (wh) was described by a linear regression model. Both models were used in calculating and predicting the resolution (Rs) in various solvent combinations. The desirability function converted the calculated (Rs) value into the desirability value (D), and the overall optimum was then defined by means of the overall desirability. The optimal eluent mixtures for the separation of compounds were easily read from the contour plot inside the horizontal plane of the prisma model. A good separation was achieved using the optimized solvent combination. Depending on the aims of the optimal separation, the program allows either optimization of critical pairs or achieving the overall optimum giving a reasonable separation for as many compounds as possible.

Systematic search for the best gene expression markers for melanoma micrometastasis detection.

Melanoma is notorious for its high tendency to metastasize and its refractoriness to treatment thereafter. Metastasis is believed to occur mostly through the lymphatic system, and the status of sentinel lymph nodes is currently recognized as the best prognostic indicator. Unfortunately, the lymphatic metastatic process is still poorly understood and the occurrence of sentinel node metastases (micrometastases) may be underestimated. We performed genome-wide gene expression analyses of melanoma lymph node micrometastases and macrometastases, and of primary melanomas and benign naevi, to characterize the early metastatic cells molecularly and to disclose the best diagnostic markers and rational targets for therapy. Significance analysis of microarrays identified 22 over- and five under-expressed genes with > or = four-fold changes in the micrometastases. Of these genes, MLANA, TYR, MIA, ERBB3, PRAME, and SPP1 were tested as potential markers by RT-PCR and immunohistochemistry. In a prospective study of 160 patients, our graded MLANA and TYR RT-PCR analyses disclosed clinically significant metastases, as assessed by disease recurrence, better than histological and immunohistochemical examinations. These results strongly suggest the clinical implementation of quantifiable RT-PCR assays to confirm and complement the pathological examination of sentinel node metastases. Furthermore, SPP1 and PRAME proved valuable as melanoma-specific markers capable of differentiating melanoma cells from benign naevi in the sentinel lymph nodes. Importantly, these two genes may also prove to be ideal targets for drug development and therapy. Most molecular traits of the micrometastases were already present in the primary tumours, suggesting that micrometastasis to sentinel lymph nodes is a fairly non-selective process.

Switch to an invasive growth phase in melanoma is associated with tenascin-C, fibronectin, and procollagen-I forming specific channel structures for invasion.

Malignant melanomas are characterized by their high propensity to invade and metastasize, but the molecular mechanisms of these traits have remained elusive. Our DNA microarray analyses of benign nevi and melanoma tissue specimens revealed that the genes encoding extracellular matrix proteins tenascin-C (TN-C), fibronectin (FN), and procollagen-I (PCOL-I) are highly upregulated in invasive and metastatic melanomas. The expression and distribution of these proteins were further studied by immunohistochemistry in benign nevi, radially and vertically growing melanomas, sentinel node micrometastases, and macrometastases. TN-C was increased in all invasive tumours and metastases, especially at invasion fronts, but not in benign nevi or non-invasive melanomas. Significantly, the intensity of TN-C staining correlated with metastasis to sentinel lymph nodes, better than tumour thickness (Breslow). Moreover, TN-C, FN, and PCOL-I appeared to co-localize in the tumours and form tubular meshworks and channels ensheathing the melanoma cells. Our data suggest that melanoma invasion is associated with the formation of special channel-like structures, providing a new concept, structured tumour cell spreading. Altogether, these data provide potential new prognostic markers and therapeutic targets/strategies for preventing melanoma dissemination.

SEQAID: a DNA sequence assembling program based on a mathematical model.

A program package, called SEQAID, to support DNA sequencing is presented. The program automatically assembles long DNA sequences from short fragments with minimal user interaction. Various tools for controlling the assembling process are also available. The main novel features of the system are that SEQAID implements several new well-behaved algorithms based on a mathematical model of the problem. It also utilizes available information on restriction fragments to detect illegitimate overlaps and to find relationships between separately assembled sequence blocks. Experiences with the system are reported including an extremely pathological real sequence which offers an interesting benchmark for this kind of programs.

Algorithms for the search of amino acid patterns in nucleic acid sequences.

Some algorithms are described for the search of regions in a nucleic acid sequence that, when translated into amino acids, are homologous to a given amino acid pattern. All algorithms are modifications of the dynamic programming method for sequence comparison such that the translation of codons is taken into account. One of the algorithms has been implemented as a FORTRAN 77 program. The program operates on files that follow the format of the EMBL Nucleotide Sequence Data Library.

Data mining for regulatory elements in yeast genome.

We have examined methods and developed a general software tool for finding and analyzing combinations of transcription factor binding sites that occur relatively often in gene upstream regions (putative promoter regions) in the yeast genome. Such frequently occurring combinations may be essential parts of possible promoter classes. The regions upstream to all genes were first isolated from the yeast genome database MIPS using the information in the annotation files of the database. The ones that do not overlap with coding regions were chosen for further studies. Next, all occurrences of the yeast transcription factor binding sites, as given in the IMD database, were located in the genome and in the selected regions in particular. Finally, by using a general purpose data mining software in combination with our own software, which parametrizes the search, we can find the combinations of binding sites that occur in the upstream regions more frequently than would be expected on the basis of the frequency of individual sites. The procedure also finds so-called association rules present in such combinations. The developed tool is available for use through the WWW.

Predicting gene regulatory elements in silico on a genomic scale.

We performed a systematic analysis of gene upstream regions in the yeast genome for occurrences of regular expression-type patterns with the goal of identifying potential regulatory elements. To achieve this goal, we have developed a new sequence pattern discovery algorithm that searches exhaustively for a priori unknown regular expression-type patterns that are over-represented in a given set of sequences. We applied the algorithm in two cases, (1) discovery of patterns in the complete set of >6000 sequences taken upstream of the putative yeast genes and (2) discovery of patterns in the regions upstream of the genes with similar expression profiles. In the first case, we looked for patterns that occur more frequently in the gene upstream regions than in the genome overall. In the second case, first we clustered the upstream regions of all the genes by similarity of their expression profiles on the basis of publicly available gene expression data and then looked for sequence patterns that are over-represented in each cluster. In both cases we considered each pattern that occurred at least in some minimum number of sequences, and rated them on the basis of their over-representation. Among the highest rating patterns, most have matches to substrings in known yeast transcription factor-binding sites. Moreover, several of them are known to be relevant to the expression of the genes from the respective clusters. Experiments on simulated data show that the majority of the discovered patterns are not expected to occur by chance.

Discovering patterns and subfamilies in biosequences.

We consider the problem of automatic discovery of patterns and the corresponding subfamilies in a set of biosequences. The sequences are unaligned and may contain noise of unknown level. The patterns are of the type used in PROSITE database. In our approach we discover patterns and the respective subfamilies simultaneously. We develop a theoretically substantiated significance measure for a set of such patterns and an algorithm approximating the best pattern set and the subfamilies. The approach is based on the minimum description length (MDL) principle. We report a computing experiment correctly finding subfamilies in the family of chromo domains and revealing new strong patterns.

Local average intensity-based method for identifying spherical particles in electron micrographs.

A method is presented that reliably detects spherical viruses from a wide variety of noisy low-contrast electron micrographs. Such detection is one of the first image analysis steps in the computer-aided reconstruction of three-dimensional density distribution models of viruses. Particle detection is based on the comparison of intensity in a circular area and in the surrounding ring followed by a number of tests to validate the potential particles. The only required input from the user in addition to the micrograph is an approximate radius of the particle. The method has been implemented as program ETHAN that has been tested for several different data sets. ETHAN has also successfully been used to detect DNA-less virus particles for an actual reconstruction.

Mining for putative regulatory elements in the yeast genome using gene expression data.

We have developed a set of methods and tools for automatic discovery of putative regulatory signals in genome sequences. The analysis pipeline consists of gene expression data clustering, sequence pattern discovery from upstream sequences of genes, a control experiment for pattern significance threshold limit detection, selection of interesting patterns, grouping of these patterns, representing the pattern groups in a concise form and evaluating the discovered putative signals against existing databases of regulatory signals. The pattern discovery is computationally the most expensive and crucial step. Our tool performs a rapid exhaustive search for a priori unknown statistically significant sequence patterns of unrestricted length. The statistical significance is determined for a set of sequences in each cluster with respect to a set of background sequences allowing the detection of subtle regulatory signals specific for each cluster. The potentially large number of significant patterns is reduced to a small number of groups by clustering them by mutual similarity. Automatically derived consensus patterns of these groups represent the results in a comprehensive way for a human investigator. We have performed a systematic analysis for the yeast Saccharomyces cerevisiae. We created a large number of independent clusterings of expression data simultaneously assessing the "goodness" of each cluster. For each of the over 52,000 clusters acquired in this way we discovered significant patterns in the upstream sequences of respective genes. We selected nearly 1,500 significant patterns by formal criteria and matched them against the experimentally mapped transcription factor binding sites in the SCPD database. We clustered the 1,500 patterns to 62 groups for which we derived automatically alignments and consensus patterns. Of these 62 groups 48 had patterns that have matching sites in SCPD database.

An MDL method for finding haplotype blocks and for estimating the strength of haplotype block boundaries.

We describe a new method for finding haplotype blocks based on the use of the minimum description length principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks, and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly et al. The results are in relatively good agreement with the published results, but also show clear differences in the predicted block boundaries and their strengths. We also give results on the block structure in population isolates.

Minimum description length block finder, a method to identify haplotype blocks and to compare the strength of block boundaries.

We describe a new probabilistic method for finding haplotype blocks that is based on the use of the minimum description length (MDL) principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly and colleagues. The results expose some problems that exist in the current methods for the evaluation of the significance of predicted block boundaries. Our method, MDL block finder, can be used to compare block borders in different sample sets, and we demonstrate this by applying the MDL-based method to define the block structure in chromosomes from population isolates.